diff --git a/README.md b/README.md
index 1f3e812..42b5d1e 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,40 @@
-# scan-context
+# scan context
-scan context
\ No newline at end of file
+# Scan Context: Egocentric Spatial Descriptor for Place Recognition within 3D Point Cloud Map
+
+**2018 IROS Giseop Kim and Ayoung Kim**
+
+## Background
+
+- 回环检测（场景识别）=场景描述+搜索
+- 3D点云缺乏色彩信息，纹理信息等，无法提取出传统的图像所特有的特征（ORB，SIFT等）
+- 如果不对点云数据进行预处理的话，就只能进行几何匹配，消耗较高
+
+## challenge
+
+- 降维的形式，尽可能多的保留深度信息
+- 描述符的编码
+- 相似度打分
+
+## Framework
+
+
+
+## scan-context
+
+
+将点云分为环形的一块一块，每一块的数值就是这一块点云海拔最高值。这样就实现了降维。
+
+
+## Similarity Score between Scan Contexts
+
+由于雷达视角的不同，即当雷达在同一地点纯转动了一定角度之后，列向量向量值不变，但是会出现偏移；行向量的行为是向量中元素的顺序会发生改变，但是行向量不会发生偏移。采用列向比较。
+
+
+## Two-phase Search Algorithm
+
+- 利用ring key 构造KD—Tree后最近邻检索
+ 
+- 相似度评分
+
+- 找到闭环对应帧后使用ICP
\ No newline at end of file
diff --git a/src/README.md b/src/README.md
new file mode 100644
index 0000000..24fb3d4
--- /dev/null
+++ b/src/README.md
@@ -0,0 +1,92 @@
+
+
+# Scan Context
+
+## NEWS (Nov, 2020): integrated with LIO-SAM
+- A Scan Context integration for LIO-SAM, named [SC-LIO-SAM (link)](https://github.com/gisbi-kim/SC-LIO-SAM), is also released.
+
+## NEWS (Oct, 2020): Radar Scan Context
+- An evaluation code for radar place recognition (a.k.a. Radar Scan Context) is uploaded.
+- please see the *fast_evaluator_radar* directory.
+
+## NEWS (April, 2020): C++ implementation
+- C++ implementation released!
+ - See the directory `cpp/module/Scancontext`
+ - Features
+ - Light-weight: a single header and cpp file named "Scancontext.h" and "Scancontext.cpp"
+ - Our module has KDtree and we used nanoflann. nanoflann is an also single-header-program and that file is in our directory.
+ - Easy to use: A user just remembers and uses only two API functions; `makeAndSaveScancontextAndKeys` and `detectLoopClosureID`.
+ - Fast: tested the loop detector runs at 10-15Hz (for 20 x 60 size, 10 candidates)
+ - Example: Real-time LiDAR SLAM
+ - We integrated the C++ implementation within the recent popular LiDAR odometry code, LeGO-LOAM .
+ - That is, LiDAR SLAM = LiDAR Odometry (LeGO-LOAM) + Loop detection (Scan Context) and closure (GTSAM)
+ - For details, see `cpp/example/lidar_slam` or refer this repository (SC-LeGO-LOAM).
+---
+
+
+- Scan Context is a global descriptor for LiDAR point cloud, which is proposed in this paper and details are easily summarized in this video .
+
+```
+@INPROCEEDINGS { gkim-2018-iros,
+ author = {Kim, Giseop and Kim, Ayoung},
+ title = { Scan Context: Egocentric Spatial Descriptor for Place Recognition within {3D} Point Cloud Map },
+ booktitle = { Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems },
+ year = { 2018 },
+ month = { Oct. },
+ address = { Madrid }
+}
+```
+- This point cloud descriptor is used for place retrieval problem such as place
+recognition and long-term localization.
+
+
+## What is Scan Context?
+
+- Scan Context is a global descriptor for LiDAR point cloud, which is especially designed for a sparse and noisy point cloud acquired in outdoor environment.
+- It encodes egocentric visible information as below:
+
+
+- A user can vary the resolution of a Scan Context. Below is the example of Scan Contexts' various resolutions for the same point cloud.
+
+
+
+## How to use?: example cases
+- The structure of this repository is composed of 3 example use cases.
+- Most of the codes are written in Matlab.
+- A directory _matlab_ contains main functions including Scan Context generation and the distance function.
+- A directory _example_ contains a full example code for a few applications. We provide a total 3 examples.
+ 1. _**basics**_ contains a literally basic codes such as generation and can be a start point to understand Scan Context.
+
+ 2. _**place recognition**_ is an example directory for our IROS18 paper. The example is conducted using KITTI sequence 00 and PlaceRecognizer.m is the main code. You can easily grasp the full pipeline of Scan Context-based place recognition via watching and following the PlaceRecognizer.m code. Our Scan Context-based place recognition system consists of two steps; description and search. The search step is then composed of two hierarchical stages (1. ring key-based KD tree for fast candidate proposal, 2. candidate to query pairwise comparison-based nearest search). We note that our coarse yaw aligning-based pairwise distance enables reverse-revisit detection well, unlike others. The pipeline is below.
+
+
+ 3. _**long-term localization**_ is an example directory for our RAL19 paper. For the separation of mapping and localization, there are separated train and test steps. The main training and test codes are written in python and Keras, only excluding data generation and performance evaluation codes (they are written in Matlab), and those python codes are provided using jupyter notebook. We note that some path may not directly work for your environment but the evaluation codes (e.g., makeDataForPRcurveForSCIresult.m) will help you understand how this classification-based SCI-localization system works. The figure below depicts our long-term localization pipeline. 
More details of our long-term localization pipeline is found in the below paper and we also recommend you to watch this video .
+```
+@ARTICLE{ gkim-2019-ral,
+ author = {G. {Kim} and B. {Park} and A. {Kim}},
+ journal = {IEEE Robotics and Automation Letters},
+ title = {1-Day Learning, 1-Year Localization: Long-Term LiDAR Localization Using Scan Context Image},
+ year = {2019},
+ volume = {4},
+ number = {2},
+ pages = {1948-1955},
+ month = {April}
+}
+```
+
+ 4. _**SLAM**_ directory contains the practical use case of Scan Context for SLAM pipeline. The details are maintained in the related other repository _[PyICP SLAM](https://github.com/kissb2/PyICP-SLAM)_; the full-python LiDAR SLAM codes using Scan Context as a loop detector.
+
+## Acknowledgment
+This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport of Korea (19CTAP-C142170-02), and [High-Definition Map Based Precise Vehicle Localization Using Cameras and LIDARs] project funded by NAVER LABS Corporation.
+
+## Contact
+If you have any questions, contact here please
+ ```
+ paulgkim@kaist.ac.kr
+ ```
+
+## License
+ 
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
+
+### Copyright
+- All codes on this page are copyrighted by KAIST and Naver Labs and published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License. You must attribute the work in the manner specified by the author. You may not use the work for commercial purposes, and you may only distribute the resulting work under the same license if you alter, transform, or create the work.
diff --git a/src/cpp/example/lidar_slam/README.md b/src/cpp/example/lidar_slam/README.md
new file mode 100644
index 0000000..92c3002
--- /dev/null
+++ b/src/cpp/example/lidar_slam/README.md
@@ -0,0 +1,2 @@
+# Go to
+- https://github.com/irapkaist/SC-LeGO-LOAM
diff --git a/src/cpp/module/Scancontext/KDTreeVectorOfVectorsAdaptor.h b/src/cpp/module/Scancontext/KDTreeVectorOfVectorsAdaptor.h
new file mode 100644
index 0000000..0a56d6e
--- /dev/null
+++ b/src/cpp/module/Scancontext/KDTreeVectorOfVectorsAdaptor.h
@@ -0,0 +1,117 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2011-16 Jose Luis Blanco (joseluisblancoc@gmail.com).
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ * notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ * notice, this list of conditions and the following disclaimer in the
+ * documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#pragma once
+
+#include
+
+#include
+
+// ===== This example shows how to use nanoflann with these types of containers: =======
+//typedef std::vector > my_vector_of_vectors_t;
+//typedef std::vector my_vector_of_vectors_t; // This requires #include
+// =====================================================================================
+
+
+/** A simple vector-of-vectors adaptor for nanoflann, without duplicating the storage.
+ * The i'th vector represents a point in the state space.
+ *
+ * \tparam DIM If set to >0, it specifies a compile-time fixed dimensionality for the points in the data set, allowing more compiler optimizations.
+ * \tparam num_t The type of the point coordinates (typically, double or float).
+ * \tparam Distance The distance metric to use: nanoflann::metric_L1, nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
+ * \tparam IndexType The type for indices in the KD-tree index (typically, size_t of int)
+ */
+template
+struct KDTreeVectorOfVectorsAdaptor
+{
+ typedef KDTreeVectorOfVectorsAdaptor self_t;
+ typedef typename Distance::template traits::distance_t metric_t;
+ typedef nanoflann::KDTreeSingleIndexAdaptor< metric_t,self_t,DIM,IndexType> index_t;
+
+ index_t* index; //! The kd-tree index for the user to call its methods as usual with any other FLANN index.
+
+ /// Constructor: takes a const ref to the vector of vectors object with the data points
+ KDTreeVectorOfVectorsAdaptor(const size_t /* dimensionality */, const VectorOfVectorsType &mat, const int leaf_max_size = 10) : m_data(mat)
+ {
+ assert(mat.size() != 0 && mat[0].size() != 0);
+ const size_t dims = mat[0].size();
+ if (DIM>0 && static_cast(dims) != DIM)
+ throw std::runtime_error("Data set dimensionality does not match the 'DIM' template argument");
+ index = new index_t( static_cast(dims), *this /* adaptor */, nanoflann::KDTreeSingleIndexAdaptorParams(leaf_max_size ) );
+ index->buildIndex();
+ }
+
+ ~KDTreeVectorOfVectorsAdaptor() {
+ delete index;
+ }
+
+ const VectorOfVectorsType &m_data;
+
+ /** Query for the \a num_closest closest points to a given point (entered as query_point[0:dim-1]).
+ * Note that this is a short-cut method for index->findNeighbors().
+ * The user can also call index->... methods as desired.
+ * \note nChecks_IGNORED is ignored but kept for compatibility with the original FLANN interface.
+ */
+ inline void query(const num_t *query_point, const size_t num_closest, IndexType *out_indices, num_t *out_distances_sq, const int nChecks_IGNORED = 10) const
+ {
+ nanoflann::KNNResultSet resultSet(num_closest);
+ resultSet.init(out_indices, out_distances_sq);
+ index->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
+ }
+
+ /** @name Interface expected by KDTreeSingleIndexAdaptor
+ * @{ */
+
+ const self_t & derived() const {
+ return *this;
+ }
+ self_t & derived() {
+ return *this;
+ }
+
+ // Must return the number of data points
+ inline size_t kdtree_get_point_count() const {
+ return m_data.size();
+ }
+
+ // Returns the dim'th component of the idx'th point in the class:
+ inline num_t kdtree_get_pt(const size_t idx, const size_t dim) const {
+ return m_data[idx][dim];
+ }
+
+ // Optional bounding-box computation: return false to default to a standard bbox computation loop.
+ // Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
+ // Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
+ template
+ bool kdtree_get_bbox(BBOX & /*bb*/) const {
+ return false;
+ }
+
+ /** @} */
+
+}; // end of KDTreeVectorOfVectorsAdaptor
diff --git a/src/cpp/module/Scancontext/Scancontext.cpp b/src/cpp/module/Scancontext/Scancontext.cpp
new file mode 100644
index 0000000..5376c69
--- /dev/null
+++ b/src/cpp/module/Scancontext/Scancontext.cpp
@@ -0,0 +1,340 @@
+#include "Scancontext.h"
+
+// namespace SC2
+// {
+
+void coreImportTest (void)
+{
+ cout << "scancontext lib is successfully imported." << endl;
+} // coreImportTest
+
+
+float rad2deg(float radians)
+{
+ return radians * 180.0 / M_PI;
+}
+
+float deg2rad(float degrees)
+{
+ return degrees * M_PI / 180.0;
+}
+
+
+float xy2theta( const float & _x, const float & _y )
+{
+ if ( _x >= 0 & _y >= 0)
+ return (180/M_PI) * atan(_y / _x);
+
+ if ( _x < 0 & _y >= 0)
+ return 180 - ( (180/M_PI) * atan(_y / (-_x)) );
+
+ if ( _x < 0 & _y < 0)
+ return 180 + ( (180/M_PI) * atan(_y / _x) );
+
+ if ( _x >= 0 & _y < 0)
+ return 360 - ( (180/M_PI) * atan((-_y) / _x) );
+} // xy2theta
+
+
+MatrixXd circshift( MatrixXd &_mat, int _num_shift )
+{
+ // shift columns to right direction
+ assert(_num_shift >= 0);
+
+ if( _num_shift == 0 )
+ {
+ MatrixXd shifted_mat( _mat );
+ return shifted_mat; // Early return
+ }
+
+ MatrixXd shifted_mat = MatrixXd::Zero( _mat.rows(), _mat.cols() );
+ for ( int col_idx = 0; col_idx < _mat.cols(); col_idx++ )
+ {
+ int new_location = (col_idx + _num_shift) % _mat.cols();
+ shifted_mat.col(new_location) = _mat.col(col_idx);
+ }
+
+ return shifted_mat;
+
+} // circshift
+
+
+std::vector eig2stdvec( MatrixXd _eigmat )
+{
+ std::vector vec( _eigmat.data(), _eigmat.data() + _eigmat.size() );
+ return vec;
+} // eig2stdvec
+
+
+double SCManager::distDirectSC ( MatrixXd &_sc1, MatrixXd &_sc2 )
+{
+ int num_eff_cols = 0; // i.e., to exclude all-nonzero sector
+ double sum_sector_similarity = 0;
+ for ( int col_idx = 0; col_idx < _sc1.cols(); col_idx++ )
+ {
+ VectorXd col_sc1 = _sc1.col(col_idx);
+ VectorXd col_sc2 = _sc2.col(col_idx);
+
+ if( col_sc1.norm() == 0 | col_sc2.norm() == 0 )
+ continue; // don't count this sector pair.
+
+ double sector_similarity = col_sc1.dot(col_sc2) / (col_sc1.norm() * col_sc2.norm());
+
+ sum_sector_similarity = sum_sector_similarity + sector_similarity;
+ num_eff_cols = num_eff_cols + 1;
+ }
+
+ double sc_sim = sum_sector_similarity / num_eff_cols;
+ return 1.0 - sc_sim;
+
+} // distDirectSC
+
+
+int SCManager::fastAlignUsingVkey( MatrixXd & _vkey1, MatrixXd & _vkey2)
+{
+ int argmin_vkey_shift = 0;
+ double min_veky_diff_norm = 10000000;
+ for ( int shift_idx = 0; shift_idx < _vkey1.cols(); shift_idx++ )
+ {
+ MatrixXd vkey2_shifted = circshift(_vkey2, shift_idx);
+
+ MatrixXd vkey_diff = _vkey1 - vkey2_shifted;
+
+ double cur_diff_norm = vkey_diff.norm();
+ if( cur_diff_norm < min_veky_diff_norm )
+ {
+ argmin_vkey_shift = shift_idx;
+ min_veky_diff_norm = cur_diff_norm;
+ }
+ }
+
+ return argmin_vkey_shift;
+
+} // fastAlignUsingVkey
+
+
+std::pair SCManager::distanceBtnScanContext( MatrixXd &_sc1, MatrixXd &_sc2 )
+{
+ // 1. fast align using variant key (not in original IROS18)
+ MatrixXd vkey_sc1 = makeSectorkeyFromScancontext( _sc1 );
+ MatrixXd vkey_sc2 = makeSectorkeyFromScancontext( _sc2 );
+ int argmin_vkey_shift = fastAlignUsingVkey( vkey_sc1, vkey_sc2 );
+
+ const int SEARCH_RADIUS = round( 0.5 * SEARCH_RATIO * _sc1.cols() ); // a half of search range
+ std::vector shift_idx_search_space { argmin_vkey_shift };
+ for ( int ii = 1; ii < SEARCH_RADIUS + 1; ii++ )
+ {
+ shift_idx_search_space.push_back( (argmin_vkey_shift + ii + _sc1.cols()) % _sc1.cols() );
+ shift_idx_search_space.push_back( (argmin_vkey_shift - ii + _sc1.cols()) % _sc1.cols() );
+ }
+ std::sort(shift_idx_search_space.begin(), shift_idx_search_space.end());
+
+ // 2. fast columnwise diff
+ int argmin_shift = 0;
+ double min_sc_dist = 10000000;
+ for ( int num_shift: shift_idx_search_space )
+ {
+ MatrixXd sc2_shifted = circshift(_sc2, num_shift);
+ double cur_sc_dist = distDirectSC( _sc1, sc2_shifted );
+ if( cur_sc_dist < min_sc_dist )
+ {
+ argmin_shift = num_shift;
+ min_sc_dist = cur_sc_dist;
+ }
+ }
+
+ return make_pair(min_sc_dist, argmin_shift);
+
+} // distanceBtnScanContext
+
+
+MatrixXd SCManager::makeScancontext( pcl::PointCloud & _scan_down )
+{
+ TicToc t_making_desc;
+
+ int num_pts_scan_down = _scan_down.points.size();
+
+ // main
+ const int NO_POINT = -1000;
+ MatrixXd desc = NO_POINT * MatrixXd::Ones(PC_NUM_RING, PC_NUM_SECTOR);
+
+ SCPointType pt;
+ float azim_angle, azim_range; // wihtin 2d plane
+ int ring_idx, sctor_idx;
+ for (int pt_idx = 0; pt_idx < num_pts_scan_down; pt_idx++)
+ {
+ pt.x = _scan_down.points[pt_idx].x;
+ pt.y = _scan_down.points[pt_idx].y;
+ pt.z = _scan_down.points[pt_idx].z + LIDAR_HEIGHT; // naive adding is ok (all points should be > 0).
+
+ // xyz to ring, sector
+ azim_range = sqrt(pt.x * pt.x + pt.y * pt.y);
+ azim_angle = xy2theta(pt.x, pt.y);
+
+ // if range is out of roi, pass
+ if( azim_range > PC_MAX_RADIUS )
+ continue;
+
+ ring_idx = std::max( std::min( PC_NUM_RING, int(ceil( (azim_range / PC_MAX_RADIUS) * PC_NUM_RING )) ), 1 );
+ sctor_idx = std::max( std::min( PC_NUM_SECTOR, int(ceil( (azim_angle / 360.0) * PC_NUM_SECTOR )) ), 1 );
+
+ // taking maximum z
+ if ( desc(ring_idx-1, sctor_idx-1) < pt.z ) // -1 means cpp starts from 0
+ desc(ring_idx-1, sctor_idx-1) = pt.z; // update for taking maximum value at that bin
+ }
+
+ // reset no points to zero (for cosine dist later)
+ for ( int row_idx = 0; row_idx < desc.rows(); row_idx++ )
+ for ( int col_idx = 0; col_idx < desc.cols(); col_idx++ )
+ if( desc(row_idx, col_idx) == NO_POINT )
+ desc(row_idx, col_idx) = 0;
+
+ t_making_desc.toc("PolarContext making");
+
+ return desc;
+} // SCManager::makeScancontext
+
+
+MatrixXd SCManager::makeRingkeyFromScancontext( Eigen::MatrixXd &_desc )
+{
+ /*
+ * summary: rowwise mean vector
+ */
+ Eigen::MatrixXd invariant_key(_desc.rows(), 1);
+ for ( int row_idx = 0; row_idx < _desc.rows(); row_idx++ )
+ {
+ Eigen::MatrixXd curr_row = _desc.row(row_idx);
+ invariant_key(row_idx, 0) = curr_row.mean();
+ }
+
+ return invariant_key;
+} // SCManager::makeRingkeyFromScancontext
+
+
+MatrixXd SCManager::makeSectorkeyFromScancontext( Eigen::MatrixXd &_desc )
+{
+ /*
+ * summary: columnwise mean vector
+ */
+ Eigen::MatrixXd variant_key(1, _desc.cols());
+ for ( int col_idx = 0; col_idx < _desc.cols(); col_idx++ )
+ {
+ Eigen::MatrixXd curr_col = _desc.col(col_idx);
+ variant_key(0, col_idx) = curr_col.mean();
+ }
+
+ return variant_key;
+} // SCManager::makeSectorkeyFromScancontext
+
+
+void SCManager::makeAndSaveScancontextAndKeys( pcl::PointCloud & _scan_down )
+{
+ Eigen::MatrixXd sc = makeScancontext(_scan_down); // v1
+ Eigen::MatrixXd ringkey = makeRingkeyFromScancontext( sc );
+ Eigen::MatrixXd sectorkey = makeSectorkeyFromScancontext( sc );
+ std::vector polarcontext_invkey_vec = eig2stdvec( ringkey );
+
+ polarcontexts_.push_back( sc );
+ polarcontext_invkeys_.push_back( ringkey );
+ polarcontext_vkeys_.push_back( sectorkey );
+ polarcontext_invkeys_mat_.push_back( polarcontext_invkey_vec );
+
+ // cout < SCManager::detectLoopClosureID ( void )
+{
+ int loop_id { -1 }; // init with -1, -1 means no loop (== LeGO-LOAM's variable "closestHistoryFrameID")
+
+ auto curr_key = polarcontext_invkeys_mat_.back(); // current observation (query)
+ auto curr_desc = polarcontexts_.back(); // current observation (query)
+
+ /*
+ * step 1: candidates from ringkey tree_
+ */
+ if( polarcontext_invkeys_mat_.size() < NUM_EXCLUDE_RECENT + 1)
+ {
+ std::pair result {loop_id, 0.0};
+ return result; // Early return
+ }
+
+ // tree_ reconstruction (not mandatory to make everytime)
+ if( tree_making_period_conter % TREE_MAKING_PERIOD_ == 0) // to save computation cost
+ {
+ TicToc t_tree_construction;
+
+ polarcontext_invkeys_to_search_.clear();
+ polarcontext_invkeys_to_search_.assign( polarcontext_invkeys_mat_.begin(), polarcontext_invkeys_mat_.end() - NUM_EXCLUDE_RECENT ) ;
+
+ polarcontext_tree_.reset();
+ polarcontext_tree_ = std::make_unique(PC_NUM_RING /* dim */, polarcontext_invkeys_to_search_, 10 /* max leaf */ );
+ // tree_ptr_->index->buildIndex(); // inernally called in the constructor of InvKeyTree (for detail, refer the nanoflann and KDtreeVectorOfVectorsAdaptor)
+ t_tree_construction.toc("Tree construction");
+ }
+ tree_making_period_conter = tree_making_period_conter + 1;
+
+ double min_dist = 10000000; // init with somthing large
+ int nn_align = 0;
+ int nn_idx = 0;
+
+ // knn search
+ std::vector candidate_indexes( NUM_CANDIDATES_FROM_TREE );
+ std::vector out_dists_sqr( NUM_CANDIDATES_FROM_TREE );
+
+ TicToc t_tree_search;
+ nanoflann::KNNResultSet knnsearch_result( NUM_CANDIDATES_FROM_TREE );
+ knnsearch_result.init( &candidate_indexes[0], &out_dists_sqr[0] );
+ polarcontext_tree_->index->findNeighbors( knnsearch_result, &curr_key[0] /* query */, nanoflann::SearchParams(10) );
+ t_tree_search.toc("Tree search");
+
+ /*
+ * step 2: pairwise distance (find optimal columnwise best-fit using cosine distance)
+ */
+ TicToc t_calc_dist;
+ for ( int candidate_iter_idx = 0; candidate_iter_idx < NUM_CANDIDATES_FROM_TREE; candidate_iter_idx++ )
+ {
+ MatrixXd polarcontext_candidate = polarcontexts_[ candidate_indexes[candidate_iter_idx] ];
+ std::pair sc_dist_result = distanceBtnScanContext( curr_desc, polarcontext_candidate );
+
+ double candidate_dist = sc_dist_result.first;
+ int candidate_align = sc_dist_result.second;
+
+ if( candidate_dist < min_dist )
+ {
+ min_dist = candidate_dist;
+ nn_align = candidate_align;
+
+ nn_idx = candidate_indexes[candidate_iter_idx];
+ }
+ }
+ t_calc_dist.toc("Distance calc");
+
+ /*
+ * loop threshold check
+ */
+ if( min_dist < SC_DIST_THRES )
+ {
+ loop_id = nn_idx;
+
+ // std::cout.precision(3);
+ cout << "[Loop found] Nearest distance: " << min_dist << " btn " << polarcontexts_.size()-1 << " and " << nn_idx << "." << endl;
+ cout << "[Loop found] yaw diff: " << nn_align * PC_UNIT_SECTORANGLE << " deg." << endl;
+ }
+ else
+ {
+ std::cout.precision(3);
+ cout << "[Not loop] Nearest distance: " << min_dist << " btn " << polarcontexts_.size()-1 << " and " << nn_idx << "." << endl;
+ cout << "[Not loop] yaw diff: " << nn_align * PC_UNIT_SECTORANGLE << " deg." << endl;
+ }
+
+ // To do: return also nn_align (i.e., yaw diff)
+ float yaw_diff_rad = deg2rad(nn_align * PC_UNIT_SECTORANGLE);
+ std::pair result {loop_id, yaw_diff_rad};
+
+ return result;
+
+} // SCManager::detectLoopClosureID
+
+// } // namespace SC2
\ No newline at end of file
diff --git a/src/cpp/module/Scancontext/Scancontext.h b/src/cpp/module/Scancontext/Scancontext.h
new file mode 100644
index 0000000..ccccc28
--- /dev/null
+++ b/src/cpp/module/Scancontext/Scancontext.h
@@ -0,0 +1,110 @@
+#pragma once
+
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+
+#include
+
+#include
+#include
+#include
+#include
+
+#include
+#include
+#include
+#include
+
+#include "nanoflann.hpp"
+#include "KDTreeVectorOfVectorsAdaptor.h"
+
+#include "tictoc.h"
+
+using namespace Eigen;
+using namespace nanoflann;
+
+using std::cout;
+using std::endl;
+using std::make_pair;
+
+using std::atan2;
+using std::cos;
+using std::sin;
+
+using SCPointType = pcl::PointXYZI; // using xyz only. but a user can exchange the original bin encoding function (i.e., max hegiht) to max intensity (for detail, refer 20 ICRA Intensity Scan Context)
+using KeyMat = std::vector >;
+using InvKeyTree = KDTreeVectorOfVectorsAdaptor< KeyMat, float >;
+
+
+// namespace SC2
+// {
+
+void coreImportTest ( void );
+
+
+// sc param-independent helper functions
+float xy2theta( const float & _x, const float & _y );
+MatrixXd circshift( MatrixXd &_mat, int _num_shift );
+std::vector eig2stdvec( MatrixXd _eigmat );
+
+
+class SCManager
+{
+public:
+ SCManager( ) = default; // reserving data space (of std::vector) could be considered. but the descriptor is lightweight so don't care.
+
+ Eigen::MatrixXd makeScancontext( pcl::PointCloud & _scan_down );
+ Eigen::MatrixXd makeRingkeyFromScancontext( Eigen::MatrixXd &_desc );
+ Eigen::MatrixXd makeSectorkeyFromScancontext( Eigen::MatrixXd &_desc );
+
+ int fastAlignUsingVkey ( MatrixXd & _vkey1, MatrixXd & _vkey2 );
+ double distDirectSC ( MatrixXd &_sc1, MatrixXd &_sc2 ); // "d" (eq 5) in the original paper (IROS 18)
+ std::pair distanceBtnScanContext ( MatrixXd &_sc1, MatrixXd &_sc2 ); // "D" (eq 6) in the original paper (IROS 18)
+
+ // User-side API
+ void makeAndSaveScancontextAndKeys( pcl::PointCloud & _scan_down );
+ std::pair detectLoopClosureID( void ); // int: nearest node index, float: relative yaw
+
+public:
+ // hyper parameters ()
+ const double LIDAR_HEIGHT = 2.0; // lidar height : add this for simply directly using lidar scan in the lidar local coord (not robot base coord) / if you use robot-coord-transformed lidar scans, just set this as 0.
+
+ const int PC_NUM_RING = 20; // 20 in the original paper (IROS 18)
+ const int PC_NUM_SECTOR = 60; // 60 in the original paper (IROS 18)
+ const double PC_MAX_RADIUS = 80.0; // 80 meter max in the original paper (IROS 18)
+ const double PC_UNIT_SECTORANGLE = 360.0 / double(PC_NUM_SECTOR);
+ const double PC_UNIT_RINGGAP = PC_MAX_RADIUS / double(PC_NUM_RING);
+
+ // tree
+ const int NUM_EXCLUDE_RECENT = 50; // simply just keyframe gap, but node position distance-based exclusion is ok.
+ const int NUM_CANDIDATES_FROM_TREE = 10; // 10 is enough. (refer the IROS 18 paper)
+
+ // loop thres
+ const double SEARCH_RATIO = 0.1; // for fast comparison, no Brute-force, but search 10 % is okay. // not was in the original conf paper, but improved ver.
+ const double SC_DIST_THRES = 0.13; // empirically 0.1-0.2 is fine (rare false-alarms) for 20x60 polar context (but for 0.15 <, DCS or ICP fit score check (e.g., in LeGO-LOAM) should be required for robustness)
+ // const double SC_DIST_THRES = 0.5; // 0.4-0.6 is good choice for using with robust kernel (e.g., Cauchy, DCS) + icp fitness threshold / if not, recommend 0.1-0.15
+
+ // config
+ const int TREE_MAKING_PERIOD_ = 50; // i.e., remaking tree frequency, to avoid non-mandatory every remaking, to save time cost / if you want to find a very recent revisits use small value of it (it is enough fast ~ 5-50ms wrt N.).
+ int tree_making_period_conter = 0;
+
+ // data
+ std::vector polarcontexts_timestamp_; // optional.
+ std::vector polarcontexts_;
+ std::vector polarcontext_invkeys_;
+ std::vector polarcontext_vkeys_;
+
+ KeyMat polarcontext_invkeys_mat_;
+ KeyMat polarcontext_invkeys_to_search_;
+ std::unique_ptr polarcontext_tree_;
+
+}; // SCManager
+
+// } // namespace SC2
diff --git a/src/cpp/module/Scancontext/nanoflann.hpp b/src/cpp/module/Scancontext/nanoflann.hpp
new file mode 100644
index 0000000..a8e4667
--- /dev/null
+++ b/src/cpp/module/Scancontext/nanoflann.hpp
@@ -0,0 +1,2040 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ * Copyright 2011-2016 Jose Luis Blanco (joseluisblancoc@gmail.com).
+ * All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ * notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ * notice, this list of conditions and the following disclaimer in the
+ * documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+/** \mainpage nanoflann C++ API documentation
+ * nanoflann is a C++ header-only library for building KD-Trees, mostly
+ * optimized for 2D or 3D point clouds.
+ *
+ * nanoflann does not require compiling or installing, just an
+ * #include in your code.
+ *
+ * See:
+ * - C++ API organized by modules
+ * - Online README
+ * - Doxygen
+ * documentation
+ */
+
+#ifndef NANOFLANN_HPP_
+#define NANOFLANN_HPP_
+
+#include
+#include
+#include
+#include // for abs()
+#include // for fwrite()
+#include // for abs()
+#include
+#include // std::reference_wrapper
+#include
+#include
+
+/** Library version: 0xMmP (M=Major,m=minor,P=patch) */
+#define NANOFLANN_VERSION 0x132
+
+// Avoid conflicting declaration of min/max macros in windows headers
+#if !defined(NOMINMAX) && \
+ (defined(_WIN32) || defined(_WIN32_) || defined(WIN32) || defined(_WIN64))
+#define NOMINMAX
+#ifdef max
+#undef max
+#undef min
+#endif
+#endif
+
+namespace nanoflann {
+/** @addtogroup nanoflann_grp nanoflann C++ library for ANN
+ * @{ */
+
+/** the PI constant (required to avoid MSVC missing symbols) */
+template T pi_const() {
+ return static_cast(3.14159265358979323846);
+}
+
+/**
+ * Traits if object is resizable and assignable (typically has a resize | assign
+ * method)
+ */
+template struct has_resize : std::false_type {};
+
+template
+struct has_resize().resize(1), 0)>
+ : std::true_type {};
+
+template struct has_assign : std::false_type {};
+
+template
+struct has_assign().assign(1, 0), 0)>
+ : std::true_type {};
+
+/**
+ * Free function to resize a resizable object
+ */
+template
+inline typename std::enable_if::value, void>::type
+resize(Container &c, const size_t nElements) {
+ c.resize(nElements);
+}
+
+/**
+ * Free function that has no effects on non resizable containers (e.g.
+ * std::array) It raises an exception if the expected size does not match
+ */
+template
+inline typename std::enable_if::value, void>::type
+resize(Container &c, const size_t nElements) {
+ if (nElements != c.size())
+ throw std::logic_error("Try to change the size of a std::array.");
+}
+
+/**
+ * Free function to assign to a container
+ */
+template
+inline typename std::enable_if::value, void>::type
+assign(Container &c, const size_t nElements, const T &value) {
+ c.assign(nElements, value);
+}
+
+/**
+ * Free function to assign to a std::array
+ */
+template
+inline typename std::enable_if::value, void>::type
+assign(Container &c, const size_t nElements, const T &value) {
+ for (size_t i = 0; i < nElements; i++)
+ c[i] = value;
+}
+
+/** @addtogroup result_sets_grp Result set classes
+ * @{ */
+template
+class KNNResultSet {
+public:
+ typedef _DistanceType DistanceType;
+ typedef _IndexType IndexType;
+ typedef _CountType CountType;
+
+private:
+ IndexType *indices;
+ DistanceType *dists;
+ CountType capacity;
+ CountType count;
+
+public:
+ inline KNNResultSet(CountType capacity_)
+ : indices(0), dists(0), capacity(capacity_), count(0) {}
+
+ inline void init(IndexType *indices_, DistanceType *dists_) {
+ indices = indices_;
+ dists = dists_;
+ count = 0;
+ if (capacity)
+ dists[capacity - 1] = (std::numeric_limits::max)();
+ }
+
+ inline CountType size() const { return count; }
+
+ inline bool full() const { return count == capacity; }
+
+ /**
+ * Called during search to add an element matching the criteria.
+ * @return true if the search should be continued, false if the results are
+ * sufficient
+ */
+ inline bool addPoint(DistanceType dist, IndexType index) {
+ CountType i;
+ for (i = count; i > 0; --i) {
+#ifdef NANOFLANN_FIRST_MATCH // If defined and two points have the same
+ // distance, the one with the lowest-index will be
+ // returned first.
+ if ((dists[i - 1] > dist) ||
+ ((dist == dists[i - 1]) && (indices[i - 1] > index))) {
+#else
+ if (dists[i - 1] > dist) {
+#endif
+ if (i < capacity) {
+ dists[i] = dists[i - 1];
+ indices[i] = indices[i - 1];
+ }
+ } else
+ break;
+ }
+ if (i < capacity) {
+ dists[i] = dist;
+ indices[i] = index;
+ }
+ if (count < capacity)
+ count++;
+
+ // tell caller that the search shall continue
+ return true;
+ }
+
+ inline DistanceType worstDist() const { return dists[capacity - 1]; }
+};
+
+/** operator "<" for std::sort() */
+struct IndexDist_Sorter {
+ /** PairType will be typically: std::pair */
+ template
+ inline bool operator()(const PairType &p1, const PairType &p2) const {
+ return p1.second < p2.second;
+ }
+};
+
+/**
+ * A result-set class used when performing a radius based search.
+ */
+template
+class RadiusResultSet {
+public:
+ typedef _DistanceType DistanceType;
+ typedef _IndexType IndexType;
+
+public:
+ const DistanceType radius;
+
+ std::vector> &m_indices_dists;
+
+ inline RadiusResultSet(
+ DistanceType radius_,
+ std::vector> &indices_dists)
+ : radius(radius_), m_indices_dists(indices_dists) {
+ init();
+ }
+
+ inline void init() { clear(); }
+ inline void clear() { m_indices_dists.clear(); }
+
+ inline size_t size() const { return m_indices_dists.size(); }
+
+ inline bool full() const { return true; }
+
+ /**
+ * Called during search to add an element matching the criteria.
+ * @return true if the search should be continued, false if the results are
+ * sufficient
+ */
+ inline bool addPoint(DistanceType dist, IndexType index) {
+ if (dist < radius)
+ m_indices_dists.push_back(std::make_pair(index, dist));
+ return true;
+ }
+
+ inline DistanceType worstDist() const { return radius; }
+
+ /**
+ * Find the worst result (furtherest neighbor) without copying or sorting
+ * Pre-conditions: size() > 0
+ */
+ std::pair worst_item() const {
+ if (m_indices_dists.empty())
+ throw std::runtime_error("Cannot invoke RadiusResultSet::worst_item() on "
+ "an empty list of results.");
+ typedef
+ typename std::vector>::const_iterator
+ DistIt;
+ DistIt it = std::max_element(m_indices_dists.begin(), m_indices_dists.end(),
+ IndexDist_Sorter());
+ return *it;
+ }
+};
+
+/** @} */
+
+/** @addtogroup loadsave_grp Load/save auxiliary functions
+ * @{ */
+template
+void save_value(FILE *stream, const T &value, size_t count = 1) {
+ fwrite(&value, sizeof(value), count, stream);
+}
+
+template
+void save_value(FILE *stream, const std::vector &value) {
+ size_t size = value.size();
+ fwrite(&size, sizeof(size_t), 1, stream);
+ fwrite(&value[0], sizeof(T), size, stream);
+}
+
+template
+void load_value(FILE *stream, T &value, size_t count = 1) {
+ size_t read_cnt = fread(&value, sizeof(value), count, stream);
+ if (read_cnt != count) {
+ throw std::runtime_error("Cannot read from file");
+ }
+}
+
+template void load_value(FILE *stream, std::vector &value) {
+ size_t size;
+ size_t read_cnt = fread(&size, sizeof(size_t), 1, stream);
+ if (read_cnt != 1) {
+ throw std::runtime_error("Cannot read from file");
+ }
+ value.resize(size);
+ read_cnt = fread(&value[0], sizeof(T), size, stream);
+ if (read_cnt != size) {
+ throw std::runtime_error("Cannot read from file");
+ }
+}
+/** @} */
+
+/** @addtogroup metric_grp Metric (distance) classes
+ * @{ */
+
+struct Metric {};
+
+/** Manhattan distance functor (generic version, optimized for
+ * high-dimensionality data sets). Corresponding distance traits:
+ * nanoflann::metric_L1 \tparam T Type of the elements (e.g. double, float,
+ * uint8_t) \tparam _DistanceType Type of distance variables (must be signed)
+ * (e.g. float, double, int64_t)
+ */
+template
+struct L1_Adaptor {
+ typedef T ElementType;
+ typedef _DistanceType DistanceType;
+
+ const DataSource &data_source;
+
+ L1_Adaptor(const DataSource &_data_source) : data_source(_data_source) {}
+
+ inline DistanceType evalMetric(const T *a, const size_t b_idx, size_t size,
+ DistanceType worst_dist = -1) const {
+ DistanceType result = DistanceType();
+ const T *last = a + size;
+ const T *lastgroup = last - 3;
+ size_t d = 0;
+
+ /* Process 4 items with each loop for efficiency. */
+ while (a < lastgroup) {
+ const DistanceType diff0 =
+ std::abs(a[0] - data_source.kdtree_get_pt(b_idx, d++));
+ const DistanceType diff1 =
+ std::abs(a[1] - data_source.kdtree_get_pt(b_idx, d++));
+ const DistanceType diff2 =
+ std::abs(a[2] - data_source.kdtree_get_pt(b_idx, d++));
+ const DistanceType diff3 =
+ std::abs(a[3] - data_source.kdtree_get_pt(b_idx, d++));
+ result += diff0 + diff1 + diff2 + diff3;
+ a += 4;
+ if ((worst_dist > 0) && (result > worst_dist)) {
+ return result;
+ }
+ }
+ /* Process last 0-3 components. Not needed for standard vector lengths. */
+ while (a < last) {
+ result += std::abs(*a++ - data_source.kdtree_get_pt(b_idx, d++));
+ }
+ return result;
+ }
+
+ template
+ inline DistanceType accum_dist(const U a, const V b, const size_t) const {
+ return std::abs(a - b);
+ }
+};
+
+/** Squared Euclidean distance functor (generic version, optimized for
+ * high-dimensionality data sets). Corresponding distance traits:
+ * nanoflann::metric_L2 \tparam T Type of the elements (e.g. double, float,
+ * uint8_t) \tparam _DistanceType Type of distance variables (must be signed)
+ * (e.g. float, double, int64_t)
+ */
+template
+struct L2_Adaptor {
+ typedef T ElementType;
+ typedef _DistanceType DistanceType;
+
+ const DataSource &data_source;
+
+ L2_Adaptor(const DataSource &_data_source) : data_source(_data_source) {}
+
+ inline DistanceType evalMetric(const T *a, const size_t b_idx, size_t size,
+ DistanceType worst_dist = -1) const {
+ DistanceType result = DistanceType();
+ const T *last = a + size;
+ const T *lastgroup = last - 3;
+ size_t d = 0;
+
+ /* Process 4 items with each loop for efficiency. */
+ while (a < lastgroup) {
+ const DistanceType diff0 = a[0] - data_source.kdtree_get_pt(b_idx, d++);
+ const DistanceType diff1 = a[1] - data_source.kdtree_get_pt(b_idx, d++);
+ const DistanceType diff2 = a[2] - data_source.kdtree_get_pt(b_idx, d++);
+ const DistanceType diff3 = a[3] - data_source.kdtree_get_pt(b_idx, d++);
+ result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
+ a += 4;
+ if ((worst_dist > 0) && (result > worst_dist)) {
+ return result;
+ }
+ }
+ /* Process last 0-3 components. Not needed for standard vector lengths. */
+ while (a < last) {
+ const DistanceType diff0 = *a++ - data_source.kdtree_get_pt(b_idx, d++);
+ result += diff0 * diff0;
+ }
+ return result;
+ }
+
+ template
+ inline DistanceType accum_dist(const U a, const V b, const size_t) const {
+ return (a - b) * (a - b);
+ }
+};
+
+/** Squared Euclidean (L2) distance functor (suitable for low-dimensionality
+ * datasets, like 2D or 3D point clouds) Corresponding distance traits:
+ * nanoflann::metric_L2_Simple \tparam T Type of the elements (e.g. double,
+ * float, uint8_t) \tparam _DistanceType Type of distance variables (must be
+ * signed) (e.g. float, double, int64_t)
+ */
+template
+struct L2_Simple_Adaptor {
+ typedef T ElementType;
+ typedef _DistanceType DistanceType;
+
+ const DataSource &data_source;
+
+ L2_Simple_Adaptor(const DataSource &_data_source)
+ : data_source(_data_source) {}
+
+ inline DistanceType evalMetric(const T *a, const size_t b_idx,
+ size_t size) const {
+ DistanceType result = DistanceType();
+ for (size_t i = 0; i < size; ++i) {
+ const DistanceType diff = a[i] - data_source.kdtree_get_pt(b_idx, i);
+ result += diff * diff;
+ }
+ return result;
+ }
+
+ template
+ inline DistanceType accum_dist(const U a, const V b, const size_t) const {
+ return (a - b) * (a - b);
+ }
+};
+
+/** SO2 distance functor
+ * Corresponding distance traits: nanoflann::metric_SO2
+ * \tparam T Type of the elements (e.g. double, float)
+ * \tparam _DistanceType Type of distance variables (must be signed) (e.g.
+ * float, double) orientation is constrained to be in [-pi, pi]
+ */
+template
+struct SO2_Adaptor {
+ typedef T ElementType;
+ typedef _DistanceType DistanceType;
+
+ const DataSource &data_source;
+
+ SO2_Adaptor(const DataSource &_data_source) : data_source(_data_source) {}
+
+ inline DistanceType evalMetric(const T *a, const size_t b_idx,
+ size_t size) const {
+ return accum_dist(a[size - 1], data_source.kdtree_get_pt(b_idx, size - 1),
+ size - 1);
+ }
+
+ /** Note: this assumes that input angles are already in the range [-pi,pi] */
+ template
+ inline DistanceType accum_dist(const U a, const V b, const size_t) const {
+ DistanceType result = DistanceType();
+ DistanceType PI = pi_const();
+ result = b - a;
+ if (result > PI)
+ result -= 2 * PI;
+ else if (result < -PI)
+ result += 2 * PI;
+ return result;
+ }
+};
+
+/** SO3 distance functor (Uses L2_Simple)
+ * Corresponding distance traits: nanoflann::metric_SO3
+ * \tparam T Type of the elements (e.g. double, float)
+ * \tparam _DistanceType Type of distance variables (must be signed) (e.g.
+ * float, double)
+ */
+template
+struct SO3_Adaptor {
+ typedef T ElementType;
+ typedef _DistanceType DistanceType;
+
+ L2_Simple_Adaptor distance_L2_Simple;
+
+ SO3_Adaptor(const DataSource &_data_source)
+ : distance_L2_Simple(_data_source) {}
+
+ inline DistanceType evalMetric(const T *a, const size_t b_idx,
+ size_t size) const {
+ return distance_L2_Simple.evalMetric(a, b_idx, size);
+ }
+
+ template
+ inline DistanceType accum_dist(const U a, const V b, const size_t idx) const {
+ return distance_L2_Simple.accum_dist(a, b, idx);
+ }
+};
+
+/** Metaprogramming helper traits class for the L1 (Manhattan) metric */
+struct metric_L1 : public Metric {
+ template struct traits {
+ typedef L1_Adaptor distance_t;
+ };
+};
+/** Metaprogramming helper traits class for the L2 (Euclidean) metric */
+struct metric_L2 : public Metric {
+ template struct traits {
+ typedef L2_Adaptor distance_t;
+ };
+};
+/** Metaprogramming helper traits class for the L2_simple (Euclidean) metric */
+struct metric_L2_Simple : public Metric {
+ template struct traits {
+ typedef L2_Simple_Adaptor distance_t;
+ };
+};
+/** Metaprogramming helper traits class for the SO3_InnerProdQuat metric */
+struct metric_SO2 : public Metric {
+ template struct traits {
+ typedef SO2_Adaptor distance_t;
+ };
+};
+/** Metaprogramming helper traits class for the SO3_InnerProdQuat metric */
+struct metric_SO3 : public Metric {
+ template struct traits {
+ typedef SO3_Adaptor distance_t;
+ };
+};
+
+/** @} */
+
+/** @addtogroup param_grp Parameter structs
+ * @{ */
+
+/** Parameters (see README.md) */
+struct KDTreeSingleIndexAdaptorParams {
+ KDTreeSingleIndexAdaptorParams(size_t _leaf_max_size = 10)
+ : leaf_max_size(_leaf_max_size) {}
+
+ size_t leaf_max_size;
+};
+
+/** Search options for KDTreeSingleIndexAdaptor::findNeighbors() */
+struct SearchParams {
+ /** Note: The first argument (checks_IGNORED_) is ignored, but kept for
+ * compatibility with the FLANN interface */
+ SearchParams(int checks_IGNORED_ = 32, float eps_ = 0, bool sorted_ = true)
+ : checks(checks_IGNORED_), eps(eps_), sorted(sorted_) {}
+
+ int checks; //!< Ignored parameter (Kept for compatibility with the FLANN
+ //!< interface).
+ float eps; //!< search for eps-approximate neighbours (default: 0)
+ bool sorted; //!< only for radius search, require neighbours sorted by
+ //!< distance (default: true)
+};
+/** @} */
+
+/** @addtogroup memalloc_grp Memory allocation
+ * @{ */
+
+/**
+ * Allocates (using C's malloc) a generic type T.
+ *
+ * Params:
+ * count = number of instances to allocate.
+ * Returns: pointer (of type T*) to memory buffer
+ */
+template inline T *allocate(size_t count = 1) {
+ T *mem = static_cast(::malloc(sizeof(T) * count));
+ return mem;
+}
+
+/**
+ * Pooled storage allocator
+ *
+ * The following routines allow for the efficient allocation of storage in
+ * small chunks from a specified pool. Rather than allowing each structure
+ * to be freed individually, an entire pool of storage is freed at once.
+ * This method has two advantages over just using malloc() and free(). First,
+ * it is far more efficient for allocating small objects, as there is
+ * no overhead for remembering all the information needed to free each
+ * object or consolidating fragmented memory. Second, the decision about
+ * how long to keep an object is made at the time of allocation, and there
+ * is no need to track down all the objects to free them.
+ *
+ */
+
+const size_t WORDSIZE = 16;
+const size_t BLOCKSIZE = 8192;
+
+class PooledAllocator {
+ /* We maintain memory alignment to word boundaries by requiring that all
+ allocations be in multiples of the machine wordsize. */
+ /* Size of machine word in bytes. Must be power of 2. */
+ /* Minimum number of bytes requested at a time from the system. Must be
+ * multiple of WORDSIZE. */
+
+ size_t remaining; /* Number of bytes left in current block of storage. */
+ void *base; /* Pointer to base of current block of storage. */
+ void *loc; /* Current location in block to next allocate memory. */
+
+ void internal_init() {
+ remaining = 0;
+ base = NULL;
+ usedMemory = 0;
+ wastedMemory = 0;
+ }
+
+public:
+ size_t usedMemory;
+ size_t wastedMemory;
+
+ /**
+ Default constructor. Initializes a new pool.
+ */
+ PooledAllocator() { internal_init(); }
+
+ /**
+ * Destructor. Frees all the memory allocated in this pool.
+ */
+ ~PooledAllocator() { free_all(); }
+
+ /** Frees all allocated memory chunks */
+ void free_all() {
+ while (base != NULL) {
+ void *prev =
+ *(static_cast(base)); /* Get pointer to prev block. */
+ ::free(base);
+ base = prev;
+ }
+ internal_init();
+ }
+
+ /**
+ * Returns a pointer to a piece of new memory of the given size in bytes
+ * allocated from the pool.
+ */
+ void *malloc(const size_t req_size) {
+ /* Round size up to a multiple of wordsize. The following expression
+ only works for WORDSIZE that is a power of 2, by masking last bits of
+ incremented size to zero.
+ */
+ const size_t size = (req_size + (WORDSIZE - 1)) & ~(WORDSIZE - 1);
+
+ /* Check whether a new block must be allocated. Note that the first word
+ of a block is reserved for a pointer to the previous block.
+ */
+ if (size > remaining) {
+
+ wastedMemory += remaining;
+
+ /* Allocate new storage. */
+ const size_t blocksize =
+ (size + sizeof(void *) + (WORDSIZE - 1) > BLOCKSIZE)
+ ? size + sizeof(void *) + (WORDSIZE - 1)
+ : BLOCKSIZE;
+
+ // use the standard C malloc to allocate memory
+ void *m = ::malloc(blocksize);
+ if (!m) {
+ fprintf(stderr, "Failed to allocate memory.\n");
+ return NULL;
+ }
+
+ /* Fill first word of new block with pointer to previous block. */
+ static_cast(m)[0] = base;
+ base = m;
+
+ size_t shift = 0;
+ // int size_t = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) &
+ // (WORDSIZE-1))) & (WORDSIZE-1);
+
+ remaining = blocksize - sizeof(void *) - shift;
+ loc = (static_cast(m) + sizeof(void *) + shift);
+ }
+ void *rloc = loc;
+ loc = static_cast(loc) + size;
+ remaining -= size;
+
+ usedMemory += size;
+
+ return rloc;
+ }
+
+ /**
+ * Allocates (using this pool) a generic type T.
+ *
+ * Params:
+ * count = number of instances to allocate.
+ * Returns: pointer (of type T*) to memory buffer
+ */
+ template T *allocate(const size_t count = 1) {
+ T *mem = static_cast(this->malloc(sizeof(T) * count));
+ return mem;
+ }
+};
+/** @} */
+
+/** @addtogroup nanoflann_metaprog_grp Auxiliary metaprogramming stuff
+ * @{ */
+
+/** Used to declare fixed-size arrays when DIM>0, dynamically-allocated vectors
+ * when DIM=-1. Fixed size version for a generic DIM:
+ */
+template struct array_or_vector_selector {
+ typedef std::array container_t;
+};
+/** Dynamic size version */
+template struct array_or_vector_selector<-1, T> {
+ typedef std::vector container_t;
+};
+
+/** @} */
+
+/** kd-tree base-class
+ *
+ * Contains the member functions common to the classes KDTreeSingleIndexAdaptor
+ * and KDTreeSingleIndexDynamicAdaptor_.
+ *
+ * \tparam Derived The name of the class which inherits this class.
+ * \tparam DatasetAdaptor The user-provided adaptor (see comments above).
+ * \tparam Distance The distance metric to use, these are all classes derived
+ * from nanoflann::Metric \tparam DIM Dimensionality of data points (e.g. 3 for
+ * 3D points) \tparam IndexType Will be typically size_t or int
+ */
+
+template
+class KDTreeBaseClass {
+
+public:
+ /** Frees the previously-built index. Automatically called within
+ * buildIndex(). */
+ void freeIndex(Derived &obj) {
+ obj.pool.free_all();
+ obj.root_node = NULL;
+ obj.m_size_at_index_build = 0;
+ }
+
+ typedef typename Distance::ElementType ElementType;
+ typedef typename Distance::DistanceType DistanceType;
+
+ /*--------------------- Internal Data Structures --------------------------*/
+ struct Node {
+ /** Union used because a node can be either a LEAF node or a non-leaf node,
+ * so both data fields are never used simultaneously */
+ union {
+ struct leaf {
+ IndexType left, right; //!< Indices of points in leaf node
+ } lr;
+ struct nonleaf {
+ int divfeat; //!< Dimension used for subdivision.
+ DistanceType divlow, divhigh; //!< The values used for subdivision.
+ } sub;
+ } node_type;
+ Node *child1, *child2; //!< Child nodes (both=NULL mean its a leaf node)
+ };
+
+ typedef Node *NodePtr;
+
+ struct Interval {
+ ElementType low, high;
+ };
+
+ /**
+ * Array of indices to vectors in the dataset.
+ */
+ std::vector vind;
+
+ NodePtr root_node;
+
+ size_t m_leaf_max_size;
+
+ size_t m_size; //!< Number of current points in the dataset
+ size_t m_size_at_index_build; //!< Number of points in the dataset when the
+ //!< index was built
+ int dim; //!< Dimensionality of each data point
+
+ /** Define "BoundingBox" as a fixed-size or variable-size container depending
+ * on "DIM" */
+ typedef
+ typename array_or_vector_selector::container_t BoundingBox;
+
+ /** Define "distance_vector_t" as a fixed-size or variable-size container
+ * depending on "DIM" */
+ typedef typename array_or_vector_selector::container_t
+ distance_vector_t;
+
+ /** The KD-tree used to find neighbours */
+
+ BoundingBox root_bbox;
+
+ /**
+ * Pooled memory allocator.
+ *
+ * Using a pooled memory allocator is more efficient
+ * than allocating memory directly when there is a large
+ * number small of memory allocations.
+ */
+ PooledAllocator pool;
+
+ /** Returns number of points in dataset */
+ size_t size(const Derived &obj) const { return obj.m_size; }
+
+ /** Returns the length of each point in the dataset */
+ size_t veclen(const Derived &obj) {
+ return static_cast(DIM > 0 ? DIM : obj.dim);
+ }
+
+ /// Helper accessor to the dataset points:
+ inline ElementType dataset_get(const Derived &obj, size_t idx,
+ int component) const {
+ return obj.dataset.kdtree_get_pt(idx, component);
+ }
+
+ /**
+ * Computes the inde memory usage
+ * Returns: memory used by the index
+ */
+ size_t usedMemory(Derived &obj) {
+ return obj.pool.usedMemory + obj.pool.wastedMemory +
+ obj.dataset.kdtree_get_point_count() *
+ sizeof(IndexType); // pool memory and vind array memory
+ }
+
+ void computeMinMax(const Derived &obj, IndexType *ind, IndexType count,
+ int element, ElementType &min_elem,
+ ElementType &max_elem) {
+ min_elem = dataset_get(obj, ind[0], element);
+ max_elem = dataset_get(obj, ind[0], element);
+ for (IndexType i = 1; i < count; ++i) {
+ ElementType val = dataset_get(obj, ind[i], element);
+ if (val < min_elem)
+ min_elem = val;
+ if (val > max_elem)
+ max_elem = val;
+ }
+ }
+
+ /**
+ * Create a tree node that subdivides the list of vecs from vind[first]
+ * to vind[last]. The routine is called recursively on each sublist.
+ *
+ * @param left index of the first vector
+ * @param right index of the last vector
+ */
+ NodePtr divideTree(Derived &obj, const IndexType left, const IndexType right,
+ BoundingBox &bbox) {
+ NodePtr node = obj.pool.template allocate(); // allocate memory
+
+ /* If too few exemplars remain, then make this a leaf node. */
+ if ((right - left) <= static_cast(obj.m_leaf_max_size)) {
+ node->child1 = node->child2 = NULL; /* Mark as leaf node. */
+ node->node_type.lr.left = left;
+ node->node_type.lr.right = right;
+
+ // compute bounding-box of leaf points
+ for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
+ bbox[i].low = dataset_get(obj, obj.vind[left], i);
+ bbox[i].high = dataset_get(obj, obj.vind[left], i);
+ }
+ for (IndexType k = left + 1; k < right; ++k) {
+ for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
+ if (bbox[i].low > dataset_get(obj, obj.vind[k], i))
+ bbox[i].low = dataset_get(obj, obj.vind[k], i);
+ if (bbox[i].high < dataset_get(obj, obj.vind[k], i))
+ bbox[i].high = dataset_get(obj, obj.vind[k], i);
+ }
+ }
+ } else {
+ IndexType idx;
+ int cutfeat;
+ DistanceType cutval;
+ middleSplit_(obj, &obj.vind[0] + left, right - left, idx, cutfeat, cutval,
+ bbox);
+
+ node->node_type.sub.divfeat = cutfeat;
+
+ BoundingBox left_bbox(bbox);
+ left_bbox[cutfeat].high = cutval;
+ node->child1 = divideTree(obj, left, left + idx, left_bbox);
+
+ BoundingBox right_bbox(bbox);
+ right_bbox[cutfeat].low = cutval;
+ node->child2 = divideTree(obj, left + idx, right, right_bbox);
+
+ node->node_type.sub.divlow = left_bbox[cutfeat].high;
+ node->node_type.sub.divhigh = right_bbox[cutfeat].low;
+
+ for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
+ bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
+ bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
+ }
+ }
+
+ return node;
+ }
+
+ void middleSplit_(Derived &obj, IndexType *ind, IndexType count,
+ IndexType &index, int &cutfeat, DistanceType &cutval,
+ const BoundingBox &bbox) {
+ const DistanceType EPS = static_cast(0.00001);
+ ElementType max_span = bbox[0].high - bbox[0].low;
+ for (int i = 1; i < (DIM > 0 ? DIM : obj.dim); ++i) {
+ ElementType span = bbox[i].high - bbox[i].low;
+ if (span > max_span) {
+ max_span = span;
+ }
+ }
+ ElementType max_spread = -1;
+ cutfeat = 0;
+ for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
+ ElementType span = bbox[i].high - bbox[i].low;
+ if (span > (1 - EPS) * max_span) {
+ ElementType min_elem, max_elem;
+ computeMinMax(obj, ind, count, i, min_elem, max_elem);
+ ElementType spread = max_elem - min_elem;
+ ;
+ if (spread > max_spread) {
+ cutfeat = i;
+ max_spread = spread;
+ }
+ }
+ }
+ // split in the middle
+ DistanceType split_val = (bbox[cutfeat].low + bbox[cutfeat].high) / 2;
+ ElementType min_elem, max_elem;
+ computeMinMax(obj, ind, count, cutfeat, min_elem, max_elem);
+
+ if (split_val < min_elem)
+ cutval = min_elem;
+ else if (split_val > max_elem)
+ cutval = max_elem;
+ else
+ cutval = split_val;
+
+ IndexType lim1, lim2;
+ planeSplit(obj, ind, count, cutfeat, cutval, lim1, lim2);
+
+ if (lim1 > count / 2)
+ index = lim1;
+ else if (lim2 < count / 2)
+ index = lim2;
+ else
+ index = count / 2;
+ }
+
+ /**
+ * Subdivide the list of points by a plane perpendicular on axe corresponding
+ * to the 'cutfeat' dimension at 'cutval' position.
+ *
+ * On return:
+ * dataset[ind[0..lim1-1]][cutfeat]cutval
+ */
+ void planeSplit(Derived &obj, IndexType *ind, const IndexType count,
+ int cutfeat, DistanceType &cutval, IndexType &lim1,
+ IndexType &lim2) {
+ /* Move vector indices for left subtree to front of list. */
+ IndexType left = 0;
+ IndexType right = count - 1;
+ for (;;) {
+ while (left <= right && dataset_get(obj, ind[left], cutfeat) < cutval)
+ ++left;
+ while (right && left <= right &&
+ dataset_get(obj, ind[right], cutfeat) >= cutval)
+ --right;
+ if (left > right || !right)
+ break; // "!right" was added to support unsigned Index types
+ std::swap(ind[left], ind[right]);
+ ++left;
+ --right;
+ }
+ /* If either list is empty, it means that all remaining features
+ * are identical. Split in the middle to maintain a balanced tree.
+ */
+ lim1 = left;
+ right = count - 1;
+ for (;;) {
+ while (left <= right && dataset_get(obj, ind[left], cutfeat) <= cutval)
+ ++left;
+ while (right && left <= right &&
+ dataset_get(obj, ind[right], cutfeat) > cutval)
+ --right;
+ if (left > right || !right)
+ break; // "!right" was added to support unsigned Index types
+ std::swap(ind[left], ind[right]);
+ ++left;
+ --right;
+ }
+ lim2 = left;
+ }
+
+ DistanceType computeInitialDistances(const Derived &obj,
+ const ElementType *vec,
+ distance_vector_t &dists) const {
+ assert(vec);
+ DistanceType distsq = DistanceType();
+
+ for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
+ if (vec[i] < obj.root_bbox[i].low) {
+ dists[i] = obj.distance.accum_dist(vec[i], obj.root_bbox[i].low, i);
+ distsq += dists[i];
+ }
+ if (vec[i] > obj.root_bbox[i].high) {
+ dists[i] = obj.distance.accum_dist(vec[i], obj.root_bbox[i].high, i);
+ distsq += dists[i];
+ }
+ }
+ return distsq;
+ }
+
+ void save_tree(Derived &obj, FILE *stream, NodePtr tree) {
+ save_value(stream, *tree);
+ if (tree->child1 != NULL) {
+ save_tree(obj, stream, tree->child1);
+ }
+ if (tree->child2 != NULL) {
+ save_tree(obj, stream, tree->child2);
+ }
+ }
+
+ void load_tree(Derived &obj, FILE *stream, NodePtr &tree) {
+ tree = obj.pool.template allocate();
+ load_value(stream, *tree);
+ if (tree->child1 != NULL) {
+ load_tree(obj, stream, tree->child1);
+ }
+ if (tree->child2 != NULL) {
+ load_tree(obj, stream, tree->child2);
+ }
+ }
+
+ /** Stores the index in a binary file.
+ * IMPORTANT NOTE: The set of data points is NOT stored in the file, so when
+ * loading the index object it must be constructed associated to the same
+ * source of data points used while building it. See the example:
+ * examples/saveload_example.cpp \sa loadIndex */
+ void saveIndex_(Derived &obj, FILE *stream) {
+ save_value(stream, obj.m_size);
+ save_value(stream, obj.dim);
+ save_value(stream, obj.root_bbox);
+ save_value(stream, obj.m_leaf_max_size);
+ save_value(stream, obj.vind);
+ save_tree(obj, stream, obj.root_node);
+ }
+
+ /** Loads a previous index from a binary file.
+ * IMPORTANT NOTE: The set of data points is NOT stored in the file, so the
+ * index object must be constructed associated to the same source of data
+ * points used while building the index. See the example:
+ * examples/saveload_example.cpp \sa loadIndex */
+ void loadIndex_(Derived &obj, FILE *stream) {
+ load_value(stream, obj.m_size);
+ load_value(stream, obj.dim);
+ load_value(stream, obj.root_bbox);
+ load_value(stream, obj.m_leaf_max_size);
+ load_value(stream, obj.vind);
+ load_tree(obj, stream, obj.root_node);
+ }
+};
+
+/** @addtogroup kdtrees_grp KD-tree classes and adaptors
+ * @{ */
+
+/** kd-tree static index
+ *
+ * Contains the k-d trees and other information for indexing a set of points
+ * for nearest-neighbor matching.
+ *
+ * The class "DatasetAdaptor" must provide the following interface (can be
+ * non-virtual, inlined methods):
+ *
+ * \code
+ * // Must return the number of data poins
+ * inline size_t kdtree_get_point_count() const { ... }
+ *
+ *
+ * // Must return the dim'th component of the idx'th point in the class:
+ * inline T kdtree_get_pt(const size_t idx, const size_t dim) const { ... }
+ *
+ * // Optional bounding-box computation: return false to default to a standard
+ * bbox computation loop.
+ * // Return true if the BBOX was already computed by the class and returned
+ * in "bb" so it can be avoided to redo it again.
+ * // Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3
+ * for point clouds) template bool kdtree_get_bbox(BBOX &bb) const
+ * {
+ * bb[0].low = ...; bb[0].high = ...; // 0th dimension limits
+ * bb[1].low = ...; bb[1].high = ...; // 1st dimension limits
+ * ...
+ * return true;
+ * }
+ *
+ * \endcode
+ *
+ * \tparam DatasetAdaptor The user-provided adaptor (see comments above).
+ * \tparam Distance The distance metric to use: nanoflann::metric_L1,
+ * nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc. \tparam DIM
+ * Dimensionality of data points (e.g. 3 for 3D points) \tparam IndexType Will
+ * be typically size_t or int
+ */
+template
+class KDTreeSingleIndexAdaptor
+ : public KDTreeBaseClass<
+ KDTreeSingleIndexAdaptor,
+ Distance, DatasetAdaptor, DIM, IndexType> {
+public:
+ /** Deleted copy constructor*/
+ KDTreeSingleIndexAdaptor(
+ const KDTreeSingleIndexAdaptor
+ &) = delete;
+
+ /**
+ * The dataset used by this index
+ */
+ const DatasetAdaptor &dataset; //!< The source of our data
+
+ const KDTreeSingleIndexAdaptorParams index_params;
+
+ Distance distance;
+
+ typedef typename nanoflann::KDTreeBaseClass<
+ nanoflann::KDTreeSingleIndexAdaptor,
+ Distance, DatasetAdaptor, DIM, IndexType>
+ BaseClassRef;
+
+ typedef typename BaseClassRef::ElementType ElementType;
+ typedef typename BaseClassRef::DistanceType DistanceType;
+
+ typedef typename BaseClassRef::Node Node;
+ typedef Node *NodePtr;
+
+ typedef typename BaseClassRef::Interval Interval;
+ /** Define "BoundingBox" as a fixed-size or variable-size container depending
+ * on "DIM" */
+ typedef typename BaseClassRef::BoundingBox BoundingBox;
+
+ /** Define "distance_vector_t" as a fixed-size or variable-size container
+ * depending on "DIM" */
+ typedef typename BaseClassRef::distance_vector_t distance_vector_t;
+
+ /**
+ * KDTree constructor
+ *
+ * Refer to docs in README.md or online in
+ * https://github.com/jlblancoc/nanoflann
+ *
+ * The KD-Tree point dimension (the length of each point in the datase, e.g. 3
+ * for 3D points) is determined by means of:
+ * - The \a DIM template parameter if >0 (highest priority)
+ * - Otherwise, the \a dimensionality parameter of this constructor.
+ *
+ * @param inputData Dataset with the input features
+ * @param params Basically, the maximum leaf node size
+ */
+ KDTreeSingleIndexAdaptor(const int dimensionality,
+ const DatasetAdaptor &inputData,
+ const KDTreeSingleIndexAdaptorParams ¶ms =
+ KDTreeSingleIndexAdaptorParams())
+ : dataset(inputData), index_params(params), distance(inputData) {
+ BaseClassRef::root_node = NULL;
+ BaseClassRef::m_size = dataset.kdtree_get_point_count();
+ BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
+ BaseClassRef::dim = dimensionality;
+ if (DIM > 0)
+ BaseClassRef::dim = DIM;
+ BaseClassRef::m_leaf_max_size = params.leaf_max_size;
+
+ // Create a permutable array of indices to the input vectors.
+ init_vind();
+ }
+
+ /**
+ * Builds the index
+ */
+ void buildIndex() {
+ BaseClassRef::m_size = dataset.kdtree_get_point_count();
+ BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
+ init_vind();
+ this->freeIndex(*this);
+ BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
+ if (BaseClassRef::m_size == 0)
+ return;
+ computeBoundingBox(BaseClassRef::root_bbox);
+ BaseClassRef::root_node =
+ this->divideTree(*this, 0, BaseClassRef::m_size,
+ BaseClassRef::root_bbox); // construct the tree
+ }
+
+ /** \name Query methods
+ * @{ */
+
+ /**
+ * Find set of nearest neighbors to vec[0:dim-1]. Their indices are stored
+ * inside the result object.
+ *
+ * Params:
+ * result = the result object in which the indices of the
+ * nearest-neighbors are stored vec = the vector for which to search the
+ * nearest neighbors
+ *
+ * \tparam RESULTSET Should be any ResultSet
+ * \return True if the requested neighbors could be found.
+ * \sa knnSearch, radiusSearch
+ */
+ template
+ bool findNeighbors(RESULTSET &result, const ElementType *vec,
+ const SearchParams &searchParams) const {
+ assert(vec);
+ if (this->size(*this) == 0)
+ return false;
+ if (!BaseClassRef::root_node)
+ throw std::runtime_error(
+ "[nanoflann] findNeighbors() called before building the index.");
+ float epsError = 1 + searchParams.eps;
+
+ distance_vector_t
+ dists; // fixed or variable-sized container (depending on DIM)
+ auto zero = static_cast(0);
+ assign(dists, (DIM > 0 ? DIM : BaseClassRef::dim),
+ zero); // Fill it with zeros.
+ DistanceType distsq = this->computeInitialDistances(*this, vec, dists);
+ searchLevel(result, vec, BaseClassRef::root_node, distsq, dists,
+ epsError); // "count_leaf" parameter removed since was neither
+ // used nor returned to the user.
+ return result.full();
+ }
+
+ /**
+ * Find the "num_closest" nearest neighbors to the \a query_point[0:dim-1].
+ * Their indices are stored inside the result object. \sa radiusSearch,
+ * findNeighbors \note nChecks_IGNORED is ignored but kept for compatibility
+ * with the original FLANN interface. \return Number `N` of valid points in
+ * the result set. Only the first `N` entries in `out_indices` and
+ * `out_distances_sq` will be valid. Return may be less than `num_closest`
+ * only if the number of elements in the tree is less than `num_closest`.
+ */
+ size_t knnSearch(const ElementType *query_point, const size_t num_closest,
+ IndexType *out_indices, DistanceType *out_distances_sq,
+ const int /* nChecks_IGNORED */ = 10) const {
+ nanoflann::KNNResultSet resultSet(num_closest);
+ resultSet.init(out_indices, out_distances_sq);
+ this->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
+ return resultSet.size();
+ }
+
+ /**
+ * Find all the neighbors to \a query_point[0:dim-1] within a maximum radius.
+ * The output is given as a vector of pairs, of which the first element is a
+ * point index and the second the corresponding distance. Previous contents of
+ * \a IndicesDists are cleared.
+ *
+ * If searchParams.sorted==true, the output list is sorted by ascending
+ * distances.
+ *
+ * For a better performance, it is advisable to do a .reserve() on the vector
+ * if you have any wild guess about the number of expected matches.
+ *
+ * \sa knnSearch, findNeighbors, radiusSearchCustomCallback
+ * \return The number of points within the given radius (i.e. indices.size()
+ * or dists.size() )
+ */
+ size_t
+ radiusSearch(const ElementType *query_point, const DistanceType &radius,
+ std::vector> &IndicesDists,
+ const SearchParams &searchParams) const {
+ RadiusResultSet resultSet(radius, IndicesDists);
+ const size_t nFound =
+ radiusSearchCustomCallback(query_point, resultSet, searchParams);
+ if (searchParams.sorted)
+ std::sort(IndicesDists.begin(), IndicesDists.end(), IndexDist_Sorter());
+ return nFound;
+ }
+
+ /**
+ * Just like radiusSearch() but with a custom callback class for each point
+ * found in the radius of the query. See the source of RadiusResultSet<> as a
+ * start point for your own classes. \sa radiusSearch
+ */
+ template
+ size_t radiusSearchCustomCallback(
+ const ElementType *query_point, SEARCH_CALLBACK &resultSet,
+ const SearchParams &searchParams = SearchParams()) const {
+ this->findNeighbors(resultSet, query_point, searchParams);
+ return resultSet.size();
+ }
+
+ /** @} */
+
+public:
+ /** Make sure the auxiliary list \a vind has the same size than the current
+ * dataset, and re-generate if size has changed. */
+ void init_vind() {
+ // Create a permutable array of indices to the input vectors.
+ BaseClassRef::m_size = dataset.kdtree_get_point_count();
+ if (BaseClassRef::vind.size() != BaseClassRef::m_size)
+ BaseClassRef::vind.resize(BaseClassRef::m_size);
+ for (size_t i = 0; i < BaseClassRef::m_size; i++)
+ BaseClassRef::vind[i] = i;
+ }
+
+ void computeBoundingBox(BoundingBox &bbox) {
+ resize(bbox, (DIM > 0 ? DIM : BaseClassRef::dim));
+ if (dataset.kdtree_get_bbox(bbox)) {
+ // Done! It was implemented in derived class
+ } else {
+ const size_t N = dataset.kdtree_get_point_count();
+ if (!N)
+ throw std::runtime_error("[nanoflann] computeBoundingBox() called but "
+ "no data points found.");
+ for (int i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
+ bbox[i].low = bbox[i].high = this->dataset_get(*this, 0, i);
+ }
+ for (size_t k = 1; k < N; ++k) {
+ for (int i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
+ if (this->dataset_get(*this, k, i) < bbox[i].low)
+ bbox[i].low = this->dataset_get(*this, k, i);
+ if (this->dataset_get(*this, k, i) > bbox[i].high)
+ bbox[i].high = this->dataset_get(*this, k, i);
+ }
+ }
+ }
+ }
+
+ /**
+ * Performs an exact search in the tree starting from a node.
+ * \tparam RESULTSET Should be any ResultSet
+ * \return true if the search should be continued, false if the results are
+ * sufficient
+ */
+ template
+ bool searchLevel(RESULTSET &result_set, const ElementType *vec,
+ const NodePtr node, DistanceType mindistsq,
+ distance_vector_t &dists, const float epsError) const {
+ /* If this is a leaf node, then do check and return. */
+ if ((node->child1 == NULL) && (node->child2 == NULL)) {
+ // count_leaf += (node->lr.right-node->lr.left); // Removed since was
+ // neither used nor returned to the user.
+ DistanceType worst_dist = result_set.worstDist();
+ for (IndexType i = node->node_type.lr.left; i < node->node_type.lr.right;
+ ++i) {
+ const IndexType index = BaseClassRef::vind[i]; // reorder... : i;
+ DistanceType dist = distance.evalMetric(
+ vec, index, (DIM > 0 ? DIM : BaseClassRef::dim));
+ if (dist < worst_dist) {
+ if (!result_set.addPoint(dist, BaseClassRef::vind[i])) {
+ // the resultset doesn't want to receive any more points, we're done
+ // searching!
+ return false;
+ }
+ }
+ }
+ return true;
+ }
+
+ /* Which child branch should be taken first? */
+ int idx = node->node_type.sub.divfeat;
+ ElementType val = vec[idx];
+ DistanceType diff1 = val - node->node_type.sub.divlow;
+ DistanceType diff2 = val - node->node_type.sub.divhigh;
+
+ NodePtr bestChild;
+ NodePtr otherChild;
+ DistanceType cut_dist;
+ if ((diff1 + diff2) < 0) {
+ bestChild = node->child1;
+ otherChild = node->child2;
+ cut_dist = distance.accum_dist(val, node->node_type.sub.divhigh, idx);
+ } else {
+ bestChild = node->child2;
+ otherChild = node->child1;
+ cut_dist = distance.accum_dist(val, node->node_type.sub.divlow, idx);
+ }
+
+ /* Call recursively to search next level down. */
+ if (!searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError)) {
+ // the resultset doesn't want to receive any more points, we're done
+ // searching!
+ return false;
+ }
+
+ DistanceType dst = dists[idx];
+ mindistsq = mindistsq + cut_dist - dst;
+ dists[idx] = cut_dist;
+ if (mindistsq * epsError <= result_set.worstDist()) {
+ if (!searchLevel(result_set, vec, otherChild, mindistsq, dists,
+ epsError)) {
+ // the resultset doesn't want to receive any more points, we're done
+ // searching!
+ return false;
+ }
+ }
+ dists[idx] = dst;
+ return true;
+ }
+
+public:
+ /** Stores the index in a binary file.
+ * IMPORTANT NOTE: The set of data points is NOT stored in the file, so when
+ * loading the index object it must be constructed associated to the same
+ * source of data points used while building it. See the example:
+ * examples/saveload_example.cpp \sa loadIndex */
+ void saveIndex(FILE *stream) { this->saveIndex_(*this, stream); }
+
+ /** Loads a previous index from a binary file.
+ * IMPORTANT NOTE: The set of data points is NOT stored in the file, so the
+ * index object must be constructed associated to the same source of data
+ * points used while building the index. See the example:
+ * examples/saveload_example.cpp \sa loadIndex */
+ void loadIndex(FILE *stream) { this->loadIndex_(*this, stream); }
+
+}; // class KDTree
+
+/** kd-tree dynamic index
+ *
+ * Contains the k-d trees and other information for indexing a set of points
+ * for nearest-neighbor matching.
+ *
+ * The class "DatasetAdaptor" must provide the following interface (can be
+ * non-virtual, inlined methods):
+ *
+ * \code
+ * // Must return the number of data poins
+ * inline size_t kdtree_get_point_count() const { ... }
+ *
+ * // Must return the dim'th component of the idx'th point in the class:
+ * inline T kdtree_get_pt(const size_t idx, const size_t dim) const { ... }
+ *
+ * // Optional bounding-box computation: return false to default to a standard
+ * bbox computation loop.
+ * // Return true if the BBOX was already computed by the class and returned
+ * in "bb" so it can be avoided to redo it again.
+ * // Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3
+ * for point clouds) template bool kdtree_get_bbox(BBOX &bb) const
+ * {
+ * bb[0].low = ...; bb[0].high = ...; // 0th dimension limits
+ * bb[1].low = ...; bb[1].high = ...; // 1st dimension limits
+ * ...
+ * return true;
+ * }
+ *
+ * \endcode
+ *
+ * \tparam DatasetAdaptor The user-provided adaptor (see comments above).
+ * \tparam Distance The distance metric to use: nanoflann::metric_L1,
+ * nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc. \tparam DIM
+ * Dimensionality of data points (e.g. 3 for 3D points) \tparam IndexType Will
+ * be typically size_t or int
+ */
+template
+class KDTreeSingleIndexDynamicAdaptor_
+ : public KDTreeBaseClass,
+ Distance, DatasetAdaptor, DIM, IndexType> {
+public:
+ /**
+ * The dataset used by this index
+ */
+ const DatasetAdaptor &dataset; //!< The source of our data
+
+ KDTreeSingleIndexAdaptorParams index_params;
+
+ std::vector &treeIndex;
+
+ Distance distance;
+
+ typedef typename nanoflann::KDTreeBaseClass<
+ nanoflann::KDTreeSingleIndexDynamicAdaptor_,
+ Distance, DatasetAdaptor, DIM, IndexType>
+ BaseClassRef;
+
+ typedef typename BaseClassRef::ElementType ElementType;
+ typedef typename BaseClassRef::DistanceType DistanceType;
+
+ typedef typename BaseClassRef::Node Node;
+ typedef Node *NodePtr;
+
+ typedef typename BaseClassRef::Interval Interval;
+ /** Define "BoundingBox" as a fixed-size or variable-size container depending
+ * on "DIM" */
+ typedef typename BaseClassRef::BoundingBox BoundingBox;
+
+ /** Define "distance_vector_t" as a fixed-size or variable-size container
+ * depending on "DIM" */
+ typedef typename BaseClassRef::distance_vector_t distance_vector_t;
+
+ /**
+ * KDTree constructor
+ *
+ * Refer to docs in README.md or online in
+ * https://github.com/jlblancoc/nanoflann
+ *
+ * The KD-Tree point dimension (the length of each point in the datase, e.g. 3
+ * for 3D points) is determined by means of:
+ * - The \a DIM template parameter if >0 (highest priority)
+ * - Otherwise, the \a dimensionality parameter of this constructor.
+ *
+ * @param inputData Dataset with the input features
+ * @param params Basically, the maximum leaf node size
+ */
+ KDTreeSingleIndexDynamicAdaptor_(
+ const int dimensionality, const DatasetAdaptor &inputData,
+ std::vector &treeIndex_,
+ const KDTreeSingleIndexAdaptorParams ¶ms =
+ KDTreeSingleIndexAdaptorParams())
+ : dataset(inputData), index_params(params), treeIndex(treeIndex_),
+ distance(inputData) {
+ BaseClassRef::root_node = NULL;
+ BaseClassRef::m_size = 0;
+ BaseClassRef::m_size_at_index_build = 0;
+ BaseClassRef::dim = dimensionality;
+ if (DIM > 0)
+ BaseClassRef::dim = DIM;
+ BaseClassRef::m_leaf_max_size = params.leaf_max_size;
+ }
+
+ /** Assignment operator definiton */
+ KDTreeSingleIndexDynamicAdaptor_
+ operator=(const KDTreeSingleIndexDynamicAdaptor_ &rhs) {
+ KDTreeSingleIndexDynamicAdaptor_ tmp(rhs);
+ std::swap(BaseClassRef::vind, tmp.BaseClassRef::vind);
+ std::swap(BaseClassRef::m_leaf_max_size, tmp.BaseClassRef::m_leaf_max_size);
+ std::swap(index_params, tmp.index_params);
+ std::swap(treeIndex, tmp.treeIndex);
+ std::swap(BaseClassRef::m_size, tmp.BaseClassRef::m_size);
+ std::swap(BaseClassRef::m_size_at_index_build,
+ tmp.BaseClassRef::m_size_at_index_build);
+ std::swap(BaseClassRef::root_node, tmp.BaseClassRef::root_node);
+ std::swap(BaseClassRef::root_bbox, tmp.BaseClassRef::root_bbox);
+ std::swap(BaseClassRef::pool, tmp.BaseClassRef::pool);
+ return *this;
+ }
+
+ /**
+ * Builds the index
+ */
+ void buildIndex() {
+ BaseClassRef::m_size = BaseClassRef::vind.size();
+ this->freeIndex(*this);
+ BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
+ if (BaseClassRef::m_size == 0)
+ return;
+ computeBoundingBox(BaseClassRef::root_bbox);
+ BaseClassRef::root_node =
+ this->divideTree(*this, 0, BaseClassRef::m_size,
+ BaseClassRef::root_bbox); // construct the tree
+ }
+
+ /** \name Query methods
+ * @{ */
+
+ /**
+ * Find set of nearest neighbors to vec[0:dim-1]. Their indices are stored
+ * inside the result object.
+ *
+ * Params:
+ * result = the result object in which the indices of the
+ * nearest-neighbors are stored vec = the vector for which to search the
+ * nearest neighbors
+ *
+ * \tparam RESULTSET Should be any ResultSet
+ * \return True if the requested neighbors could be found.
+ * \sa knnSearch, radiusSearch
+ */
+ template
+ bool findNeighbors(RESULTSET &result, const ElementType *vec,
+ const SearchParams &searchParams) const {
+ assert(vec);
+ if (this->size(*this) == 0)
+ return false;
+ if (!BaseClassRef::root_node)
+ return false;
+ float epsError = 1 + searchParams.eps;
+
+ // fixed or variable-sized container (depending on DIM)
+ distance_vector_t dists;
+ // Fill it with zeros.
+ assign(dists, (DIM > 0 ? DIM : BaseClassRef::dim),
+ static_cast(0));
+ DistanceType distsq = this->computeInitialDistances(*this, vec, dists);
+ searchLevel(result, vec, BaseClassRef::root_node, distsq, dists,
+ epsError); // "count_leaf" parameter removed since was neither
+ // used nor returned to the user.
+ return result.full();
+ }
+
+ /**
+ * Find the "num_closest" nearest neighbors to the \a query_point[0:dim-1].
+ * Their indices are stored inside the result object. \sa radiusSearch,
+ * findNeighbors \note nChecks_IGNORED is ignored but kept for compatibility
+ * with the original FLANN interface. \return Number `N` of valid points in
+ * the result set. Only the first `N` entries in `out_indices` and
+ * `out_distances_sq` will be valid. Return may be less than `num_closest`
+ * only if the number of elements in the tree is less than `num_closest`.
+ */
+ size_t knnSearch(const ElementType *query_point, const size_t num_closest,
+ IndexType *out_indices, DistanceType *out_distances_sq,
+ const int /* nChecks_IGNORED */ = 10) const {
+ nanoflann::KNNResultSet resultSet(num_closest);
+ resultSet.init(out_indices, out_distances_sq);
+ this->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
+ return resultSet.size();
+ }
+
+ /**
+ * Find all the neighbors to \a query_point[0:dim-1] within a maximum radius.
+ * The output is given as a vector of pairs, of which the first element is a
+ * point index and the second the corresponding distance. Previous contents of
+ * \a IndicesDists are cleared.
+ *
+ * If searchParams.sorted==true, the output list is sorted by ascending
+ * distances.
+ *
+ * For a better performance, it is advisable to do a .reserve() on the vector
+ * if you have any wild guess about the number of expected matches.
+ *
+ * \sa knnSearch, findNeighbors, radiusSearchCustomCallback
+ * \return The number of points within the given radius (i.e. indices.size()
+ * or dists.size() )
+ */
+ size_t
+ radiusSearch(const ElementType *query_point, const DistanceType &radius,
+ std::vector> &IndicesDists,
+ const SearchParams &searchParams) const {
+ RadiusResultSet resultSet(radius, IndicesDists);
+ const size_t nFound =
+ radiusSearchCustomCallback(query_point, resultSet, searchParams);
+ if (searchParams.sorted)
+ std::sort(IndicesDists.begin(), IndicesDists.end(), IndexDist_Sorter());
+ return nFound;
+ }
+
+ /**
+ * Just like radiusSearch() but with a custom callback class for each point
+ * found in the radius of the query. See the source of RadiusResultSet<> as a
+ * start point for your own classes. \sa radiusSearch
+ */
+ template
+ size_t radiusSearchCustomCallback(
+ const ElementType *query_point, SEARCH_CALLBACK &resultSet,
+ const SearchParams &searchParams = SearchParams()) const {
+ this->findNeighbors(resultSet, query_point, searchParams);
+ return resultSet.size();
+ }
+
+ /** @} */
+
+public:
+ void computeBoundingBox(BoundingBox &bbox) {
+ resize(bbox, (DIM > 0 ? DIM : BaseClassRef::dim));
+
+ if (dataset.kdtree_get_bbox(bbox)) {
+ // Done! It was implemented in derived class
+ } else {
+ const size_t N = BaseClassRef::m_size;
+ if (!N)
+ throw std::runtime_error("[nanoflann] computeBoundingBox() called but "
+ "no data points found.");
+ for (int i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
+ bbox[i].low = bbox[i].high =
+ this->dataset_get(*this, BaseClassRef::vind[0], i);
+ }
+ for (size_t k = 1; k < N; ++k) {
+ for (int i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
+ if (this->dataset_get(*this, BaseClassRef::vind[k], i) < bbox[i].low)
+ bbox[i].low = this->dataset_get(*this, BaseClassRef::vind[k], i);
+ if (this->dataset_get(*this, BaseClassRef::vind[k], i) > bbox[i].high)
+ bbox[i].high = this->dataset_get(*this, BaseClassRef::vind[k], i);
+ }
+ }
+ }
+ }
+
+ /**
+ * Performs an exact search in the tree starting from a node.
+ * \tparam RESULTSET Should be any ResultSet
+ */
+ template
+ void searchLevel(RESULTSET &result_set, const ElementType *vec,
+ const NodePtr node, DistanceType mindistsq,
+ distance_vector_t &dists, const float epsError) const {
+ /* If this is a leaf node, then do check and return. */
+ if ((node->child1 == NULL) && (node->child2 == NULL)) {
+ // count_leaf += (node->lr.right-node->lr.left); // Removed since was
+ // neither used nor returned to the user.
+ DistanceType worst_dist = result_set.worstDist();
+ for (IndexType i = node->node_type.lr.left; i < node->node_type.lr.right;
+ ++i) {
+ const IndexType index = BaseClassRef::vind[i]; // reorder... : i;
+ if (treeIndex[index] == -1)
+ continue;
+ DistanceType dist = distance.evalMetric(
+ vec, index, (DIM > 0 ? DIM : BaseClassRef::dim));
+ if (dist < worst_dist) {
+ if (!result_set.addPoint(
+ static_cast(dist),
+ static_cast(
+ BaseClassRef::vind[i]))) {
+ // the resultset doesn't want to receive any more points, we're done
+ // searching!
+ return; // false;
+ }
+ }
+ }
+ return;
+ }
+
+ /* Which child branch should be taken first? */
+ int idx = node->node_type.sub.divfeat;
+ ElementType val = vec[idx];
+ DistanceType diff1 = val - node->node_type.sub.divlow;
+ DistanceType diff2 = val - node->node_type.sub.divhigh;
+
+ NodePtr bestChild;
+ NodePtr otherChild;
+ DistanceType cut_dist;
+ if ((diff1 + diff2) < 0) {
+ bestChild = node->child1;
+ otherChild = node->child2;
+ cut_dist = distance.accum_dist(val, node->node_type.sub.divhigh, idx);
+ } else {
+ bestChild = node->child2;
+ otherChild = node->child1;
+ cut_dist = distance.accum_dist(val, node->node_type.sub.divlow, idx);
+ }
+
+ /* Call recursively to search next level down. */
+ searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError);
+
+ DistanceType dst = dists[idx];
+ mindistsq = mindistsq + cut_dist - dst;
+ dists[idx] = cut_dist;
+ if (mindistsq * epsError <= result_set.worstDist()) {
+ searchLevel(result_set, vec, otherChild, mindistsq, dists, epsError);
+ }
+ dists[idx] = dst;
+ }
+
+public:
+ /** Stores the index in a binary file.
+ * IMPORTANT NOTE: The set of data points is NOT stored in the file, so when
+ * loading the index object it must be constructed associated to the same
+ * source of data points used while building it. See the example:
+ * examples/saveload_example.cpp \sa loadIndex */
+ void saveIndex(FILE *stream) { this->saveIndex_(*this, stream); }
+
+ /** Loads a previous index from a binary file.
+ * IMPORTANT NOTE: The set of data points is NOT stored in the file, so the
+ * index object must be constructed associated to the same source of data
+ * points used while building the index. See the example:
+ * examples/saveload_example.cpp \sa loadIndex */
+ void loadIndex(FILE *stream) { this->loadIndex_(*this, stream); }
+};
+
+/** kd-tree dynaimic index
+ *
+ * class to create multiple static index and merge their results to behave as
+ * single dynamic index as proposed in Logarithmic Approach.
+ *
+ * Example of usage:
+ * examples/dynamic_pointcloud_example.cpp
+ *
+ * \tparam DatasetAdaptor The user-provided adaptor (see comments above).
+ * \tparam Distance The distance metric to use: nanoflann::metric_L1,
+ * nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc. \tparam DIM
+ * Dimensionality of data points (e.g. 3 for 3D points) \tparam IndexType Will
+ * be typically size_t or int
+ */
+template
+class KDTreeSingleIndexDynamicAdaptor {
+public:
+ typedef typename Distance::ElementType ElementType;
+ typedef typename Distance::DistanceType DistanceType;
+
+protected:
+ size_t m_leaf_max_size;
+ size_t treeCount;
+ size_t pointCount;
+
+ /**
+ * The dataset used by this index
+ */
+ const DatasetAdaptor &dataset; //!< The source of our data
+
+ std::vector treeIndex; //!< treeIndex[idx] is the index of tree in which
+ //!< point at idx is stored. treeIndex[idx]=-1
+ //!< means that point has been removed.
+
+ KDTreeSingleIndexAdaptorParams index_params;
+
+ int dim; //!< Dimensionality of each data point
+
+ typedef KDTreeSingleIndexDynamicAdaptor_
+ index_container_t;
+ std::vector index;
+
+public:
+ /** Get a const ref to the internal list of indices; the number of indices is
+ * adapted dynamically as the dataset grows in size. */
+ const std::vector &getAllIndices() const { return index; }
+
+private:
+ /** finds position of least significant unset bit */
+ int First0Bit(IndexType num) {
+ int pos = 0;
+ while (num & 1) {
+ num = num >> 1;
+ pos++;
+ }
+ return pos;
+ }
+
+ /** Creates multiple empty trees to handle dynamic support */
+ void init() {
+ typedef KDTreeSingleIndexDynamicAdaptor_
+ my_kd_tree_t;
+ std::vector index_(
+ treeCount, my_kd_tree_t(dim /*dim*/, dataset, treeIndex, index_params));
+ index = index_;
+ }
+
+public:
+ Distance distance;
+
+ /**
+ * KDTree constructor
+ *
+ * Refer to docs in README.md or online in
+ * https://github.com/jlblancoc/nanoflann
+ *
+ * The KD-Tree point dimension (the length of each point in the datase, e.g. 3
+ * for 3D points) is determined by means of:
+ * - The \a DIM template parameter if >0 (highest priority)
+ * - Otherwise, the \a dimensionality parameter of this constructor.
+ *
+ * @param inputData Dataset with the input features
+ * @param params Basically, the maximum leaf node size
+ */
+ KDTreeSingleIndexDynamicAdaptor(const int dimensionality,
+ const DatasetAdaptor &inputData,
+ const KDTreeSingleIndexAdaptorParams ¶ms =
+ KDTreeSingleIndexAdaptorParams(),
+ const size_t maximumPointCount = 1000000000U)
+ : dataset(inputData), index_params(params), distance(inputData) {
+ treeCount = static_cast(std::log2(maximumPointCount));
+ pointCount = 0U;
+ dim = dimensionality;
+ treeIndex.clear();
+ if (DIM > 0)
+ dim = DIM;
+ m_leaf_max_size = params.leaf_max_size;
+ init();
+ const size_t num_initial_points = dataset.kdtree_get_point_count();
+ if (num_initial_points > 0) {
+ addPoints(0, num_initial_points - 1);
+ }
+ }
+
+ /** Deleted copy constructor*/
+ KDTreeSingleIndexDynamicAdaptor(
+ const KDTreeSingleIndexDynamicAdaptor &) = delete;
+
+ /** Add points to the set, Inserts all points from [start, end] */
+ void addPoints(IndexType start, IndexType end) {
+ size_t count = end - start + 1;
+ treeIndex.resize(treeIndex.size() + count);
+ for (IndexType idx = start; idx <= end; idx++) {
+ int pos = First0Bit(pointCount);
+ index[pos].vind.clear();
+ treeIndex[pointCount] = pos;
+ for (int i = 0; i < pos; i++) {
+ for (int j = 0; j < static_cast(index[i].vind.size()); j++) {
+ index[pos].vind.push_back(index[i].vind[j]);
+ if (treeIndex[index[i].vind[j]] != -1)
+ treeIndex[index[i].vind[j]] = pos;
+ }
+ index[i].vind.clear();
+ index[i].freeIndex(index[i]);
+ }
+ index[pos].vind.push_back(idx);
+ index[pos].buildIndex();
+ pointCount++;
+ }
+ }
+
+ /** Remove a point from the set (Lazy Deletion) */
+ void removePoint(size_t idx) {
+ if (idx >= pointCount)
+ return;
+ treeIndex[idx] = -1;
+ }
+
+ /**
+ * Find set of nearest neighbors to vec[0:dim-1]. Their indices are stored
+ * inside the result object.
+ *
+ * Params:
+ * result = the result object in which the indices of the
+ * nearest-neighbors are stored vec = the vector for which to search the
+ * nearest neighbors
+ *
+ * \tparam RESULTSET Should be any ResultSet
+ * \return True if the requested neighbors could be found.
+ * \sa knnSearch, radiusSearch
+ */
+ template
+ bool findNeighbors(RESULTSET &result, const ElementType *vec,
+ const SearchParams &searchParams) const {
+ for (size_t i = 0; i < treeCount; i++) {
+ index[i].findNeighbors(result, &vec[0], searchParams);
+ }
+ return result.full();
+ }
+};
+
+/** An L2-metric KD-tree adaptor for working with data directly stored in an
+ * Eigen Matrix, without duplicating the data storage. Each row in the matrix
+ * represents a point in the state space.
+ *
+ * Example of usage:
+ * \code
+ * Eigen::Matrix mat;
+ * // Fill out "mat"...
+ *
+ * typedef KDTreeEigenMatrixAdaptor< Eigen::Matrix >
+ * my_kd_tree_t; const int max_leaf = 10; my_kd_tree_t mat_index(mat, max_leaf
+ * ); mat_index.index->buildIndex(); mat_index.index->... \endcode
+ *
+ * \tparam DIM If set to >0, it specifies a compile-time fixed dimensionality
+ * for the points in the data set, allowing more compiler optimizations. \tparam
+ * Distance The distance metric to use: nanoflann::metric_L1,
+ * nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
+ */
+template
+struct KDTreeEigenMatrixAdaptor {
+ typedef KDTreeEigenMatrixAdaptor self_t;
+ typedef typename MatrixType::Scalar num_t;
+ typedef typename MatrixType::Index IndexType;
+ typedef
+ typename Distance::template traits::distance_t metric_t;
+ typedef KDTreeSingleIndexAdaptor
+ index_t;
+
+ index_t *index; //! The kd-tree index for the user to call its methods as
+ //! usual with any other FLANN index.
+
+ /// Constructor: takes a const ref to the matrix object with the data points
+ KDTreeEigenMatrixAdaptor(const size_t dimensionality,
+ const std::reference_wrapper &mat,
+ const int leaf_max_size = 10)
+ : m_data_matrix(mat) {
+ const auto dims = mat.get().cols();
+ if (size_t(dims) != dimensionality)
+ throw std::runtime_error(
+ "Error: 'dimensionality' must match column count in data matrix");
+ if (DIM > 0 && int(dims) != DIM)
+ throw std::runtime_error(
+ "Data set dimensionality does not match the 'DIM' template argument");
+ index =
+ new index_t(static_cast(dims), *this /* adaptor */,
+ nanoflann::KDTreeSingleIndexAdaptorParams(leaf_max_size));
+ index->buildIndex();
+ }
+
+public:
+ /** Deleted copy constructor */
+ KDTreeEigenMatrixAdaptor(const self_t &) = delete;
+
+ ~KDTreeEigenMatrixAdaptor() { delete index; }
+
+ const std::reference_wrapper m_data_matrix;
+
+ /** Query for the \a num_closest closest points to a given point (entered as
+ * query_point[0:dim-1]). Note that this is a short-cut method for
+ * index->findNeighbors(). The user can also call index->... methods as
+ * desired. \note nChecks_IGNORED is ignored but kept for compatibility with
+ * the original FLANN interface.
+ */
+ inline void query(const num_t *query_point, const size_t num_closest,
+ IndexType *out_indices, num_t *out_distances_sq,
+ const int /* nChecks_IGNORED */ = 10) const {
+ nanoflann::KNNResultSet resultSet(num_closest);
+ resultSet.init(out_indices, out_distances_sq);
+ index->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
+ }
+
+ /** @name Interface expected by KDTreeSingleIndexAdaptor
+ * @{ */
+
+ const self_t &derived() const { return *this; }
+ self_t &derived() { return *this; }
+
+ // Must return the number of data points
+ inline size_t kdtree_get_point_count() const {
+ return m_data_matrix.get().rows();
+ }
+
+ // Returns the dim'th component of the idx'th point in the class:
+ inline num_t kdtree_get_pt(const IndexType idx, size_t dim) const {
+ return m_data_matrix.get().coeff(idx, IndexType(dim));
+ }
+
+ // Optional bounding-box computation: return false to default to a standard
+ // bbox computation loop.
+ // Return true if the BBOX was already computed by the class and returned in
+ // "bb" so it can be avoided to redo it again. Look at bb.size() to find out
+ // the expected dimensionality (e.g. 2 or 3 for point clouds)
+ template bool kdtree_get_bbox(BBOX & /*bb*/) const {
+ return false;
+ }
+
+ /** @} */
+
+}; // end of KDTreeEigenMatrixAdaptor
+ /** @} */
+
+/** @} */ // end of grouping
+} // namespace nanoflann
+
+#endif /* NANOFLANN_HPP_ */
diff --git a/src/cpp/module/Scancontext/tictoc.h b/src/cpp/module/Scancontext/tictoc.h
new file mode 100644
index 0000000..937abf8
--- /dev/null
+++ b/src/cpp/module/Scancontext/tictoc.h
@@ -0,0 +1,47 @@
+// Author: Tong Qin qintonguav@gmail.com
+// Shaozu Cao saozu.cao@connect.ust.hk
+
+#pragma once
+
+#include
+#include
+#include
+#include
+#include
+
+class TicToc
+{
+public:
+ TicToc()
+ {
+ tic();
+ }
+
+ TicToc( bool _disp )
+ {
+ disp_ = _disp;
+ tic();
+ }
+
+ void tic()
+ {
+ start = std::chrono::system_clock::now();
+ }
+
+ void toc( std::string _about_task )
+ {
+ end = std::chrono::system_clock::now();
+ std::chrono::duration elapsed_seconds = end - start;
+ double elapsed_ms = elapsed_seconds.count() * 1000;
+
+ if( disp_ )
+ {
+ std::cout.precision(3); // 10 for sec, 3 for ms
+ std::cout << _about_task << ": " << elapsed_ms << " msec." << std::endl;
+ }
+ }
+
+private:
+ std::chrono::time_point start, end;
+ bool disp_ = false;
+};
diff --git a/src/example/SLAM/README.md b/src/example/SLAM/README.md
new file mode 100644
index 0000000..2b29331
--- /dev/null
+++ b/src/example/SLAM/README.md
@@ -0,0 +1,2 @@
+# Scan Context for LiDAR SLAM
+- Go to [PyICP SLAM](https://github.com/kissb2/PyICP-SLAM)
diff --git a/src/example/basic/basic.m b/src/example/basic/basic.m
new file mode 100644
index 0000000..c8a5fbe
--- /dev/null
+++ b/src/example/basic/basic.m
@@ -0,0 +1,81 @@
+clear; clc;
+addpath(genpath('../../matlab/'));
+
+
+%% Parameters
+data_dir = '../../sample_data/KITTI/00/velodyne/';
+
+basic_max_range = 80; % meter
+basic_num_sectors = 60;
+basic_num_rings = 20;
+
+
+%% Visualization of ScanContext
+bin_path = [data_dir, '000094.bin'];
+ptcloud = KITTIbin2Ptcloud(bin_path);
+sc = Ptcloud2ScanContext(ptcloud, basic_num_sectors, basic_num_rings, basic_max_range);
+
+h1 = figure(1); clf;
+imagesc(sc);
+set(gcf, 'Position', [10 10 800 300]);
+xlabel('sector'); ylabel('ring');
+
+% for vivid visualization
+colormap jet;
+caxis([0, 4]); % KITTI00 is usually in z: [0, 4]
+
+
+%% Making Ringkey and maintaining kd-tree
+% Read the PlaceRecognizer.m
+
+
+%% ScanContext with different resolution
+
+figure(2); clf;
+pcshow(ptcloud); colormap jet; caxis([0 4]);
+
+res = [0.25, 0.5, 1, 2, 3];
+
+h2=figure(3); clf;
+set(gcf, 'Position', [10 10 500 1000]);
+
+for i = 1:length(res)
+ num_sectors = basic_num_sectors * res(i);
+ num_rings = basic_num_rings * res(i);
+
+ sc = Ptcloud2ScanContext(ptcloud, num_sectors, num_rings, basic_max_range);
+
+ subplot(length(res), 1, i);
+ imagesc(sc); hold on;
+ colormap jet;
+ caxis([0, 4]); % KITTI00 is usually in z: [0, 4]
+end
+
+
+
+%% Comparison btn two scan contexts
+
+KITTI_bin1a_path = [data_dir, '000094.bin'];
+KITTI_bin1b_path = [data_dir, '000095.bin'];
+KITTI_bin2a_path = [data_dir, '000198.bin'];
+KITTI_bin2b_path = [data_dir, '000199.bin'];
+
+ptcloud_KITTI1a = KITTIbin2Ptcloud(KITTI_bin1a_path);
+ptcloud_KITTI1b = KITTIbin2Ptcloud(KITTI_bin1b_path);
+ptcloud_KITTI2a = KITTIbin2Ptcloud(KITTI_bin2a_path);
+ptcloud_KITTI2b = KITTIbin2Ptcloud(KITTI_bin2b_path);
+
+sc_KITTI1a = Ptcloud2ScanContext(ptcloud_KITTI1a, basic_num_sectors, basic_num_rings, basic_max_range);
+sc_KITTI1b = Ptcloud2ScanContext(ptcloud_KITTI1b, basic_num_sectors, basic_num_rings, basic_max_range);
+sc_KITTI2a = Ptcloud2ScanContext(ptcloud_KITTI2a, basic_num_sectors, basic_num_rings, basic_max_range);
+sc_KITTI2b = Ptcloud2ScanContext(ptcloud_KITTI2b, basic_num_sectors, basic_num_rings, basic_max_range);
+
+dist_1a_1b = DistanceBtnScanContexts(sc_KITTI1a, sc_KITTI1b);
+dist_1b_2a = DistanceBtnScanContexts(sc_KITTI1b, sc_KITTI2a);
+dist_1a_2a = DistanceBtnScanContexts(sc_KITTI1a, sc_KITTI2a);
+dist_2a_2b = DistanceBtnScanContexts(sc_KITTI2a, sc_KITTI2b);
+
+disp([dist_1a_1b, dist_1b_2a, dist_1a_2a, dist_2a_2b]);
+
+
+
diff --git a/src/example/basic/ptcloud.png b/src/example/basic/ptcloud.png
new file mode 100644
index 0000000..182444e
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diff --git a/src/example/basic/scmaking.gif b/src/example/basic/scmaking.gif
new file mode 100644
index 0000000..88fc5d9
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diff --git a/src/example/basic/various_res.png b/src/example/basic/various_res.png
new file mode 100644
index 0000000..d6391c8
Binary files /dev/null and b/src/example/basic/various_res.png differ
diff --git a/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/Main.m b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/Main.m
new file mode 100644
index 0000000..9aaa74c
--- /dev/null
+++ b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/Main.m
@@ -0,0 +1,112 @@
+%% information
+% main for Sampling places
+
+%%
+clear; clc;
+addpath(genpath('../../../../../matlab/'));
+addpath(genpath('./helper'));
+
+SaveDirectoryList
+Parameters
+
+%% Preparation 1: make pre-determined Grid Cell index
+PlaceIndexAndGridCenters_10m = makeGridCellIndex(xRange, yRange, 10);
+
+%% Preparation 2: get scan times
+SequenceDate = '2012-01-15'; % ### Change this part to your date
+ScanBaseDir = 'F:\NCLT/'; % ### Change this part to your path
+
+ScanDir = strcat(ScanBaseDir, SequenceDate, '/velodyne_sync/');
+Scans = dir(ScanDir); Scans(1:2, :) = []; Scans = {Scans(:).name};
+ScanTimes = getNCLTscanInformation(Scans);
+
+%% Preparation 3: load GT pose (for calc moving diff and location)
+GroundTruthPosePath = strcat(ScanBaseDir, SequenceDate, '/groundtruth_', SequenceDate, '.csv');
+GroundTruthPoseData = csvread(GroundTruthPosePath);
+
+GroundTruthPoseTime = GroundTruthPoseData(:, 1);
+GroundTruthPoseXYZ = GroundTruthPoseData(:, 2:4);
+
+nGroundTruthPoses = length(GroundTruthPoseData);
+
+%% logger
+TrajectoryInformationWRT10mCell = [];
+
+nTotalSampledPlaces = 0;
+
+%% Main: Sampling
+
+MoveCounter = 0; % Reset 0 again for every SamplingGap reached.
+for ii = 1000:nGroundTruthPoses % just quite large number 1000 for avoiding first several NaNs
+ curTime = GroundTruthPoseTime(ii, 1);
+
+ prvPose = GroundTruthPoseXYZ(ii-1, :);
+ curPose = GroundTruthPoseXYZ(ii, :);
+
+ curMove = norm(curPose - prvPose);
+ MoveCounter = MoveCounter + curMove;
+
+ if(MoveCounter >= SamplingGap)
+ nTotalSampledPlaces = nTotalSampledPlaces + 1;
+ curSamplingCounter = nTotalSampledPlaces;
+
+ % Returns the index of the cell, where the current pose is closest to the cell's center coordinates.
+ PlaceIdx_10m = getPlaceIdx(curPose, PlaceIndexAndGridCenters_10m); % 2nd argument is cell's size
+
+ % load current point cloud
+ curPtcloud = getNearestPtcloud( ScanTimes, curTime, Scans, ScanDir);
+
+ %% Save data
+ % log
+ TrajectoryInformationWRT10mCell = [TrajectoryInformationWRT10mCell; curTime, curPose, nTotalSampledPlaces, PlaceIdx_10m];
+
+ % scan context
+ ScanContextForward = Ptcloud2ScanContext(curPtcloud, nSectors, nRings, Lmax);
+
+ % SCI gray (1 channel)
+ ScanContextForwardRanged = ScaleSc2Img(ScanContextForward, NCLTminHeight, NCLTmaxHeight);
+ ScanContextForwardScaled = ScanContextForwardRanged./maxColor;
+
+ SCIforwardGray = round(ScanContextForwardScaled*255);
+ SCIforwardGray = ind2gray(SCIforwardGray, gray(255));
+
+ % SCI jet (color, 3 channel)
+ SCIforwardColor = round(ScanContextForwardScaled*255);
+ SCIforwardColor = ind2rgb(SCIforwardColor, jet(255));
+
+ saveSCIcolor(SCIforwardColor, DIR_SCIcolor, curSamplingCounter, PlaceIdx_10m, '10', 'f');
+
+ % SCI jet + Backward dataAug
+ ScanContextBackwardScaled = circshift(ScanContextForwardScaled, nSectors/2, 2);
+ SCIbackwardColor = round(ScanContextBackwardScaled*255);
+ SCIbackwardColor = ind2rgb(SCIbackwardColor, jet(255));
+
+ saveSCIcolor(SCIforwardColor, DIR_SCIcolorAlsoBack, curSamplingCounter, PlaceIdx_10m, '10', 'f');
+ saveSCIcolor(SCIbackwardColor, DIR_SCIcolorAlsoBack, curSamplingCounter, PlaceIdx_10m, '10', 'b');
+
+ % End: Reset counter
+ MoveCounter = 0;
+
+ % Tracking progress message
+ if(rem(curSamplingCounter, 100) == 0)
+ message = strcat(num2str(curSamplingCounter), "th sample is saved." );
+ disp(message)
+ end
+
+ end
+
+end
+
+
+%% save Trajectory Information
+% 10m
+filepath = strcat(DIR_SampledPlacesInformation, '/TrajectoryInformation.csv');
+TrajectoryInformation = TrajectoryInformationWRT10mCell;
+dlmwrite(filepath, TrajectoryInformation, 'precision','%.6f')
+
+
+
+
+
+
+
diff --git a/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/Parameters.m b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/Parameters.m
new file mode 100644
index 0000000..0533ed8
--- /dev/null
+++ b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/Parameters.m
@@ -0,0 +1,29 @@
+% flag for train/test
+IF_TRAINING = 1;
+
+% sampling gap
+SamplingGap = 1; % in meter
+
+% place resolution
+PlaceCellSize = 10; % in meter
+
+% NCLT region
+xRange = [-350, 130];
+yRange = [-730, 120];
+
+
+% scan context
+nRings = 40;
+nSectors = 120;
+Lmax = 80;
+
+% scan context image
+NCLTminHeight = 0;
+NCLTmaxHeight = 15;
+
+SCI_HEIGHT_RANGE = [NCLTminHeight, NCLTmaxHeight];
+
+minColor = 0;
+maxColor = 255;
+rangeColor = maxColor - minColor;
+
diff --git a/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/SaveDirectoryList.m b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/SaveDirectoryList.m
new file mode 100644
index 0000000..8c7ec11
--- /dev/null
+++ b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/SaveDirectoryList.m
@@ -0,0 +1,6 @@
+
+%% Save Directories
+
+DIR_SampledPlacesInformation = './data/SampledPlacesInformation/';
+DIR_SCIcolor = './data/SCI_jet0to15/';
+DIR_SCIcolorAlsoBack = './data/SCI_jet0to15_BackAug/';
diff --git a/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/ScaleSc2Img.m b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/ScaleSc2Img.m
new file mode 100644
index 0000000..baf3041
--- /dev/null
+++ b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/ScaleSc2Img.m
@@ -0,0 +1,33 @@
+function [ scScaled ] = ScaleSc2Img( scOriginal, minHeight, maxHeight )
+
+maxColor = 255;
+
+rangeHeight = maxHeight - minHeight;
+
+nRows = size(scOriginal, 1);
+nCols = size(scOriginal, 2);
+
+scOriginalRangeCut = scOriginal;
+% cut into range
+for ithRow = 1:nRows
+ for jthCol = 1:nCols
+
+ ithPixel = scOriginal(ithRow, jthCol);
+
+ if(ithPixel >= maxHeight)
+ scOriginalRangeCut(ithRow, jthCol) = maxHeight;
+ end
+
+ if(ithPixel <= minHeight)
+ scOriginalRangeCut(ithRow, jthCol) = minHeight;
+ end
+
+ scOriginalRangeCut(ithRow, jthCol) = round(scOriginalRangeCut(ithRow, jthCol) * (maxColor/rangeHeight));
+ end
+end
+
+scScaled = scOriginalRangeCut;
+
+
+end
+
diff --git a/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/getNCLTscanInformation.m b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/getNCLTscanInformation.m
new file mode 100644
index 0000000..58e40e3
--- /dev/null
+++ b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/getNCLTscanInformation.m
@@ -0,0 +1,15 @@
+function [ ScanTimes ] = getNCLTscanInformation( Scans )
+
+nScans = length(Scans);
+
+TIME_LENGTH = 16; % Dont Change this
+ScanTimes = zeros(nScans, 1);
+for i=1:nScans
+ ithScanName = Scans{i};
+ ithScanTime = str2double(ithScanName(1:TIME_LENGTH));
+ ScanTimes(i) = ithScanTime;
+end
+
+
+end
+
diff --git a/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/getNearestPtcloud.m b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/getNearestPtcloud.m
new file mode 100644
index 0000000..a6349e4
--- /dev/null
+++ b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/getNearestPtcloud.m
@@ -0,0 +1,9 @@
+function [ Ptcloud ] = getNearestPtcloud( ScanTimes, curTime, Scans, ScanDir)
+
+[MinTimeDelta, ArgminIdx] = min(abs(ScanTimes-curTime));
+ArgminBinName = Scans{ArgminIdx};
+ArgminBinPath = strcat(ScanDir, ArgminBinName);
+Ptcloud = NCLTbin2Ptcloud(ArgminBinPath);
+
+end
+
diff --git a/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/getPlaceIdx.m b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/getPlaceIdx.m
new file mode 100644
index 0000000..ec662d2
--- /dev/null
+++ b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/getPlaceIdx.m
@@ -0,0 +1,23 @@
+function [ PlaceIdx ] = getPlaceIdx(curPose, PlaceIndexAndGridCenters)
+
+%% load meta file
+
+%% Main
+curX = curPose(1);
+curY = curPose(2);
+
+PlaceCellCenters = PlaceIndexAndGridCenters(:, 2:3);
+nPlaces = length(PlaceCellCenters);
+
+Dists = zeros(nPlaces, 1);
+for ii=1:nPlaces
+ Dist = norm(PlaceCellCenters(ii, :) - [curX, curY]);
+ Dists(ii) = Dist;
+end
+
+[NearestDist, NearestIdx] = min(Dists);
+
+PlaceIdx = NearestIdx;
+
+end
+
diff --git a/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/getThetaFromXY.m b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/getThetaFromXY.m
new file mode 100644
index 0000000..828b38b
--- /dev/null
+++ b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/getThetaFromXY.m
@@ -0,0 +1,17 @@
+function [ theta ] = getThetaFromXY( x, y )
+
+ if (x >= 0 && y >= 0)
+ theta = 180/pi * atan(y/x);
+ end
+ if (x < 0 && y >= 0)
+ theta = 180 - ((180/pi) * atan(y/(-x)));
+ end
+ if (x < 0 && y < 0)
+ theta = 180 + ((180/pi) * atan(y/x));
+ end
+ if ( x >= 0 && y < 0)
+ theta = 360 - ((180/pi) * atan((-y)/x));
+ end
+
+end
+
diff --git a/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/makeGridCellIndex.m b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/makeGridCellIndex.m
new file mode 100644
index 0000000..033b50d
--- /dev/null
+++ b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/makeGridCellIndex.m
@@ -0,0 +1,28 @@
+function [ PlaceIndexAndGridCenters ] = makeGridCellIndex ( xRange, yRange, PlaceCellSize )
+
+xSize = xRange(2) - xRange(1);
+ySize = yRange(2) - yRange(1);
+
+nGridX = round(xSize/PlaceCellSize);
+nGridY = round(ySize/PlaceCellSize);
+
+xGridBoundaries = linspace(xRange(1), xRange(2), nGridX+1);
+yGridBoundaries = linspace(yRange(1), yRange(2), nGridY+1);
+
+nTotalIndex = nGridX * nGridY;
+
+curAssignedIndex = 1;
+PlaceIndexAndGridCenters = zeros(nTotalIndex, 3);
+for ii=1:nGridX
+ xGridCenter = (xGridBoundaries(ii+1) + xGridBoundaries(ii))/2;
+ for jj=1:nGridY
+ yGridCenter = (yGridBoundaries(jj+1) + yGridBoundaries(jj))/2;
+
+ PlaceIndexAndGridCenters(curAssignedIndex, :) = [curAssignedIndex, xGridCenter, yGridCenter];
+ curAssignedIndex = curAssignedIndex + 1;
+ end
+
+end
+
+end
+
diff --git a/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/saveSCIcolor.m b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/saveSCIcolor.m
new file mode 100644
index 0000000..9d39388
--- /dev/null
+++ b/src/example/longterm_localization/NCLT/2012-01-15/1_DataMaker/helper/saveSCIcolor.m
@@ -0,0 +1,22 @@
+function [ ] = saveSCIcolor(curSCIcolor, DIR_SCIcolor, SamplingCounter, PlaceIdx, CellSize, ForB)
+
+SCIcolor = curSCIcolor;
+
+curSamplingCounter = num2str(SamplingCounter,'%0#6.f');
+curSamplingCounter = curSamplingCounter(1:end-1);
+
+curPlaceIdx = num2str(PlaceIdx,'%0#6.f');
+curPlaceIdx = curPlaceIdx(1:end-1);
+
+saveName = strcat(curSamplingCounter, '_', curPlaceIdx);
+saveDir = strcat(DIR_SCIcolor, CellSize, '/');
+if( ~exist(saveDir))
+ mkdir(saveDir)
+end
+
+savePath = strcat(saveDir, saveName, ForB, '.png');
+
+imwrite(SCIcolor, savePath);
+
+end
+
diff --git a/src/example/longterm_localization/NCLT/2012-01-15/2_Train/.ipynb_checkpoints/train-checkpoint.ipynb b/src/example/longterm_localization/NCLT/2012-01-15/2_Train/.ipynb_checkpoints/train-checkpoint.ipynb
new file mode 100644
index 0000000..66a93e2
--- /dev/null
+++ b/src/example/longterm_localization/NCLT/2012-01-15/2_Train/.ipynb_checkpoints/train-checkpoint.ipynb
@@ -0,0 +1,302 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import numpy as np \n",
+ "import pandas as pd \n",
+ "import tensorflow as tf\n",
+ "import keras\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 115,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Data info \n",
+ "rootDir = '..your_data_path/'\n",
+ "\n",
+ "Dataset = 'NCLT'\n",
+ "TrainOrTest = '/Train/'\n",
+ "SequenceDate = '2012-01-15'\n",
+ "\n",
+ "SCImiddlePath = '/5. SCI_jet0to15_BackAug/'\n",
+ "\n",
+ "GridCellSize = '10'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 116,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/media/gskim/IRAP-ADV1/Data/ICRA2019/NCLT/Train/2012-01-15/5. SCI_jet0to15_BackAug/10/\n"
+ ]
+ }
+ ],
+ "source": [
+ "DataPath = rootDir + Dataset + TrainOrTest + SequenceDate + SCImiddlePath + GridCellSize + '/'\n",
+ "print(DataPath)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 181,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def getTrainingDataNCLT(DataPath, SequenceDate): \n",
+ "\n",
+ " # info\n",
+ " WholeData = os.listdir(DataPath)\n",
+ " nWholeData = len(WholeData)\n",
+ " print(str(nWholeData) + ' data exist in ' + SequenceDate)\n",
+ " \n",
+ " # read \n",
+ " X = []\n",
+ " y = []\n",
+ " for ii in range(nWholeData):\n",
+ " dataName = WholeData[ii]\n",
+ " dataPath = DataPath + dataName\n",
+ " \n",
+ " dataTrajNodeOrder = int(dataName[0:5])\n",
+ "\n",
+ " SCI = plt.imread(dataPath)\n",
+ " dataPlaceIndex = int(dataName[6:11])\n",
+ " \n",
+ " X.append(SCI)\n",
+ " y.append(dataPlaceIndex)\n",
+ " \n",
+ " # progress message \n",
+ " if ii%1000==0:\n",
+ " print(str(format((ii/nWholeData)*100, '.1f')), '% loaded.')\n",
+ " \n",
+ " \n",
+ " dataShape = SCI.shape\n",
+ " \n",
+ " # X\n",
+ " X_nd = np.zeros(shape=(nWholeData, dataShape[0], dataShape[1], dataShape[2]))\n",
+ " for jj in range(len(X)):\n",
+ " X_nd[jj, :, :] = X[jj]\n",
+ " X_nd = X_nd.astype('float32')\n",
+ " \n",
+ " # y (one-hot encoded)\n",
+ " from sklearn.preprocessing import LabelEncoder\n",
+ " lbl_enc = LabelEncoder()\n",
+ " lbl_enc.fit(y)\n",
+ " \n",
+ " ClassesTheSequenceHave = lbl_enc.classes_\n",
+ " nClassesTheSequenceHave = len(ClassesTheSequenceHave)\n",
+ " \n",
+ " y = lbl_enc.transform(y)\n",
+ " y_nd = keras.utils.np_utils.to_categorical(y, num_classes=nClassesTheSequenceHave)\n",
+ "\n",
+ " # log message \n",
+ " print('Data size: %s' % nWholeData)\n",
+ " print(' ')\n",
+ " print('Data shape:', X_nd.shape)\n",
+ " print('Label shape:', y_nd.shape)\n",
+ " \n",
+ " return X_nd, y_nd, lbl_enc\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 182,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "15078 data exist in 2012-01-15\n",
+ "0.0 % loaded.\n",
+ "6.6 % loaded.\n",
+ "13.3 % loaded.\n",
+ "19.9 % loaded.\n",
+ "26.5 % loaded.\n",
+ "33.2 % loaded.\n",
+ "39.8 % loaded.\n",
+ "46.4 % loaded.\n",
+ "53.1 % loaded.\n",
+ "59.7 % loaded.\n",
+ "66.3 % loaded.\n",
+ "73.0 % loaded.\n",
+ "79.6 % loaded.\n",
+ "86.2 % loaded.\n",
+ "92.9 % loaded.\n",
+ "99.5 % loaded.\n",
+ "Data size: 15078\n",
+ "Data shape: (15078, 40, 120, 3)\n",
+ "Label shape: (15078, 579)\n"
+ ]
+ }
+ ],
+ "source": [
+ "[X, y, lbl_enc] = getTrainingDataNCLT(DataPath, SequenceDate)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 172,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(40, 120, 3)\n",
+ "(579,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "dataShape = X[0].shape\n",
+ "labelShape = y[0].shape\n",
+ "\n",
+ "print(dataShape)\n",
+ "print(labelShape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 173,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Model \n",
+ "from keras import backend as K\n",
+ "K.clear_session()\n",
+ "\n",
+ "ModelName = 'my_model'\n",
+ "\n",
+ "Drop1 = 0.7\n",
+ "Drop2 = 0.7\n",
+ "\n",
+ "KernelSize = 5\n",
+ "\n",
+ "nConv1Filter = 64\n",
+ "nConv2Filter = 128\n",
+ "nConv3Filter = 256\n",
+ "\n",
+ "nFCN1 = 64\n",
+ "\n",
+ "inputs = keras.layers.Input(shape=(dataShape[0], dataShape[1], dataShape[2]))\n",
+ "x = keras.layers.Conv2D(filters=nConv1Filter, kernel_size=KernelSize, activation='relu', padding='same')(inputs)\n",
+ "x = keras.layers.MaxPooling2D(pool_size=(2, 2), strides=None, padding='valid')(x)\n",
+ "x = keras.layers.BatchNormalization()(x)\n",
+ "x = keras.layers.Conv2D(filters=nConv2Filter, kernel_size=KernelSize, activation='relu', padding='same')(x)\n",
+ "x = keras.layers.MaxPool2D()(x)\n",
+ "x = keras.layers.BatchNormalization()(x)\n",
+ "x = keras.layers.Conv2D(filters=nConv3Filter, kernel_size=KernelSize, activation='relu', padding='same')(x)\n",
+ "x = keras.layers.MaxPool2D()(x)\n",
+ "x = keras.layers.Flatten()(x)\n",
+ "x = keras.layers.Dropout(rate=Drop1)(x)\n",
+ "x = keras.layers.Dense(units=nFCN1)(x)\n",
+ "x = keras.layers.Dropout(rate=Drop2)(x)\n",
+ "outputs = keras.layers.Dense(units=labelShape[0], activation='softmax')(x)\n",
+ "\n",
+ "model = keras.models.Model(inputs=inputs, outputs=outputs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 174,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Model Compile \n",
+ "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])\n",
+ "model.build(None,)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "#Train\n",
+ "X_train = X\n",
+ "y_train = y\n",
+ "\n",
+ "nEpoch = 200\n",
+ "\n",
+ "model.fit(X_train,\n",
+ " y_train,\n",
+ " epochs=nEpoch,\n",
+ " batch_size=64,\n",
+ " verbose=1\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Train\n",
+ "X_train = X\n",
+ "y_train = y\n",
+ "\n",
+ "nEpoch = 200\n",
+ "\n",
+ "model.fit(X_train,\n",
+ " y_train,\n",
+ " epochs=nEpoch,\n",
+ " batch_size=64,\n",
+ " verbose=1\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 186,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# model save \n",
+ "modelName = 'ModelWS_' + GridCellSize + 'm_' + ModelName + '.h5'\n",
+ "model.save(modelName)\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.5.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/example/longterm_localization/NCLT/2012-01-15/2_Train/train.ipynb b/src/example/longterm_localization/NCLT/2012-01-15/2_Train/train.ipynb
new file mode 100644
index 0000000..66a93e2
--- /dev/null
+++ b/src/example/longterm_localization/NCLT/2012-01-15/2_Train/train.ipynb
@@ -0,0 +1,302 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import numpy as np \n",
+ "import pandas as pd \n",
+ "import tensorflow as tf\n",
+ "import keras\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 115,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Data info \n",
+ "rootDir = '..your_data_path/'\n",
+ "\n",
+ "Dataset = 'NCLT'\n",
+ "TrainOrTest = '/Train/'\n",
+ "SequenceDate = '2012-01-15'\n",
+ "\n",
+ "SCImiddlePath = '/5. SCI_jet0to15_BackAug/'\n",
+ "\n",
+ "GridCellSize = '10'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 116,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/media/gskim/IRAP-ADV1/Data/ICRA2019/NCLT/Train/2012-01-15/5. SCI_jet0to15_BackAug/10/\n"
+ ]
+ }
+ ],
+ "source": [
+ "DataPath = rootDir + Dataset + TrainOrTest + SequenceDate + SCImiddlePath + GridCellSize + '/'\n",
+ "print(DataPath)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 181,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def getTrainingDataNCLT(DataPath, SequenceDate): \n",
+ "\n",
+ " # info\n",
+ " WholeData = os.listdir(DataPath)\n",
+ " nWholeData = len(WholeData)\n",
+ " print(str(nWholeData) + ' data exist in ' + SequenceDate)\n",
+ " \n",
+ " # read \n",
+ " X = []\n",
+ " y = []\n",
+ " for ii in range(nWholeData):\n",
+ " dataName = WholeData[ii]\n",
+ " dataPath = DataPath + dataName\n",
+ " \n",
+ " dataTrajNodeOrder = int(dataName[0:5])\n",
+ "\n",
+ " SCI = plt.imread(dataPath)\n",
+ " dataPlaceIndex = int(dataName[6:11])\n",
+ " \n",
+ " X.append(SCI)\n",
+ " y.append(dataPlaceIndex)\n",
+ " \n",
+ " # progress message \n",
+ " if ii%1000==0:\n",
+ " print(str(format((ii/nWholeData)*100, '.1f')), '% loaded.')\n",
+ " \n",
+ " \n",
+ " dataShape = SCI.shape\n",
+ " \n",
+ " # X\n",
+ " X_nd = np.zeros(shape=(nWholeData, dataShape[0], dataShape[1], dataShape[2]))\n",
+ " for jj in range(len(X)):\n",
+ " X_nd[jj, :, :] = X[jj]\n",
+ " X_nd = X_nd.astype('float32')\n",
+ " \n",
+ " # y (one-hot encoded)\n",
+ " from sklearn.preprocessing import LabelEncoder\n",
+ " lbl_enc = LabelEncoder()\n",
+ " lbl_enc.fit(y)\n",
+ " \n",
+ " ClassesTheSequenceHave = lbl_enc.classes_\n",
+ " nClassesTheSequenceHave = len(ClassesTheSequenceHave)\n",
+ " \n",
+ " y = lbl_enc.transform(y)\n",
+ " y_nd = keras.utils.np_utils.to_categorical(y, num_classes=nClassesTheSequenceHave)\n",
+ "\n",
+ " # log message \n",
+ " print('Data size: %s' % nWholeData)\n",
+ " print(' ')\n",
+ " print('Data shape:', X_nd.shape)\n",
+ " print('Label shape:', y_nd.shape)\n",
+ " \n",
+ " return X_nd, y_nd, lbl_enc\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 182,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "15078 data exist in 2012-01-15\n",
+ "0.0 % loaded.\n",
+ "6.6 % loaded.\n",
+ "13.3 % loaded.\n",
+ "19.9 % loaded.\n",
+ "26.5 % loaded.\n",
+ "33.2 % loaded.\n",
+ "39.8 % loaded.\n",
+ "46.4 % loaded.\n",
+ "53.1 % loaded.\n",
+ "59.7 % loaded.\n",
+ "66.3 % loaded.\n",
+ "73.0 % loaded.\n",
+ "79.6 % loaded.\n",
+ "86.2 % loaded.\n",
+ "92.9 % loaded.\n",
+ "99.5 % loaded.\n",
+ "Data size: 15078\n",
+ "Data shape: (15078, 40, 120, 3)\n",
+ "Label shape: (15078, 579)\n"
+ ]
+ }
+ ],
+ "source": [
+ "[X, y, lbl_enc] = getTrainingDataNCLT(DataPath, SequenceDate)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 172,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(40, 120, 3)\n",
+ "(579,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "dataShape = X[0].shape\n",
+ "labelShape = y[0].shape\n",
+ "\n",
+ "print(dataShape)\n",
+ "print(labelShape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 173,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Model \n",
+ "from keras import backend as K\n",
+ "K.clear_session()\n",
+ "\n",
+ "ModelName = 'my_model'\n",
+ "\n",
+ "Drop1 = 0.7\n",
+ "Drop2 = 0.7\n",
+ "\n",
+ "KernelSize = 5\n",
+ "\n",
+ "nConv1Filter = 64\n",
+ "nConv2Filter = 128\n",
+ "nConv3Filter = 256\n",
+ "\n",
+ "nFCN1 = 64\n",
+ "\n",
+ "inputs = keras.layers.Input(shape=(dataShape[0], dataShape[1], dataShape[2]))\n",
+ "x = keras.layers.Conv2D(filters=nConv1Filter, kernel_size=KernelSize, activation='relu', padding='same')(inputs)\n",
+ "x = keras.layers.MaxPooling2D(pool_size=(2, 2), strides=None, padding='valid')(x)\n",
+ "x = keras.layers.BatchNormalization()(x)\n",
+ "x = keras.layers.Conv2D(filters=nConv2Filter, kernel_size=KernelSize, activation='relu', padding='same')(x)\n",
+ "x = keras.layers.MaxPool2D()(x)\n",
+ "x = keras.layers.BatchNormalization()(x)\n",
+ "x = keras.layers.Conv2D(filters=nConv3Filter, kernel_size=KernelSize, activation='relu', padding='same')(x)\n",
+ "x = keras.layers.MaxPool2D()(x)\n",
+ "x = keras.layers.Flatten()(x)\n",
+ "x = keras.layers.Dropout(rate=Drop1)(x)\n",
+ "x = keras.layers.Dense(units=nFCN1)(x)\n",
+ "x = keras.layers.Dropout(rate=Drop2)(x)\n",
+ "outputs = keras.layers.Dense(units=labelShape[0], activation='softmax')(x)\n",
+ "\n",
+ "model = keras.models.Model(inputs=inputs, outputs=outputs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 174,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Model Compile \n",
+ "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])\n",
+ "model.build(None,)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "#Train\n",
+ "X_train = X\n",
+ "y_train = y\n",
+ "\n",
+ "nEpoch = 200\n",
+ "\n",
+ "model.fit(X_train,\n",
+ " y_train,\n",
+ " epochs=nEpoch,\n",
+ " batch_size=64,\n",
+ " verbose=1\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Train\n",
+ "X_train = X\n",
+ "y_train = y\n",
+ "\n",
+ "nEpoch = 200\n",
+ "\n",
+ "model.fit(X_train,\n",
+ " y_train,\n",
+ " epochs=nEpoch,\n",
+ " batch_size=64,\n",
+ " verbose=1\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 186,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# model save \n",
+ "modelName = 'ModelWS_' + GridCellSize + 'm_' + ModelName + '.h5'\n",
+ "model.save(modelName)\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.5.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/.ipynb_checkpoints/inference-checkpoint.ipynb b/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/.ipynb_checkpoints/inference-checkpoint.ipynb
new file mode 100644
index 0000000..bbacf87
--- /dev/null
+++ b/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/.ipynb_checkpoints/inference-checkpoint.ipynb
@@ -0,0 +1,303 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import numpy as np \n",
+ "import pandas as pd \n",
+ "import tensorflow as tf\n",
+ "import keras\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Data info \n",
+ "rootDir = '..your_path'\n",
+ "\n",
+ "Dataset = 'NCLT'\n",
+ "TrainOrTest = '/Test/'\n",
+ "SequenceDate = '2013-04-05'\n",
+ "\n",
+ "SCImiddlePath = '/4. SCI_jet0to15/'\n",
+ "\n",
+ "GridCellSize = '10'\n",
+ "\n",
+ "DataPath = rootDir + Dataset + TrainOrTest + SequenceDate + SCImiddlePath + GridCellSize + '/'\n",
+ "print(DataPath)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "DataPath = ICRArootDir + Dataset + TrainOrTest + SequenceDate + SCImiddlePath + GridCellSize + '/'\n",
+ "print(DataPath)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def getTestDataNCLT(DataPath, SequenceDate, lbl_enc_train): \n",
+ "\n",
+ " # load training label encoding information \n",
+ " from sklearn.preprocessing import LabelEncoder\n",
+ " ClassesTrainingSequenceHave = lbl_enc_train.classes_\n",
+ " nClassesTrainingSequenceHave = len(ClassesTrainingSequenceHave)\n",
+ "\n",
+ " # info\n",
+ " WholeData = os.listdir(DataPath)\n",
+ " nWholeData = len(WholeData)\n",
+ " print(str(nWholeData) + ' data exist in ' + SequenceDate)\n",
+ " \n",
+ " # read \n",
+ " X_seen = []\n",
+ " y_seen = []\n",
+ " X_unseen = []\n",
+ " y_unseen = []\n",
+ " \n",
+ " for ii in range(nWholeData):\n",
+ " dataName = WholeData[ii]\n",
+ " dataPath = DataPath + dataName\n",
+ " \n",
+ " dataTrajNodeOrder = int(dataName[0:5])\n",
+ "\n",
+ " SCI = plt.imread(dataPath)\n",
+ " dataPlaceIndex = int(dataName[6:11])\n",
+ " \n",
+ " # if label is in the train, then save into the seen (seen is only subset to be tested)\n",
+ " if dataPlaceIndex in ClassesTrainingSequenceHave:\n",
+ " X_seen.append(SCI)\n",
+ " y_seen.append(dataPlaceIndex)\n",
+ " else:\n",
+ " X_unseen.append(SCI)\n",
+ " y_unseen.append(dataPlaceIndex)\n",
+ " \n",
+ " # progress message \n",
+ " if ii%1000==0:\n",
+ " print(str(format((ii/nWholeData)*100, '.1f')), '% loaded.')\n",
+ " \n",
+ " dataShape = SCI.shape\n",
+ " \n",
+ " # X\n",
+ " nSeenData = len(X_seen)\n",
+ " X_nd = np.zeros(shape=(nSeenData, dataShape[0], dataShape[1], dataShape[2]))\n",
+ " for jj in range(nSeenData):\n",
+ " X_nd[jj, :, :] = X_seen[jj]\n",
+ " X_nd = X_nd.astype('float32')\n",
+ " \n",
+ " # y (one-hot encoded) \n",
+ " y_seen = lbl_enc_train.transform(y_seen)\n",
+ " y_nd = keras.utils.np_utils.to_categorical(y_seen, num_classes=nClassesTrainingSequenceHave)\n",
+ "\n",
+ " # log message \n",
+ " print('Data size: %s' % nWholeData)\n",
+ " print('- Seen data: %s' % len(X_seen))\n",
+ " print('- Uneen data: %s' % len(X_unseen))\n",
+ " print(' ')\n",
+ " print('Data shape:', X_nd.shape)\n",
+ " print('Label shape:', y_nd.shape)\n",
+ " \n",
+ " return X_nd, y_nd, X_unseen, y_unseen\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load training label encoding information for discriminate seen/unseen of test \n",
+ "import pickle\n",
+ "TrainingDate = '2012-01-15'\n",
+ "TrainingDataPath = 'data_pickle/Train_' + TrainingDate + '_SCI_color.pkl'\n",
+ "\n",
+ "with open(TrainingDataPath, 'rb') as f: # Python 3: open(..., 'rb')\n",
+ " X_train, y_train, lbl_enc_train = pickle.load(f)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load test data using training label encoder information \n",
+ "[X_seen, y_seen, X_unseen, y_unseen] = getTestDataNCLT(DataPath, SequenceDate, lbl_enc_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/gskim/anaconda3/envs/tfkeras/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+ " from ._conv import register_converters as _register_converters\n",
+ "Using TensorFlow backend.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "input_1 (InputLayer) (None, 40, 120, 3) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_1 (Conv2D) (None, 40, 120, 64) 4864 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_1 (MaxPooling2 (None, 20, 60, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_1 (Batch (None, 20, 60, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "conv2d_2 (Conv2D) (None, 20, 60, 128) 204928 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_2 (MaxPooling2 (None, 10, 30, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_2 (Batch (None, 10, 30, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "conv2d_3 (Conv2D) (None, 10, 30, 256) 819456 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_3 (MaxPooling2 (None, 5, 15, 256) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten_1 (Flatten) (None, 19200) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_1 (Dropout) (None, 19200) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_1 (Dense) (None, 64) 1228864 \n",
+ "_________________________________________________________________\n",
+ "dropout_2 (Dropout) (None, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_2 (Dense) (None, 579) 37635 \n",
+ "=================================================================\n",
+ "Total params: 2,296,515\n",
+ "Trainable params: 2,296,131\n",
+ "Non-trainable params: 384\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "from keras.models import load_model\n",
+ "modelName = 'pre_trained_model/base0.h5'\n",
+ "testModel = load_model(modelName)\n",
+ "\n",
+ "testModel.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "5170/5170 [==============================] - 1s 182us/step\n",
+ "2012-02-04\n",
+ "- Test score: 1.008079155962518\n",
+ "- Test accuracy: 82.76595741913904\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load Trained net \n",
+ "from keras.models import load_model\n",
+ "modelName = 'model/base0.h5'\n",
+ "testModel = load_model(modelName)\n",
+ "\n",
+ "# Predict \n",
+ "scores_TEST = testModel.evaluate(X_seen, y_seen, verbose=1, batch_size=1000)\n",
+ "print(SequenceDate)\n",
+ "print('- Test score:', scores_TEST[0])\n",
+ "print('- Test accuracy:', scores_TEST[1]*100)\n",
+ "print(' ')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# save prediction (for later top N analysis )\n",
+ "y_seen_predicted = testModel.predict(X_seen)\n",
+ "\n",
+ "# save \n",
+ "filename_y_seen_predicted = 'results_predictionvectors/base0/' + SequenceDate + '_seen_predicted'\n",
+ "np.save(filename_y_seen_predicted, y_seen_predicted)\n",
+ "\n",
+ "# save prediction (for later top N analysis )\n",
+ "X_unseen = np.array(X_unseen)\n",
+ "y_unseen_predicted = testModel.predict(X_unseen)\n",
+ "\n",
+ "# save \n",
+ "filename_y_unseen_predicted = 'results_predictionvectors/base0/' + SequenceDate + '_unseen_predicted'\n",
+ "np.save(filename_y_unseen_predicted, y_unseen_predicted)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "### save GT also \n",
+ "\n",
+ "# seen \n",
+ "filename_y_seen_GT = 'results_predictionvectors/base0/' + SequenceDate + '_seen_GT'\n",
+ "np.save(filename_y_seen_GT, y_seen)\n",
+ "\n",
+ "# unseen\n",
+ "filename_y_unseen_GT = 'results_predictionvectors/base0/' + SequenceDate + '_unseen_GT'\n",
+ "np.save(filename_y_unseen_GT, y_unseen)\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.5.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/PRcurve/drawPRcurve_NCLT_SCI.m b/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/PRcurve/drawPRcurve_NCLT_SCI.m
new file mode 100644
index 0000000..689799c
--- /dev/null
+++ b/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/PRcurve/drawPRcurve_NCLT_SCI.m
@@ -0,0 +1,100 @@
+clear
+
+%% Path info
+Dataset = 'NCLT';
+Method = 'SCI';
+% ResultsDir = strcat('Result/', Dataset, '/', Method, '/');
+
+ResultsDir = 'Result/';
+
+%% Params
+FigIdx = 1;
+figure(FigIdx); clf;
+
+TopNindexes = [25];
+nTopNindexes = length(TopNindexes);
+
+%% Main
+SequenceNames = dir(ResultsDir); SequenceNames(1:2, :) = []; SequenceNames = {SequenceNames(:).name};
+nSequences = length(SequenceNames);
+
+for ithTopN = 1:nTopNindexes
+
+ TopNidx = TopNindexes(ithTopN);
+
+ for ithSeq = 1:nSequences
+
+ % seq info
+ ithSeqName = SequenceNames{ithSeq};
+ ithSeqPath = strcat(ResultsDir, ithSeqName, '/');
+ ithSeqPRcurveData = dir(ithSeqPath); ithSeqPRcurveData(1:2, :) = []; ithSeqPRcurveData = {ithSeqPRcurveData(:).name};
+
+ % load
+ nCorrectRejectionsAll = load(strcat(ithSeqPath, ithSeqPRcurveData{1}));
+ nCorrectRejectionsAll = nCorrectRejectionsAll.nCorrectRejections;
+ nCorrectRejectionsForThisTopN = nCorrectRejectionsAll(TopNidx, :);
+
+ nFalseAlarmsAll = load(strcat(ithSeqPath, ithSeqPRcurveData{2}));
+ nFalseAlarmsAll = nFalseAlarmsAll.nFalseAlarms;
+ nFalseAlarmsForThisTopN = nFalseAlarmsAll(TopNidx, :);
+
+ nHitsAll = load(strcat(ithSeqPath, ithSeqPRcurveData{3}));
+ nHitsAll = nHitsAll.nHits;
+ nHitsForThisTopN = nHitsAll(TopNidx, :);
+
+ nMissesAll = load(strcat(ithSeqPath, ithSeqPRcurveData{4}));
+ nMissesAll = nMissesAll.nMisses;
+ nMissesForThisTopN = nMissesAll(TopNidx, :);
+
+ % info
+ nTopNs = size(nCorrectRejectionsAll, 1);
+ nThres = size(nCorrectRejectionsAll, 2);
+
+ % main
+ Precisions = [];
+ Recalls = [];
+ Accuracies = [];
+ for ithThres = 1:nThres
+ nCorrectRejections = nCorrectRejectionsForThisTopN(ithThres);
+ nFalseAlarms = nFalseAlarmsForThisTopN(ithThres);
+ nHits = nHitsForThisTopN(ithThres);
+ nMisses = nMissesForThisTopN(ithThres);
+
+ nTotalTestPlaces = nCorrectRejections + nFalseAlarms + nHits + nMisses;
+
+ Precision = nHits / (nHits + nFalseAlarms);
+ Recall = nHits / (nHits + nMisses);
+ Acc = (nHits + nCorrectRejections)/nTotalTestPlaces;
+
+ Precisions = [Precisions; Precision];
+ Recalls = [Recalls; Recall];
+ Accuracies = [Accuracies; Acc];
+ end
+
+ % draw
+ figure(FigIdx);
+ plot(Recalls, Precisions, 'LineWidth', 2); % commonly x axis is recall
+ title('SCI at NCLT');
+ xlabel('Recall'); ylabel('Precision');
+ % axis equal;
+ xlim([0, 1]); ylim([0,1]);
+ grid on; grid minor;
+ hold on;
+
+ end
+
+ lgd = legend(SequenceNames, 'Location', 'best');
+ lgd.FontSize = 9;
+ lgd.FontWeight = 'bold';
+
+
+ %% save
+ % fileName = strcat('./results/', testDate, '_PRcurveWithEntropyThresVarying.png');
+ % saveas(gcf, fileName)
+ %
+ % fileName = strcat('./results/', testDate, '_EntireWorkSpace.mat');
+ % save(fileName)
+
+end
+
+
diff --git a/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/PRcurve/makeDataForPRcurveForSCIresult.m b/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/PRcurve/makeDataForPRcurveForSCIresult.m
new file mode 100644
index 0000000..b8e624d
--- /dev/null
+++ b/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/PRcurve/makeDataForPRcurveForSCIresult.m
@@ -0,0 +1,142 @@
+clear
+addpath(genpath('./'));
+
+%% Setup
+Dataset = 'NCLT';
+Method = 'LearningSCI';
+ResultDir = strcat('/media/gskim/IRAP-ADV1/Data/ICRA2019/Experiments/#. trunk/', Dataset, '/', Method, '/10m/results_predictionvectors/base0/');
+
+NCLTTestDateNames = {'2012-02-04', '2012-03-17', '2012-05-26', '2012-06-15', '2012-08-20', '2012-09-28', '2012-10-28', '2012-11-16', '2013-02-23', '2013-04-05'};
+nNCLTTestDates = length(NCLTTestDateNames);
+
+
+%% Main
+for ithDate = 1:nNCLTTestDates
+
+ TestDateName = NCLTTestDateNames{ithDate};
+
+ % path
+ SeenGTs_Path = strcat(ResultDir, TestDateName, '_seen_GT.npy');
+ SeenPredictions_Path = strcat(ResultDir, TestDateName, '_seen_predicted.npy');
+
+ UnseenGTs_Path = strcat(ResultDir, TestDateName, '_unseen_GT.npy');
+ UnseenPredictions_Path = strcat(ResultDir, TestDateName, '_unseen_predicted.npy');
+
+ % load
+ SeenGTs = double(readNPY(SeenGTs_Path));
+ SeenPredictions = double(readNPY(SeenPredictions_Path));
+ UnseenGTs = double(readNPY(UnseenGTs_Path));
+ UnseenPredictions = double(readNPY(UnseenPredictions_Path));
+
+ % info
+ nSeenPlaces = size(SeenGTs, 1);
+ nUnseenPlaces = size(UnseenGTs, 1);
+
+ % concate seen and unseen for convenience
+% TotalGTs = [SeenGTs; UnseenGTs];
+ TotalPredictions = [SeenPredictions; UnseenPredictions];
+ TotalSeenFlags = [ ones(nSeenPlaces, 1); zeros(nUnseenPlaces, 1)]; % seen (1) or unseen (0)
+ nTotalTestPlaces = nSeenPlaces + nUnseenPlaces;
+ nTestData = nTotalTestPlaces;
+
+
+ %% Main
+
+ % policy (top N)
+ TopN = 25;
+ TopNs = linspace(1, TopN, TopN);
+ nTopNs = length(TopNs);
+
+ % Entropy thresholds
+ Thresholds = linspace(0, 1, 200);
+ nThresholds = length(Thresholds);
+
+ % Main variables to store the result for drawing PR curve
+ nHits = zeros(nTopNs, nThresholds);
+ nFalseAlarms = zeros(nTopNs, nThresholds);
+ nCorrectRejections = zeros(nTopNs, nThresholds);
+ nMisses = zeros(nTopNs, nThresholds);
+
+ for ith=1:nTestData
+ tic
+
+ % Flag: Seen(1) or Unseen(0)
+ SeenOrUnseen = TotalSeenFlags(ith);
+
+ % GT place idx (in this code, use it only for the seen case. thus knowing unseen place index is unnecessary in this case for PR curve analysis
+ if(SeenOrUnseen == 1)
+ [dummy, ithTruthPlaceIdx] = max(SeenGTs(ith, :)); % but this idx is not the real place index, plz refer the scikitlearn label maker
+ else
+ ithTruthPlaceIdx = nan;
+ end
+
+ % ith prediction vector
+ ithPrediction = TotalPredictions(ith, :);
+
+ % entropy
+ EntropyOfPrediction = NormalizedEntropyOfVector(ithPrediction); % Entropy for module 1 (new place detection)
+
+ % top N predictions
+ [NearestSoftmaxOutputs, idxs] = sort(ithPrediction, 'descend');
+ NearestPlaceIdxs = idxs(1:TopN); % as mentioned above: this idx is not the real place index, plz refer the scikitlearn label maker
+
+
+ for ithTopN = 1:nTopNs
+
+ ithNearestPlaceIdxs = NearestPlaceIdxs(1:ithTopN);
+
+ for ithThres = 1:nThresholds
+
+ ithDistThreshold = Thresholds(ithThres);
+
+ % main
+ if(EntropyOfPrediction >= ithDistThreshold)
+ % if over the theshold, it is considered unseen.
+ % for unseen considered, no step 2 (recognition of place index), and just quit now.
+ if(SeenOrUnseen == 0)
+ % TN: Correct Rejection
+ nCorrectRejections(ithTopN, ithThres) = nCorrectRejections(ithTopN, ithThres) + 1;
+ else
+ % FN: MISS
+ nMisses(ithTopN, ithThres) = nMisses(ithTopN, ithThres) + 1;
+ end
+ else
+ % if under the theshold, it is considered seen.
+ % and then check the correctness
+ if( ismember(ithTruthPlaceIdx, ithNearestPlaceIdxs) )
+ % TP: Hit
+ nHits(ithTopN, ithThres) = nHits(ithTopN, ithThres) + 1;
+ else
+ % FP: False Alarm
+ nFalseAlarms(ithTopN, ithThres) = nFalseAlarms(ithTopN, ithThres) + 1;
+ end
+ end
+
+ end
+
+ end
+
+ message = strcat(num2str(ith), "/", num2str(nTestData), " of ", TestDateName);
+ disp(message);
+ toc
+
+ end
+
+
+ % save the results (PR curve is for the later.)
+ savePath = strcat('Result/', TestDateName, '/');
+ if(~(7==exist(savePath,'dir')))
+ mkdir(savePath);
+ end
+ save(strcat(savePath, 'nCorrectRejections.mat'), 'nCorrectRejections');
+ save(strcat(savePath, 'nMisses.mat'), 'nMisses');
+ save(strcat(savePath, 'nHits.mat'), 'nHits');
+ save(strcat(savePath, 'nFalseAlarms.mat'), 'nFalseAlarms');
+
+end
+
+
+
+
+
+
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diff --git a/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/PRcurve/src/EntropyOfVector.m b/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/PRcurve/src/EntropyOfVector.m
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--- /dev/null
+++ b/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/PRcurve/src/EntropyOfVector.m
@@ -0,0 +1,16 @@
+function H = EntropyOfVector(vec)
+
+len = length(vec);
+
+H = 0;
+for ith=1:len
+ pith = vec(ith);
+ if(pith == 0)
+ H = H + 0; % 0log0 = 0
+ else
+ H = H + ( -1 * (pith*log2(pith)) );
+ end
+end
+
+end
+
diff --git a/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/PRcurve/src/NormalizedEntropyOfVector.m b/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/PRcurve/src/NormalizedEntropyOfVector.m
new file mode 100644
index 0000000..f1d0bfa
--- /dev/null
+++ b/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/PRcurve/src/NormalizedEntropyOfVector.m
@@ -0,0 +1,22 @@
+function NormalizedH = NormalizedEntropyOfVector(vec)
+
+len = length(vec);
+
+% max entropy
+maxEntropy = -len*(1/len * log2(1/len));
+
+H = 0;
+for ith=1:len
+ pith = vec(ith);
+ if(pith == 0)
+ H = H + 0; % 0log0 = 0
+ else
+ H = H + ( -1 * (pith*log2(pith)) );
+ end
+end
+
+% return
+NormalizedH = H/maxEntropy;
+
+end
+
diff --git a/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/inference.ipynb b/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/inference.ipynb
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index 0000000..bbacf87
--- /dev/null
+++ b/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/inference.ipynb
@@ -0,0 +1,303 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import numpy as np \n",
+ "import pandas as pd \n",
+ "import tensorflow as tf\n",
+ "import keras\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Data info \n",
+ "rootDir = '..your_path'\n",
+ "\n",
+ "Dataset = 'NCLT'\n",
+ "TrainOrTest = '/Test/'\n",
+ "SequenceDate = '2013-04-05'\n",
+ "\n",
+ "SCImiddlePath = '/4. SCI_jet0to15/'\n",
+ "\n",
+ "GridCellSize = '10'\n",
+ "\n",
+ "DataPath = rootDir + Dataset + TrainOrTest + SequenceDate + SCImiddlePath + GridCellSize + '/'\n",
+ "print(DataPath)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "DataPath = ICRArootDir + Dataset + TrainOrTest + SequenceDate + SCImiddlePath + GridCellSize + '/'\n",
+ "print(DataPath)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def getTestDataNCLT(DataPath, SequenceDate, lbl_enc_train): \n",
+ "\n",
+ " # load training label encoding information \n",
+ " from sklearn.preprocessing import LabelEncoder\n",
+ " ClassesTrainingSequenceHave = lbl_enc_train.classes_\n",
+ " nClassesTrainingSequenceHave = len(ClassesTrainingSequenceHave)\n",
+ "\n",
+ " # info\n",
+ " WholeData = os.listdir(DataPath)\n",
+ " nWholeData = len(WholeData)\n",
+ " print(str(nWholeData) + ' data exist in ' + SequenceDate)\n",
+ " \n",
+ " # read \n",
+ " X_seen = []\n",
+ " y_seen = []\n",
+ " X_unseen = []\n",
+ " y_unseen = []\n",
+ " \n",
+ " for ii in range(nWholeData):\n",
+ " dataName = WholeData[ii]\n",
+ " dataPath = DataPath + dataName\n",
+ " \n",
+ " dataTrajNodeOrder = int(dataName[0:5])\n",
+ "\n",
+ " SCI = plt.imread(dataPath)\n",
+ " dataPlaceIndex = int(dataName[6:11])\n",
+ " \n",
+ " # if label is in the train, then save into the seen (seen is only subset to be tested)\n",
+ " if dataPlaceIndex in ClassesTrainingSequenceHave:\n",
+ " X_seen.append(SCI)\n",
+ " y_seen.append(dataPlaceIndex)\n",
+ " else:\n",
+ " X_unseen.append(SCI)\n",
+ " y_unseen.append(dataPlaceIndex)\n",
+ " \n",
+ " # progress message \n",
+ " if ii%1000==0:\n",
+ " print(str(format((ii/nWholeData)*100, '.1f')), '% loaded.')\n",
+ " \n",
+ " dataShape = SCI.shape\n",
+ " \n",
+ " # X\n",
+ " nSeenData = len(X_seen)\n",
+ " X_nd = np.zeros(shape=(nSeenData, dataShape[0], dataShape[1], dataShape[2]))\n",
+ " for jj in range(nSeenData):\n",
+ " X_nd[jj, :, :] = X_seen[jj]\n",
+ " X_nd = X_nd.astype('float32')\n",
+ " \n",
+ " # y (one-hot encoded) \n",
+ " y_seen = lbl_enc_train.transform(y_seen)\n",
+ " y_nd = keras.utils.np_utils.to_categorical(y_seen, num_classes=nClassesTrainingSequenceHave)\n",
+ "\n",
+ " # log message \n",
+ " print('Data size: %s' % nWholeData)\n",
+ " print('- Seen data: %s' % len(X_seen))\n",
+ " print('- Uneen data: %s' % len(X_unseen))\n",
+ " print(' ')\n",
+ " print('Data shape:', X_nd.shape)\n",
+ " print('Label shape:', y_nd.shape)\n",
+ " \n",
+ " return X_nd, y_nd, X_unseen, y_unseen\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load training label encoding information for discriminate seen/unseen of test \n",
+ "import pickle\n",
+ "TrainingDate = '2012-01-15'\n",
+ "TrainingDataPath = 'data_pickle/Train_' + TrainingDate + '_SCI_color.pkl'\n",
+ "\n",
+ "with open(TrainingDataPath, 'rb') as f: # Python 3: open(..., 'rb')\n",
+ " X_train, y_train, lbl_enc_train = pickle.load(f)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load test data using training label encoder information \n",
+ "[X_seen, y_seen, X_unseen, y_unseen] = getTestDataNCLT(DataPath, SequenceDate, lbl_enc_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/gskim/anaconda3/envs/tfkeras/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+ " from ._conv import register_converters as _register_converters\n",
+ "Using TensorFlow backend.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "input_1 (InputLayer) (None, 40, 120, 3) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_1 (Conv2D) (None, 40, 120, 64) 4864 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_1 (MaxPooling2 (None, 20, 60, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_1 (Batch (None, 20, 60, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "conv2d_2 (Conv2D) (None, 20, 60, 128) 204928 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_2 (MaxPooling2 (None, 10, 30, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_2 (Batch (None, 10, 30, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "conv2d_3 (Conv2D) (None, 10, 30, 256) 819456 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_3 (MaxPooling2 (None, 5, 15, 256) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten_1 (Flatten) (None, 19200) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_1 (Dropout) (None, 19200) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_1 (Dense) (None, 64) 1228864 \n",
+ "_________________________________________________________________\n",
+ "dropout_2 (Dropout) (None, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_2 (Dense) (None, 579) 37635 \n",
+ "=================================================================\n",
+ "Total params: 2,296,515\n",
+ "Trainable params: 2,296,131\n",
+ "Non-trainable params: 384\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "from keras.models import load_model\n",
+ "modelName = 'pre_trained_model/base0.h5'\n",
+ "testModel = load_model(modelName)\n",
+ "\n",
+ "testModel.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "5170/5170 [==============================] - 1s 182us/step\n",
+ "2012-02-04\n",
+ "- Test score: 1.008079155962518\n",
+ "- Test accuracy: 82.76595741913904\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load Trained net \n",
+ "from keras.models import load_model\n",
+ "modelName = 'model/base0.h5'\n",
+ "testModel = load_model(modelName)\n",
+ "\n",
+ "# Predict \n",
+ "scores_TEST = testModel.evaluate(X_seen, y_seen, verbose=1, batch_size=1000)\n",
+ "print(SequenceDate)\n",
+ "print('- Test score:', scores_TEST[0])\n",
+ "print('- Test accuracy:', scores_TEST[1]*100)\n",
+ "print(' ')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# save prediction (for later top N analysis )\n",
+ "y_seen_predicted = testModel.predict(X_seen)\n",
+ "\n",
+ "# save \n",
+ "filename_y_seen_predicted = 'results_predictionvectors/base0/' + SequenceDate + '_seen_predicted'\n",
+ "np.save(filename_y_seen_predicted, y_seen_predicted)\n",
+ "\n",
+ "# save prediction (for later top N analysis )\n",
+ "X_unseen = np.array(X_unseen)\n",
+ "y_unseen_predicted = testModel.predict(X_unseen)\n",
+ "\n",
+ "# save \n",
+ "filename_y_unseen_predicted = 'results_predictionvectors/base0/' + SequenceDate + '_unseen_predicted'\n",
+ "np.save(filename_y_unseen_predicted, y_unseen_predicted)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "### save GT also \n",
+ "\n",
+ "# seen \n",
+ "filename_y_seen_GT = 'results_predictionvectors/base0/' + SequenceDate + '_seen_GT'\n",
+ "np.save(filename_y_seen_GT, y_seen)\n",
+ "\n",
+ "# unseen\n",
+ "filename_y_unseen_GT = 'results_predictionvectors/base0/' + SequenceDate + '_unseen_GT'\n",
+ "np.save(filename_y_unseen_GT, y_unseen)\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.5.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/pre_trained_model/base0.h5 b/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/pre_trained_model/base0.h5
new file mode 100644
index 0000000..6669c79
Binary files /dev/null and b/src/example/longterm_localization/NCLT/2012-01-15/3_Inference/pre_trained_model/base0.h5 differ
diff --git a/src/example/longterm_localization/sci_pipeline.png b/src/example/longterm_localization/sci_pipeline.png
new file mode 100644
index 0000000..9950598
Binary files /dev/null and b/src/example/longterm_localization/sci_pipeline.png differ
diff --git a/src/example/place_recognition/KITTI/00/DistBtn2Dpose.m b/src/example/place_recognition/KITTI/00/DistBtn2Dpose.m
new file mode 100644
index 0000000..aecb875
--- /dev/null
+++ b/src/example/place_recognition/KITTI/00/DistBtn2Dpose.m
@@ -0,0 +1,3 @@
+function dist = DistBtn2Dpose(pose1, pose2)
+ dist = sqrt( (pose1(1) - pose2(1))^2 + (pose1(2) - pose2(2))^2);
+end
diff --git a/src/example/place_recognition/KITTI/00/GTposes.mat b/src/example/place_recognition/KITTI/00/GTposes.mat
new file mode 100644
index 0000000..64b3948
Binary files /dev/null and b/src/example/place_recognition/KITTI/00/GTposes.mat differ
diff --git a/src/example/place_recognition/KITTI/00/KITTIbin2PtcloudWithIndex.m b/src/example/place_recognition/KITTI/00/KITTIbin2PtcloudWithIndex.m
new file mode 100644
index 0000000..5a683d5
--- /dev/null
+++ b/src/example/place_recognition/KITTI/00/KITTIbin2PtcloudWithIndex.m
@@ -0,0 +1,21 @@
+function ptcloud = KITTIbin2PtcloudWithIndex(base_dir, index)
+
+%% File path
+if( length(num2str(index)) == 4 )
+ bin_path = strcat(base_dir, '00', num2str(index), '.bin');
+elseif (length(num2str(index)) == 3)
+ bin_path = strcat(base_dir, '000', num2str(index), '.bin');
+elseif (length(num2str(index)) == 2)
+ bin_path = strcat(base_dir, '0000', num2str(index), '.bin');
+elseif (length(num2str(index)) == 1)
+ bin_path = strcat(base_dir, '00000', num2str(index), '.bin');
+end
+
+%% Read
+fid = fopen(bin_path, 'rb'); raw_data = fread(fid, [4 inf], 'single'); fclose(fid);
+points = raw_data(1:3,:)';
+points(:, 3) = points(:, 3) + 1.9; % z in car coord.
+
+ptcloud = pointCloud(points);
+
+end % end of function
diff --git a/src/example/place_recognition/KITTI/00/PlaceRecognizer.m b/src/example/place_recognition/KITTI/00/PlaceRecognizer.m
new file mode 100644
index 0000000..ee8cae1
--- /dev/null
+++ b/src/example/place_recognition/KITTI/00/PlaceRecognizer.m
@@ -0,0 +1,105 @@
+clear; clc;
+addpath(genpath('../../../../matlab/'));
+
+%% Change this to your path
+% vel_dir = '/media/gskim/Data/KITTI odo/data_odometry_velodyne/dataset/sequences/00/velodyne/';
+vel_dir = 'your_pointcloud_files_path';
+
+
+%% Params
+% below 3 parameters: same setting as the original paper (G. Kim, 18 IROS)
+max_range = 80; % meter
+num_sectors = 60;
+num_rings = 20;
+
+num_candidates = 50; % means Scan Context-50 in the paper.
+
+loop_thres = 0.2; % ### this is a user parameter ###
+
+num_enough_node_diff = 50;
+
+
+%% Main
+load('GTposes.mat');
+
+ringkeys = [];
+scancontexts = {};
+
+loop_log = [];
+num_nodes = length(GTposes);
+for ith_node = 1:num_nodes-1
+
+ % information
+ idx_query = ith_node;
+ ptcloud = KITTIbin2PtcloudWithIndex(vel_dir, idx_query);
+
+ sc_query = Ptcloud2ScanContext(ptcloud, num_sectors, num_rings, max_range);
+ scancontexts{end+1} = sc_query; % save into database
+
+ % ringkey tree
+ ringkey = ScanContext2RingKey(sc_query);
+ ringkeys = [ringkeys; ringkey];
+ tree = createns(ringkeys, 'NSMethod', 'kdtree'); % Create object to use in k-nearest neighbor search
+
+ % try loop-detection after enough moving
+ if(ith_node < num_candidates)
+ continue;
+ end
+
+ % find nearest candidates
+ candidates = knnsearch(tree, ringkey, 'K', num_candidates);
+
+ % check dist btn candidates is lower than the given threshold.
+ idx_nearest = 0;
+ min_dist = inf; % initialization
+ for ith_candidate = 1:length(candidates)
+
+ idx_candidate = candidates(ith_candidate);
+ sc_candidate = scancontexts{idx_candidate};
+
+ % skip condition: do not check nearest measurements
+ if( abs(idx_query - idx_candidate) < num_enough_node_diff)
+ continue;
+ end
+
+ % Main
+ distance_to_query = DistanceBtnScanContexts(sc_query, sc_candidate);
+ if( distance_to_query > loop_thres)
+ continue;
+ end
+
+ if( distance_to_query < min_dist)
+ idx_nearest = idx_candidate;
+ min_dist = distance_to_query;
+ end
+
+ end
+
+ % log the result
+ if( ~isequal(min_dist, inf)) % that is, when any loop (satisfied under acceptance theshold) occured.
+ pose_dist_real = DistBtn2Dpose(GTposes(idx_query,:), GTposes(idx_nearest,:));
+ loop_log = [loop_log; ...
+ idx_query, idx_nearest, min_dist, pose_dist_real];
+ end
+
+ %% Log Message: procedure and LoopFound Event
+ if( rem(idx_query, 100) == 0)
+ disp( strcat(num2str(idx_query), '.bin processed') );
+ end
+ if( ~isequal(min_dist, inf) )
+ disp( strcat("Loop found: ", num2str(idx_query), " <-> ", num2str(idx_nearest), " with dist ", num2str(min_dist)));
+ end
+
+end
+
+
+%% Save the result into csv file
+save_dir = strcat('./result/', num2str(num_candidates),'/', num2str(loop_thres), '/');
+save_file_name = strcat(save_dir, '/LogLoopFound.csv');
+if( ~exist(save_dir))
+ mkdir(save_dir)
+end
+csvwrite(save_file_name, loop_log);
+
+
+
diff --git a/src/example/place_recognition/KITTI/00/result/50/0.2/LogLoopFound.csv b/src/example/place_recognition/KITTI/00/result/50/0.2/LogLoopFound.csv
new file mode 100644
index 0000000..b611beb
--- /dev/null
+++ b/src/example/place_recognition/KITTI/00/result/50/0.2/LogLoopFound.csv
@@ -0,0 +1,757 @@
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diff --git a/src/fast_evaluator/00.csv b/src/fast_evaluator/00.csv
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diff --git a/src/fast_evaluator/linspecer.m b/src/fast_evaluator/linspecer.m
new file mode 100644
index 0000000..31c5e6e
--- /dev/null
+++ b/src/fast_evaluator/linspecer.m
@@ -0,0 +1,261 @@
+% function lineStyles = linspecer(N)
+% This function creates an Nx3 array of N [R B G] colors
+% These can be used to plot lots of lines with distinguishable and nice
+% looking colors.
+%
+% lineStyles = linspecer(N); makes N colors for you to use: lineStyles(ii,:)
+%
+% colormap(linspecer); set your colormap to have easily distinguishable
+% colors and a pleasing aesthetic
+%
+% lineStyles = linspecer(N,'qualitative'); forces the colors to all be distinguishable (up to 12)
+% lineStyles = linspecer(N,'sequential'); forces the colors to vary along a spectrum
+%
+% % Examples demonstrating the colors.
+%
+% LINE COLORS
+% N=6;
+% X = linspace(0,pi*3,1000);
+% Y = bsxfun(@(x,n)sin(x+2*n*pi/N), X.', 1:N);
+% C = linspecer(N);
+% axes('NextPlot','replacechildren', 'ColorOrder',C);
+% plot(X,Y,'linewidth',5)
+% ylim([-1.1 1.1]);
+%
+% SIMPLER LINE COLOR EXAMPLE
+% N = 6; X = linspace(0,pi*3,1000);
+% C = linspecer(N)
+% hold off;
+% for ii=1:N
+% Y = sin(X+2*ii*pi/N);
+% plot(X,Y,'color',C(ii,:),'linewidth',3);
+% hold on;
+% end
+%
+% COLORMAP EXAMPLE
+% A = rand(15);
+% figure; imagesc(A); % default colormap
+% figure; imagesc(A); colormap(linspecer); % linspecer colormap
+%
+% See also NDHIST, NHIST, PLOT, COLORMAP, 43700-cubehelix-colormaps
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% by Jonathan Lansey, March 2009-2013 Lansey at gmail.com %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+%% credits and where the function came from
+% The colors are largely taken from:
+% http://colorbrewer2.org and Cynthia Brewer, Mark Harrower and The Pennsylvania State University
+%
+%
+% She studied this from a phsychometric perspective and crafted the colors
+% beautifully.
+%
+% I made choices from the many there to decide the nicest once for plotting
+% lines in Matlab. I also made a small change to one of the colors I
+% thought was a bit too bright. In addition some interpolation is going on
+% for the sequential line styles.
+%
+%
+%%
+
+function lineStyles=linspecer(N,varargin)
+
+if nargin==0 % return a colormap
+ lineStyles = linspecer(128);
+ return;
+end
+
+if ischar(N)
+ lineStyles = linspecer(128,N);
+ return;
+end
+
+if N<=0 % its empty, nothing else to do here
+ lineStyles=[];
+ return;
+end
+
+% interperet varagin
+qualFlag = 0;
+colorblindFlag = 0;
+
+if ~isempty(varargin)>0 % you set a parameter?
+ switch lower(varargin{1})
+ case {'qualitative','qua'}
+ if N>12 % go home, you just can't get this.
+ warning('qualitiative is not possible for greater than 12 items, please reconsider');
+ else
+ if N>9
+ warning(['Default may be nicer for ' num2str(N) ' for clearer colors use: whitebg(''black''); ']);
+ end
+ end
+ qualFlag = 1;
+ case {'sequential','seq'}
+ lineStyles = colorm(N);
+ return;
+ case {'white','whitefade'}
+ lineStyles = whiteFade(N);return;
+ case 'red'
+ lineStyles = whiteFade(N,'red');return;
+ case 'blue'
+ lineStyles = whiteFade(N,'blue');return;
+ case 'green'
+ lineStyles = whiteFade(N,'green');return;
+ case {'gray','grey'}
+ lineStyles = whiteFade(N,'gray');return;
+ case {'colorblind'}
+ colorblindFlag = 1;
+ otherwise
+ warning(['parameter ''' varargin{1} ''' not recognized']);
+ end
+end
+% *.95
+% predefine some colormaps
+ set3 = colorBrew2mat({[141, 211, 199];[ 255, 237, 111];[ 190, 186, 218];[ 251, 128, 114];[ 128, 177, 211];[ 253, 180, 98];[ 179, 222, 105];[ 188, 128, 189];[ 217, 217, 217];[ 204, 235, 197];[ 252, 205, 229];[ 255, 255, 179]}');
+set1JL = brighten(colorBrew2mat({[228, 26, 28];[ 55, 126, 184]; [ 77, 175, 74];[ 255, 127, 0];[ 255, 237, 111]*.85;[ 166, 86, 40];[ 247, 129, 191];[ 153, 153, 153];[ 152, 78, 163]}'));
+set1 = brighten(colorBrew2mat({[ 55, 126, 184]*.85;[228, 26, 28];[ 77, 175, 74];[ 255, 127, 0];[ 152, 78, 163]}),.8);
+
+% colorblindSet = {[215,25,28];[253,174,97];[171,217,233];[44,123,182]};
+colorblindSet = {[215,25,28];[253,174,97];[171,217,233]*.8;[44,123,182]*.8};
+
+set3 = dim(set3,.93);
+
+if colorblindFlag
+ switch N
+ % sorry about this line folks. kind of legacy here because I used to
+ % use individual 1x3 cells instead of nx3 arrays
+ case 4
+ lineStyles = colorBrew2mat(colorblindSet);
+ otherwise
+ colorblindFlag = false;
+ warning('sorry unsupported colorblind set for this number, using regular types');
+ end
+end
+if ~colorblindFlag
+ switch N
+ case 1
+ lineStyles = { [ 55, 126, 184]/255};
+ case {2, 3, 4, 5 }
+ lineStyles = set1(1:N);
+ case {6 , 7, 8, 9}
+ lineStyles = set1JL(1:N)';
+ case {10, 11, 12}
+ if qualFlag % force qualitative graphs
+ lineStyles = set3(1:N)';
+ else % 10 is a good number to start with the sequential ones.
+ lineStyles = cmap2linspecer(colorm(N));
+ end
+ otherwise % any old case where I need a quick job done.
+ lineStyles = cmap2linspecer(colorm(N));
+ end
+end
+lineStyles = cell2mat(lineStyles);
+
+end
+
+% extra functions
+function varIn = colorBrew2mat(varIn)
+for ii=1:length(varIn) % just divide by 255
+ varIn{ii}=varIn{ii}/255;
+end
+end
+
+function varIn = brighten(varIn,varargin) % increase the brightness
+
+if isempty(varargin),
+ frac = .9;
+else
+ frac = varargin{1};
+end
+
+for ii=1:length(varIn)
+ varIn{ii}=varIn{ii}*frac+(1-frac);
+end
+end
+
+function varIn = dim(varIn,f)
+ for ii=1:length(varIn)
+ varIn{ii} = f*varIn{ii};
+ end
+end
+
+function vOut = cmap2linspecer(vIn) % changes the format from a double array to a cell array with the right format
+vOut = cell(size(vIn,1),1);
+for ii=1:size(vIn,1)
+ vOut{ii} = vIn(ii,:);
+end
+end
+%%
+% colorm returns a colormap which is really good for creating informative
+% heatmap style figures.
+% No particular color stands out and it doesn't do too badly for colorblind people either.
+% It works by interpolating the data from the
+% 'spectral' setting on http://colorbrewer2.org/ set to 11 colors
+% It is modified a little to make the brightest yellow a little less bright.
+function cmap = colorm(varargin)
+n = 100;
+if ~isempty(varargin)
+ n = varargin{1};
+end
+
+if n==1
+ cmap = [0.2005 0.5593 0.7380];
+ return;
+end
+if n==2
+ cmap = [0.2005 0.5593 0.7380;
+ 0.9684 0.4799 0.2723];
+ return;
+end
+
+frac=.95; % Slight modification from colorbrewer here to make the yellows in the center just a bit darker
+cmapp = [158, 1, 66; 213, 62, 79; 244, 109, 67; 253, 174, 97; 254, 224, 139; 255*frac, 255*frac, 191*frac; 230, 245, 152; 171, 221, 164; 102, 194, 165; 50, 136, 189; 94, 79, 162];
+x = linspace(1,n,size(cmapp,1));
+xi = 1:n;
+cmap = zeros(n,3);
+for ii=1:3
+ cmap(:,ii) = pchip(x,cmapp(:,ii),xi);
+end
+cmap = flipud(cmap/255);
+end
+
+function cmap = whiteFade(varargin)
+n = 100;
+if nargin>0
+ n = varargin{1};
+end
+
+thisColor = 'blue';
+
+if nargin>1
+ thisColor = varargin{2};
+end
+switch thisColor
+ case {'gray','grey'}
+ cmapp = [255,255,255;240,240,240;217,217,217;189,189,189;150,150,150;115,115,115;82,82,82;37,37,37;0,0,0];
+ case 'green'
+ cmapp = [247,252,245;229,245,224;199,233,192;161,217,155;116,196,118;65,171,93;35,139,69;0,109,44;0,68,27];
+ case 'blue'
+ cmapp = [247,251,255;222,235,247;198,219,239;158,202,225;107,174,214;66,146,198;33,113,181;8,81,156;8,48,107];
+ case 'red'
+ cmapp = [255,245,240;254,224,210;252,187,161;252,146,114;251,106,74;239,59,44;203,24,29;165,15,21;103,0,13];
+ otherwise
+ warning(['sorry your color argument ' thisColor ' was not recognized']);
+end
+
+cmap = interpomap(n,cmapp);
+end
+
+% Eat a approximate colormap, then interpolate the rest of it up.
+function cmap = interpomap(n,cmapp)
+ x = linspace(1,n,size(cmapp,1));
+ xi = 1:n;
+ cmap = zeros(n,3);
+ for ii=1:3
+ cmap(:,ii) = pchip(x,cmapp(:,ii),xi);
+ end
+ cmap = (cmap/255); % flipud??
+end
+
+
+
diff --git a/src/fast_evaluator/main.m b/src/fast_evaluator/main.m
new file mode 100644
index 0000000..0b3649a
--- /dev/null
+++ b/src/fast_evaluator/main.m
@@ -0,0 +1,154 @@
+clear; clc;
+
+addpath(genpath('src'));
+addpath(genpath('data'));
+
+%% data preparation
+global data_path;
+% your directory should contain files like this
+% - 00
+% l- 00.csv (gt pose)
+% l- velodyne
+% l- <0xxxx.bin>
+data_path = '/media/gskim/Data/KITTI odo/data_odometry_velodyne/dataset/sequences/00/';
+
+
+down_shape = [40, 120];
+skip_data_frame = 1;
+[data_scancontexts, data_ringkeys, data_poses] = loadData(down_shape, skip_data_frame);
+
+figure(101); clf;
+plot(data_poses(:,1), data_poses(:,2));
+axis equal;
+
+%% main - global recognizer
+revisit_criteria = 5; % in meter (recommend test for 5, 10, 20 meters)
+keyframe_gap = 1; % for_fast_eval (if 1, no skip)
+
+global num_candidates; num_candidates = 50;
+% global num_node_enough_apart; num_node_enough_apart = 50;
+
+% policy (top N)
+num_top_n = 25;
+top_n = linspace(1, num_top_n, num_top_n);
+
+% Entropy thresholds
+middle_thres = 0.01;
+thresholds1 = linspace(0, middle_thres, 50);
+thresholds2 = linspace(middle_thres, 1, 50);
+thresholds = [thresholds1, thresholds2];
+num_thresholds = length(thresholds);
+
+% Main variables to store the result for drawing PR curve
+num_hits = zeros(num_top_n, num_thresholds);
+num_false_alarms = zeros(num_top_n, num_thresholds);
+num_correct_rejections = zeros(num_top_n, num_thresholds);
+num_misses = zeros(num_top_n, num_thresholds);
+
+% main
+loop_log = [];
+
+exp_poses = [];
+exp_ringkeys = [];
+exp_scancontexts = {};
+
+num_queries = length(data_poses);
+for query_idx = 1:num_queries - 1
+
+ % save to (online) DB
+ query_sc = data_scancontexts{query_idx};
+ query_rk = data_ringkeys(query_idx, :);
+ query_pose = data_poses(query_idx,:);
+
+ exp_scancontexts{end+1} = query_sc;
+ exp_poses = [exp_poses; query_pose];
+ exp_ringkeys = [exp_ringkeys; query_rk];
+
+ if(rem(query_idx, keyframe_gap) ~= 0)
+ continue;
+ end
+
+ if( length(exp_scancontexts) < num_candidates )
+ continue;
+ end
+
+ tree = createns(exp_ringkeys(1:end-(num_candidates-1), :), 'NSMethod', 'kdtree'); % Create object to use in k-nearest neighbor search
+
+ % revisitness
+ [revisitness, how_far_apart] = isRevisitGlobalLoc(query_pose, exp_poses(1:end-(num_candidates-1), :), revisit_criteria);
+ disp([revisitness, how_far_apart])
+
+ % find candidates
+ candidates = knnsearch(tree, query_rk, 'K', num_candidates);
+
+ % find the nearest (top 1) via pairwise comparison
+ nearest_idx = 0;
+ min_dist = inf; % initialization
+ for ith_candidate = 1:length(candidates)
+ candidate_node_idx = candidates(ith_candidate);
+ candidate_img = exp_scancontexts{candidate_node_idx};
+
+ distance_to_query = sc_dist(query_sc, candidate_img);
+
+ if( distance_to_query < min_dist)
+ nearest_idx = candidate_node_idx;
+ min_dist = distance_to_query;
+ end
+ end
+
+ % prcurve analysis
+ for topk = 1:num_top_n
+ for thres_idx = 1:num_thresholds
+ threshold = thresholds(thres_idx);
+
+ reject = 0;
+ if( min_dist > threshold)
+ reject = 1;
+ end
+
+ if(reject == 1)
+ if(revisitness == 0)
+ % TN: Correct Rejection
+ num_correct_rejections(topk, thres_idx) = num_correct_rejections(topk, thres_idx) + 1;
+ else
+ % FN: MISS
+ num_misses(topk, thres_idx) = num_misses(topk, thres_idx) + 1;
+ end
+ else
+ % if under the theshold, it is considered seen.
+ % and then check the correctness
+ if( dist_btn_pose(query_pose, exp_poses(nearest_idx, :)) < revisit_criteria)
+ % TP: Hit
+ num_hits(topk, thres_idx) = num_hits(topk, thres_idx) + 1;
+ else
+ % FP: False Alarm
+ num_false_alarms(topk, thres_idx) = num_false_alarms(topk, thres_idx) + 1;
+ end
+ end
+
+ end
+ end
+
+ if( rem(query_idx, 100) == 0)
+ disp( strcat(num2str(query_idx/num_queries * 100), ' % processed') );
+ end
+
+end
+
+
+%% save the log
+savePath = strcat("pr_result/within ", num2str(revisit_criteria), "m/");
+if((~7==exist(savePath,'dir')))
+ mkdir(savePath);
+end
+save(strcat(savePath, 'nCorrectRejections.mat'), 'num_correct_rejections');
+save(strcat(savePath, 'nMisses.mat'), 'num_misses');
+save(strcat(savePath, 'nHits.mat'), 'num_hits');
+save(strcat(savePath, 'nFalseAlarms.mat'), 'num_false_alarms');
+
+
+
+
+
+
+
diff --git a/src/fast_evaluator/prcurve_drawer.m b/src/fast_evaluator/prcurve_drawer.m
new file mode 100644
index 0000000..ab4d8a2
--- /dev/null
+++ b/src/fast_evaluator/prcurve_drawer.m
@@ -0,0 +1,130 @@
+ResultsDir = './pr_result/';
+
+%%
+title_str = strcat('KITTI 00');
+
+%% Params
+FigIdx = 2;
+figure(FigIdx); clf;
+
+TopNindexes = [1];
+name = 'top1';
+
+nTopNindexes = length(TopNindexes);
+
+%% Main
+SequenceNames = dir(ResultsDir); SequenceNames(1:2, :) = []; SequenceNames = {SequenceNames(:).name};
+nSequences = length(SequenceNames);
+
+all_Precisions = {};
+all_Recalls = {};
+
+for ithTopN = 1:nTopNindexes
+
+ TopNidx = TopNindexes(ithTopN);
+
+ line_width = 4;
+
+ LineColors = colorcube(nSequences);
+ LineColors = linspecer(nSequences,'qualitative');
+% LineColors = linspecer(nSequences,'sequential');
+ LineColors = flipud(LineColors);
+
+ AUCs = zeros(1, nSequences);
+ for ithSeq = 1:nSequences
+
+ % seq info
+ ithSeqName = SequenceNames{ithSeq};
+ SequenceNames{ithSeq} = string(ithSeqName);
+
+ ithSeqPath = strcat(ResultsDir, ithSeqName, '/');
+ ithSeqPRcurveData = dir(ithSeqPath); ithSeqPRcurveData(1:2, :) = []; ithSeqPRcurveData = {ithSeqPRcurveData(:).name};
+
+ % load
+ nCorrectRejectionsAll = load(strcat(ithSeqPath, ithSeqPRcurveData{1}));
+ nCorrectRejectionsAll = nCorrectRejectionsAll.num_correct_rejections;
+ nCorrectRejectionsForThisTopN = nCorrectRejectionsAll(TopNidx, :);
+
+ nFalseAlarmsAll = load(strcat(ithSeqPath, ithSeqPRcurveData{2}));
+ nFalseAlarmsAll = nFalseAlarmsAll.num_false_alarms;
+ nFalseAlarmsForThisTopN = nFalseAlarmsAll(TopNidx, :);
+
+ nHitsAll = load(strcat(ithSeqPath, ithSeqPRcurveData{3}));
+ nHitsAll = nHitsAll.num_hits;
+ nHitsForThisTopN = nHitsAll(TopNidx, :);
+
+ nMissesAll = load(strcat(ithSeqPath, ithSeqPRcurveData{4}));
+ nMissesAll = nMissesAll.num_misses;
+ nMissesForThisTopN = nMissesAll(TopNidx, :);
+
+ % info
+ nTopNs = size(nCorrectRejectionsAll, 1);
+ nThres = size(nCorrectRejectionsAll, 2);
+
+ % main
+ Precisions = [];
+ Recalls = [];
+ Accuracies = [];
+ for ithThres = 1:nThres
+ nCorrectRejections = nCorrectRejectionsForThisTopN(ithThres);
+ nFalseAlarms = nFalseAlarmsForThisTopN(ithThres);
+ nHits = nHitsForThisTopN(ithThres);
+ nMisses = nMissesForThisTopN(ithThres);
+
+ nTotalTestPlaces = nCorrectRejections + nFalseAlarms + nHits + nMisses;
+
+ Precision = nHits / (nHits + nFalseAlarms);
+ Recall = nHits / (nHits + nMisses);
+ Acc = (nHits + nCorrectRejections)/nTotalTestPlaces;
+
+ Precisions = [Precisions; Precision];
+ Recalls = [Recalls; Recall];
+ Accuracies = [Accuracies; Acc];
+ end
+
+ num_points = length(Precisions);
+ Precisions(1) = 1;
+ AUC = 0;
+ for ith = 1:num_points-1
+ small_area = 1/2 * (Precisions(ith) + Precisions(ith+1)) * (Recalls(ith+1)-Recalls(ith));
+ AUC = AUC + small_area;
+ end
+ AUCs(ithSeq) = AUC;
+
+ all_Precisions{ithSeq} = Precisions;
+ all_Recalls{ithSeq} = Recalls;
+
+ % draw
+ figure(FigIdx);
+ set(gcf, 'Position', [10 10 800 500]);
+
+ fontsize = 10;
+ p = plot(Recalls, Precisions, 'LineWidth', line_width); % commonly x axis is recall
+ title(title_str, 'FontSize', fontsize);
+ xlabel('Recall', 'FontSize', fontsize); ylabel('Precision', 'FontSize', fontsize);
+ set(gca, 'FontSize', fontsize+5)
+ xticks([0 0.2 0.4 0.6 0.8 1.0])
+ xticklabels({'0','0.2','0.4','0.6','0.8','1'})
+ yticks([0 0.2 0.4 0.6 0.8 1.0])
+ yticklabels({'0','0.2','0.4','0.6','0.8','1'})
+
+ p(1).Color = LineColors(ithSeq, :);
+ p(1).MarkerEdgeColor = LineColors(ithSeq, :);
+ % axis equal;
+ xlim([0, 1]); ylim([0,1]);
+ grid on; grid minor;
+ hold on;
+
+ end
+
+ lgd = legend(SequenceNames, 'Location', 'best');
+ lgd.FontSize = fontsize + 3;
+ lgd.FontWeight = 'bold';
+
+ grid minor;
+
+ name = 'prcurve';
+ print('-bestfit', name,'-dpdf')
+
+end
+
diff --git a/src/fast_evaluator/src/Ptcloud2ScanContext.m b/src/fast_evaluator/src/Ptcloud2ScanContext.m
new file mode 100644
index 0000000..2ad3834
--- /dev/null
+++ b/src/fast_evaluator/src/Ptcloud2ScanContext.m
@@ -0,0 +1,102 @@
+function [ img ] = Ptcloud2ScanContext( ptcloud, num_sector, num_ring, max_range )
+
+%% Preprocessing
+
+% Downsampling for fast search
+gridStep = 0.5; % 0.5m cubic grid downsampling is applied in the paper.
+ptcloud = pcdownsample(ptcloud, 'gridAverage', gridStep);
+
+% point cloud information
+num_points = ptcloud.Count;
+gap = max_range / num_ring;
+angle_one_sector = 360/num_sector;
+
+
+%% vacant bins
+cell_bins = cell(num_ring, num_sector);
+cell_bin_counter = ones(num_ring, num_sector);
+
+enough_large = 500; % for fast and constant time save, We contain maximum 500 points per each bin.
+enough_small = -10000;
+for ith_ring = 1:num_ring
+ for ith_sector = 1:num_sector
+ bin = enough_small * ones(enough_large, 3);
+ cell_bins{ith_ring, ith_sector} = bin;
+ end
+end
+
+
+%% Save a point to the corresponding bin
+for ith_point =1:num_points
+
+ % Point information
+ ith_point_xyz = ptcloud.Location(ith_point,:);
+ ith_point_r = sqrt(ith_point_xyz(1)^2 + ith_point_xyz(2)^2);
+ ith_point_theta = XY2Theta(ith_point_xyz(1), ith_point_xyz(2)); % degree
+
+ % Find the corresponding ring index
+ tmp_ring_index = floor(ith_point_r/gap);
+ if(tmp_ring_index >= num_ring)
+ ring_index = num_ring;
+ else
+ ring_index = tmp_ring_index + 1;
+ end
+
+ % Find the corresponding sector index
+ tmp_sector_index = ceil(ith_point_theta/angle_one_sector);
+ if(tmp_sector_index == 0)
+ sector_index = 1;
+ elseif(tmp_sector_index > num_sector || tmp_sector_index < 1)
+ sector_index = num_sector;
+ else
+ sector_index = tmp_sector_index;
+ end
+
+ % Assign point to the corresponding bin cell
+ try
+ corresponding_counter = cell_bin_counter(ring_index, sector_index); % 1D real value.
+ catch
+ continue;
+ end
+ cell_bins{ring_index, sector_index}(corresponding_counter, :) = ith_point_xyz;
+ cell_bin_counter(ring_index, sector_index) = cell_bin_counter(ring_index, sector_index) + 1; % increase count 1
+
+end
+
+
+%% bin to image format (2D matrix)
+img = zeros(num_ring, num_sector);
+
+min_num_thres = 5; % a bin with few points, we consider it is noise.
+
+% Find maximum Z value of each bin and Save into img
+for ith_ring = 1:num_ring
+ for ith_sector = 1:num_sector
+ value_of_the_bin = 0;
+ points_in_bin_ij = cell_bins{ith_ring, ith_sector};
+
+ if( IsBinHaveMoreThanMinimumPoints(points_in_bin_ij, min_num_thres, enough_small) )
+ value_of_the_bin = max(points_in_bin_ij(:, 3));
+ else
+ value_of_the_bin = 0;
+ end
+
+ img(ith_ring, ith_sector) = value_of_the_bin;
+ end
+end
+
+
+end % end of the main function
+
+
+function bool = IsBinHaveMoreThanMinimumPoints(mat, minimum_thres, enough_small)
+
+min_thres_point = mat(minimum_thres, :);
+
+if( isequal(min_thres_point, [ enough_small, enough_small, enough_small]) )
+ bool = 0;
+else
+ bool = 1;
+end
+
+end
diff --git a/src/fast_evaluator/src/XY2Theta.m b/src/fast_evaluator/src/XY2Theta.m
new file mode 100644
index 0000000..d2d2d1a
--- /dev/null
+++ b/src/fast_evaluator/src/XY2Theta.m
@@ -0,0 +1,17 @@
+function [ theta ] = XY2Theta( x, y )
+
+ if (x >= 0 && y >= 0)
+ theta = 180/pi * atan(y/x);
+ end
+ if (x < 0 && y >= 0)
+ theta = 180 - ((180/pi) * atan(y/(-x)));
+ end
+ if (x < 0 && y < 0)
+ theta = 180 + ((180/pi) * atan(y/x));
+ end
+ if ( x >= 0 && y < 0)
+ theta = 360 - ((180/pi) * atan((-y)/x));
+ end
+
+end
+
diff --git a/src/fast_evaluator/src/deg2utm.m b/src/fast_evaluator/src/deg2utm.m
new file mode 100644
index 0000000..a6527fa
--- /dev/null
+++ b/src/fast_evaluator/src/deg2utm.m
@@ -0,0 +1,121 @@
+function [x,y,utmzone] = deg2utm(Lat,Lon)
+% -------------------------------------------------------------------------
+% [x,y,utmzone] = deg2utm(Lat,Lon)
+%
+% Description: Function to convert lat/lon vectors into UTM coordinates (WGS84).
+% Some code has been extracted from UTM.m function by Gabriel Ruiz Martinez.
+%
+% Inputs:
+% Lat: Latitude vector. Degrees. +ddd.ddddd WGS84
+% Lon: Longitude vector. Degrees. +ddd.ddddd WGS84
+%
+% Outputs:
+% x, y , utmzone. See example
+%
+% Example 1:
+% Lat=[40.3154333; 46.283900; 37.577833; 28.645650; 38.855550; 25.061783];
+% Lon=[-3.4857166; 7.8012333; -119.95525; -17.759533; -94.7990166; 121.640266];
+% [x,y,utmzone] = deg2utm(Lat,Lon);
+% fprintf('%7.0f ',x)
+% 458731 407653 239027 230253 343898 362850
+% fprintf('%7.0f ',y)
+% 4462881 5126290 4163083 3171843 4302285 2772478
+% utmzone =
+% 30 T
+% 32 T
+% 11 S
+% 28 R
+% 15 S
+% 51 R
+%
+% Example 2: If you have Lat/Lon coordinates in Degrees, Minutes and Seconds
+% LatDMS=[40 18 55.56; 46 17 2.04];
+% LonDMS=[-3 29 8.58; 7 48 4.44];
+% Lat=dms2deg(mat2dms(LatDMS)); %convert into degrees
+% Lon=dms2deg(mat2dms(LonDMS)); %convert into degrees
+% [x,y,utmzone] = deg2utm(Lat,Lon)
+%
+% Author:
+% Rafael Palacios
+% Universidad Pontificia Comillas
+% Madrid, Spain
+% Version: Apr/06, Jun/06, Aug/06, Aug/06
+% Aug/06: fixed a problem (found by Rodolphe Dewarrat) related to southern
+% hemisphere coordinates.
+% Aug/06: corrected m-Lint warnings
+%-------------------------------------------------------------------------
+% Argument checking
+%
+error(nargchk(2, 2, nargin)); %2 arguments required
+n1=length(Lat);
+n2=length(Lon);
+if (n1~=n2)
+ error('Lat and Lon vectors should have the same length');
+end
+% Memory pre-allocation
+%
+x=zeros(n1,1);
+y=zeros(n1,1);
+utmzone(n1,:)='60 X';
+% Main Loop
+%
+for i=1:n1
+ la=Lat(i);
+ lo=Lon(i);
+ sa = 6378137.000000 ; sb = 6356752.314245;
+
+ %e = ( ( ( sa ^ 2 ) - ( sb ^ 2 ) ) ^ 0.5 ) / sa;
+ e2 = ( ( ( sa ^ 2 ) - ( sb ^ 2 ) ) ^ 0.5 ) / sb;
+ e2cuadrada = e2 ^ 2;
+ c = ( sa ^ 2 ) / sb;
+ %alpha = ( sa - sb ) / sa; %f
+ %ablandamiento = 1 / alpha; % 1/f
+ lat = la * ( pi / 180 );
+ lon = lo * ( pi / 180 );
+ Huso = fix( ( lo / 6 ) + 31);
+ S = ( ( Huso * 6 ) - 183 );
+ deltaS = lon - ( S * ( pi / 180 ) );
+ if (la<-72), Letra='C';
+ elseif (la<-64), Letra='D';
+ elseif (la<-56), Letra='E';
+ elseif (la<-48), Letra='F';
+ elseif (la<-40), Letra='G';
+ elseif (la<-32), Letra='H';
+ elseif (la<-24), Letra='J';
+ elseif (la<-16), Letra='K';
+ elseif (la<-8), Letra='L';
+ elseif (la<0), Letra='M';
+ elseif (la<8), Letra='N';
+ elseif (la<16), Letra='P';
+ elseif (la<24), Letra='Q';
+ elseif (la<32), Letra='R';
+ elseif (la<40), Letra='S';
+ elseif (la<48), Letra='T';
+ elseif (la<56), Letra='U';
+ elseif (la<64), Letra='V';
+ elseif (la<72), Letra='W';
+ else Letra='X';
+ end
+ a = cos(lat) * sin(deltaS);
+ epsilon = 0.5 * log( ( 1 + a) / ( 1 - a ) );
+ nu = atan( tan(lat) / cos(deltaS) ) - lat;
+ v = ( c / ( ( 1 + ( e2cuadrada * ( cos(lat) ) ^ 2 ) ) ) ^ 0.5 ) * 0.9996;
+ ta = ( e2cuadrada / 2 ) * epsilon ^ 2 * ( cos(lat) ) ^ 2;
+ a1 = sin( 2 * lat );
+ a2 = a1 * ( cos(lat) ) ^ 2;
+ j2 = lat + ( a1 / 2 );
+ j4 = ( ( 3 * j2 ) + a2 ) / 4;
+ j6 = ( ( 5 * j4 ) + ( a2 * ( cos(lat) ) ^ 2) ) / 3;
+ alfa = ( 3 / 4 ) * e2cuadrada;
+ beta = ( 5 / 3 ) * alfa ^ 2;
+ gama = ( 35 / 27 ) * alfa ^ 3;
+ Bm = 0.9996 * c * ( lat - alfa * j2 + beta * j4 - gama * j6 );
+ xx = epsilon * v * ( 1 + ( ta / 3 ) ) + 500000;
+ yy = nu * v * ( 1 + ta ) + Bm;
+ if (yy<0)
+ yy=9999999+yy;
+ end
+ x(i)=xx;
+ y(i)=yy;
+ utmzone(i,:)=sprintf('%02d %c',Huso,Letra);
+end
diff --git a/src/fast_evaluator/src/dist_btn_pose.m b/src/fast_evaluator/src/dist_btn_pose.m
new file mode 100644
index 0000000..0d8e55e
--- /dev/null
+++ b/src/fast_evaluator/src/dist_btn_pose.m
@@ -0,0 +1,3 @@
+function dist = dist_btn_pose(pose1, pose2)
+ dist = sqrt( (pose1(1) - pose2(1))^2 + (pose1(2) - pose2(2))^2);
+end
diff --git a/src/fast_evaluator/src/find_topk_from_candidates.m b/src/fast_evaluator/src/find_topk_from_candidates.m
new file mode 100644
index 0000000..4ae5175
--- /dev/null
+++ b/src/fast_evaluator/src/find_topk_from_candidates.m
@@ -0,0 +1,28 @@
+function [ nearest_idx, min_dist ] = find_topk_from_candidates(query_img, query_idx, candidates, thres)
+
+global num_node_enough_apart;
+global radar_imgs;
+
+nearest_idx = 0;
+min_dist = inf; % initialization
+for ith_candidate = 1:length(candidates)
+ candidate_node_idx = candidates(ith_candidate);
+ candidate_img = radar_imgs{candidate_node_idx};
+
+ if( abs(query_idx - candidate_node_idx) < num_node_enough_apart)
+ continue;
+ end
+
+ distance_to_query = dist(query_img, candidate_img);
+ if( distance_to_query > thres)
+ continue;
+ end
+
+ if( distance_to_query < min_dist)
+ nearest_idx = candidate_node_idx;
+ min_dist = distance_to_query;
+ end
+end
+
+end
+
diff --git a/src/fast_evaluator/src/isRevisitGlobalLoc.m b/src/fast_evaluator/src/isRevisitGlobalLoc.m
new file mode 100644
index 0000000..6ebf0ec
--- /dev/null
+++ b/src/fast_evaluator/src/isRevisitGlobalLoc.m
@@ -0,0 +1,20 @@
+function [is_revisit, min_dist] = isRevisitGlobalLoc(query_pose, db_poses, thres)
+
+num_dbs = length(db_poses);
+
+dists = zeros(1, num_dbs);
+for ii=1:num_dbs
+ dist = norm(query_pose - db_poses(ii, :));
+ dists(ii) = dist;
+end
+
+if ( min(dists) < thres )
+ is_revisit = 1;
+else
+ is_revisit = 0;
+end
+
+min_dist = min(dists);
+
+end
+
diff --git a/src/fast_evaluator/src/is_revisit.m b/src/fast_evaluator/src/is_revisit.m
new file mode 100644
index 0000000..14132a8
--- /dev/null
+++ b/src/fast_evaluator/src/is_revisit.m
@@ -0,0 +1,23 @@
+function [ revisitness ] = is_revisit(query_idx, query_pose, radar_poses, revisit_criteria, num_node_enough_apart)
+
+num_db = size(radar_poses, 1);
+
+revisitness = 0;
+for ii = 1:num_db
+
+ if( abs(query_idx - ii) < num_node_enough_apart)
+ continue;
+ end
+
+ pose = radar_poses(ii, :);
+
+ dist = dist_btn_pose(query_pose, pose);
+
+ if(dist < revisit_criteria)
+ revisitness = 1;
+ break;
+ end
+end
+
+end
+
diff --git a/src/fast_evaluator/src/loadData.m b/src/fast_evaluator/src/loadData.m
new file mode 100644
index 0000000..c189be3
--- /dev/null
+++ b/src/fast_evaluator/src/loadData.m
@@ -0,0 +1,45 @@
+function [scancontexts, ringkeys, poses] = loadData(down_shape, skip_data_frame)
+
+%%
+global data_path;
+data_save_path = fullfile('data/');
+
+%%
+% newly make
+if exist(data_save_path) == 0
+ % make
+ [scancontexts, ringkeys, poses] = makeExperience(data_path, down_shape, skip_data_frame);
+
+ % save
+ mkdir(data_save_path);
+
+ filename = strcat(data_save_path, 'scancontexts', num2str(down_shape(1)), 'x', num2str(down_shape(2)), '.mat');
+ save(filename, 'scancontexts');
+ filename = strcat(data_save_path, 'ringkeys', num2str(down_shape(1)), 'x', num2str(down_shape(2)), '.mat');
+ save(filename, 'ringkeys');
+ filename = strcat(data_save_path, 'poses', num2str(down_shape(1)), 'x', num2str(down_shape(2)), '.mat');
+ save(filename, 'poses');
+
+% or load
+else
+ filename = strcat(data_save_path, 'scancontexts', num2str(down_shape(1)), 'x', num2str(down_shape(2)), '.mat');
+ load(filename);
+ % fix
+ for iii = 1:length(scancontexts)
+ sc = double(scancontexts{iii});
+ scancontexts{iii} = sc;
+ end
+
+ filename = strcat(data_save_path, 'ringkeys', num2str(down_shape(1)), 'x', num2str(down_shape(2)), '.mat');
+ load(filename);
+ filename = strcat(data_save_path, 'poses', num2str(down_shape(1)), 'x', num2str(down_shape(2)), '.mat');
+ load(filename);
+
+ disp('- successfully loaded.');
+end
+
+%%
+disp(' ');
+
+end
+
diff --git a/src/fast_evaluator/src/makeExperience.m b/src/fast_evaluator/src/makeExperience.m
new file mode 100644
index 0000000..6731c1d
--- /dev/null
+++ b/src/fast_evaluator/src/makeExperience.m
@@ -0,0 +1,63 @@
+function [scancontexts, ringkeys, xy_poses] = makeExperience(data_dir, shape, skip_data_frame)
+
+%%
+num_rings = shape(1);
+num_sectors = shape(2);
+
+%%
+lidar_data_dir = strcat(data_dir, 'velodyne/');
+data_names = osdir(lidar_data_dir);
+
+%% gps to xyz
+gtpose = csvread(strcat(data_dir, '00.csv'));
+% gtpose_time = gtpose(:, 1);
+gtpose_xy = gtpose(:, [4,12]);
+
+
+%%
+num_data = length(data_names);
+num_data_save = floor(num_data/skip_data_frame) + 1;
+save_counter = 1;
+
+scancontexts = cell(1, num_data_save);
+ringkeys = zeros(num_data_save, num_rings);
+xy_poses = zeros(num_data_save, 2);
+
+for data_idx = 1:num_data
+
+ if(rem(data_idx, skip_data_frame) ~=0)
+ continue;
+ end
+
+ file_name = data_names{data_idx};
+ data_time = str2double(file_name(1:end-4));
+ data_path = strcat(lidar_data_dir, file_name);
+
+ % get
+ ptcloud = readBin(data_path);
+ sc = Ptcloud2ScanContext(ptcloud, shape(2), shape(1), 80); % up to 80 meter
+
+ rk = ringkey(sc);
+
+% [nearest_time_gap, nearest_idx] = min(abs(repmat(data_time, length(gtpose_time), 1) - gtpose_time));
+ xy_pose = gtpose_xy(data_idx, :);
+
+ % save
+ scancontexts{save_counter} = sc;
+ ringkeys(save_counter, :) = rk;
+ xy_poses(save_counter, :) = xy_pose;
+ save_counter = save_counter + 1;
+
+ % log
+ if(rem(data_idx, 100) == 0)
+ message = strcat(num2str(data_idx), " / ", num2str(num_data), " processed (skip: ", num2str(skip_data_frame), ")");
+ disp(message);
+ end
+end
+
+scancontexts = scancontexts(1:save_counter-1);
+ringkeys = ringkeys(1:save_counter-1, :);
+xy_poses = xy_poses(1:save_counter-1, :);
+
+
+end
diff --git a/src/fast_evaluator/src/osdir.m b/src/fast_evaluator/src/osdir.m
new file mode 100644
index 0000000..2698637
--- /dev/null
+++ b/src/fast_evaluator/src/osdir.m
@@ -0,0 +1,4 @@
+function [files] = osdir(path)
+ files = dir(path); files(1:2) = []; files = {files(:).name};
+end
+
diff --git a/src/fast_evaluator/src/readBin.m b/src/fast_evaluator/src/readBin.m
new file mode 100644
index 0000000..011e3de
--- /dev/null
+++ b/src/fast_evaluator/src/readBin.m
@@ -0,0 +1,10 @@
+function ptcloud = readBin(bin_path)
+
+%% Read
+fid = fopen(bin_path, 'rb'); raw_data = fread(fid, [4 inf], 'single'); fclose(fid);
+points = raw_data(1:3,:)';
+points(:, 3) = points(:, 3) + 1.9; % z in car coord.
+
+ptcloud = pointCloud(points);
+
+end % end of function
diff --git a/src/fast_evaluator/src/resize_polar_img.m b/src/fast_evaluator/src/resize_polar_img.m
new file mode 100644
index 0000000..7db285e
--- /dev/null
+++ b/src/fast_evaluator/src/resize_polar_img.m
@@ -0,0 +1,29 @@
+function [down_img] = resize_polar_img(varargin)
+
+%%
+% arg 1: target image
+% arg 2: size of the downsized image; the number of [r, theta] for 200m, 360 deg
+% arg 3: interpolation type
+
+%%
+if nargin == 1
+ img = varargin{1};
+ rescale_pixel = [40, 60];
+ interpolation_method = 'box';
+end
+
+if nargin == 2
+ img = varargin{1};
+ rescale_pixel = varargin{2};
+ interpolation_method = 'box';
+end
+
+if nargin == 3
+ img = varargin{1};
+ rescale_pixel = varargin{2};
+ interpolation_method = varargin{3};
+end
+
+down_img = imresize(img, rescale_pixel, 'method', interpolation_method);
+
+end
\ No newline at end of file
diff --git a/src/fast_evaluator/src/ringkey.m b/src/fast_evaluator/src/ringkey.m
new file mode 100644
index 0000000..cf69120
--- /dev/null
+++ b/src/fast_evaluator/src/ringkey.m
@@ -0,0 +1,13 @@
+function [ ring_key ] = ringkey(sc)
+
+num_rings = size(sc, 1);
+
+ring_key = zeros(1, num_rings);
+for ith=1:num_rings
+ ith_ring = sc(ith,:);
+% ring_key(ith) = mean(ith_ring);
+ ring_key(ith) = nnz(ith_ring);
+end
+
+end
+
diff --git a/src/fast_evaluator/src/sc_dist.m b/src/fast_evaluator/src/sc_dist.m
new file mode 100644
index 0000000..9463936
--- /dev/null
+++ b/src/fast_evaluator/src/sc_dist.m
@@ -0,0 +1,41 @@
+function [dist] = dist(sc1,sc2)
+
+num_sectors = size(sc1, 2);
+
+% repeate to move 1 columns
+sim_for_each_cols = zeros(1, num_sectors);
+
+for i = 1:num_sectors
+ %% Shift
+ one_step = 1; % const
+ sc1 = circshift(sc1, one_step, 2); % 2 means columne shift
+
+ %% compare
+ sum_of_cos_sim = 0;
+ num_col_engaged = 0;
+
+ for j = 1:num_sectors
+ col_j_1 = sc1(:,j);
+ col_j_2 = sc2(:,j);
+
+ if( ~any(col_j_1) || ~any(col_j_2))
+ continue;
+ end
+
+ % calc sim
+ cos_similarity = dot(col_j_1, col_j_2) / (norm(col_j_1)*norm(col_j_2));
+ sum_of_cos_sim = sum_of_cos_sim + cos_similarity;
+
+ num_col_engaged = num_col_engaged +1;
+ end
+
+ % devided by num_col_engaged: So, even if there are many columns that are excluded from the calculation, we can get high scores if other columns are well fit.
+ sim_for_each_cols(i) = sum_of_cos_sim/num_col_engaged;
+
+end
+
+sim = max(sim_for_each_cols);
+
+dist = 1 - sim;
+
+end
diff --git a/src/fast_evaluator_radar/README.md b/src/fast_evaluator_radar/README.md
new file mode 100644
index 0000000..a955927
--- /dev/null
+++ b/src/fast_evaluator_radar/README.md
@@ -0,0 +1,23 @@
+# Radar Scan Context
+
+- Scan Context also works for the radar data (i.e., Navtech radar)
+ - Radar Scan Context was introduced in the [MulRan dataset paper](https://irap.kaist.ac.kr/publications/gskim-2020-icra.pdf)
+ - This directory contains the evaluation code, for [radar place recognition](https://sites.google.com/view/mulran-pr/radar-place-recognition), used in the MulRan paper.
+ - if you use the dataset or our method, please refer the paper:
+ ```
+ @INPROCEEDINGS { gskim-2020-icra,
+ AUTHOR = { Giseop Kim, Yeong Sang Park, Younghun Cho, Jinyong Jeong, Ayoung Kim },
+ TITLE = { MulRan: Multimodal Range Dataset for Urban Place Recognition },
+ BOOKTITLE = { Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) },
+ YEAR = { 2020 },
+ MONTH = { May },
+ ADDRESS = { Paris }
+ }
+ ```
+
+- More information about the radar data, please refer [MulRan](https://sites.google.com/view/mulran-pr/home) or Oxford Radar RobotCar dataset
+
+## How to use
+- 1. write your own MulRan dataset path in the main.m file
+- 2. run main.m (then some data and evaluation files will be generated)
+- 3. run prcurve_drawer.m
diff --git a/src/fast_evaluator_radar/linspecer.m b/src/fast_evaluator_radar/linspecer.m
new file mode 100644
index 0000000..31c5e6e
--- /dev/null
+++ b/src/fast_evaluator_radar/linspecer.m
@@ -0,0 +1,261 @@
+% function lineStyles = linspecer(N)
+% This function creates an Nx3 array of N [R B G] colors
+% These can be used to plot lots of lines with distinguishable and nice
+% looking colors.
+%
+% lineStyles = linspecer(N); makes N colors for you to use: lineStyles(ii,:)
+%
+% colormap(linspecer); set your colormap to have easily distinguishable
+% colors and a pleasing aesthetic
+%
+% lineStyles = linspecer(N,'qualitative'); forces the colors to all be distinguishable (up to 12)
+% lineStyles = linspecer(N,'sequential'); forces the colors to vary along a spectrum
+%
+% % Examples demonstrating the colors.
+%
+% LINE COLORS
+% N=6;
+% X = linspace(0,pi*3,1000);
+% Y = bsxfun(@(x,n)sin(x+2*n*pi/N), X.', 1:N);
+% C = linspecer(N);
+% axes('NextPlot','replacechildren', 'ColorOrder',C);
+% plot(X,Y,'linewidth',5)
+% ylim([-1.1 1.1]);
+%
+% SIMPLER LINE COLOR EXAMPLE
+% N = 6; X = linspace(0,pi*3,1000);
+% C = linspecer(N)
+% hold off;
+% for ii=1:N
+% Y = sin(X+2*ii*pi/N);
+% plot(X,Y,'color',C(ii,:),'linewidth',3);
+% hold on;
+% end
+%
+% COLORMAP EXAMPLE
+% A = rand(15);
+% figure; imagesc(A); % default colormap
+% figure; imagesc(A); colormap(linspecer); % linspecer colormap
+%
+% See also NDHIST, NHIST, PLOT, COLORMAP, 43700-cubehelix-colormaps
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% by Jonathan Lansey, March 2009-2013 Lansey at gmail.com %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+%% credits and where the function came from
+% The colors are largely taken from:
+% http://colorbrewer2.org and Cynthia Brewer, Mark Harrower and The Pennsylvania State University
+%
+%
+% She studied this from a phsychometric perspective and crafted the colors
+% beautifully.
+%
+% I made choices from the many there to decide the nicest once for plotting
+% lines in Matlab. I also made a small change to one of the colors I
+% thought was a bit too bright. In addition some interpolation is going on
+% for the sequential line styles.
+%
+%
+%%
+
+function lineStyles=linspecer(N,varargin)
+
+if nargin==0 % return a colormap
+ lineStyles = linspecer(128);
+ return;
+end
+
+if ischar(N)
+ lineStyles = linspecer(128,N);
+ return;
+end
+
+if N<=0 % its empty, nothing else to do here
+ lineStyles=[];
+ return;
+end
+
+% interperet varagin
+qualFlag = 0;
+colorblindFlag = 0;
+
+if ~isempty(varargin)>0 % you set a parameter?
+ switch lower(varargin{1})
+ case {'qualitative','qua'}
+ if N>12 % go home, you just can't get this.
+ warning('qualitiative is not possible for greater than 12 items, please reconsider');
+ else
+ if N>9
+ warning(['Default may be nicer for ' num2str(N) ' for clearer colors use: whitebg(''black''); ']);
+ end
+ end
+ qualFlag = 1;
+ case {'sequential','seq'}
+ lineStyles = colorm(N);
+ return;
+ case {'white','whitefade'}
+ lineStyles = whiteFade(N);return;
+ case 'red'
+ lineStyles = whiteFade(N,'red');return;
+ case 'blue'
+ lineStyles = whiteFade(N,'blue');return;
+ case 'green'
+ lineStyles = whiteFade(N,'green');return;
+ case {'gray','grey'}
+ lineStyles = whiteFade(N,'gray');return;
+ case {'colorblind'}
+ colorblindFlag = 1;
+ otherwise
+ warning(['parameter ''' varargin{1} ''' not recognized']);
+ end
+end
+% *.95
+% predefine some colormaps
+ set3 = colorBrew2mat({[141, 211, 199];[ 255, 237, 111];[ 190, 186, 218];[ 251, 128, 114];[ 128, 177, 211];[ 253, 180, 98];[ 179, 222, 105];[ 188, 128, 189];[ 217, 217, 217];[ 204, 235, 197];[ 252, 205, 229];[ 255, 255, 179]}');
+set1JL = brighten(colorBrew2mat({[228, 26, 28];[ 55, 126, 184]; [ 77, 175, 74];[ 255, 127, 0];[ 255, 237, 111]*.85;[ 166, 86, 40];[ 247, 129, 191];[ 153, 153, 153];[ 152, 78, 163]}'));
+set1 = brighten(colorBrew2mat({[ 55, 126, 184]*.85;[228, 26, 28];[ 77, 175, 74];[ 255, 127, 0];[ 152, 78, 163]}),.8);
+
+% colorblindSet = {[215,25,28];[253,174,97];[171,217,233];[44,123,182]};
+colorblindSet = {[215,25,28];[253,174,97];[171,217,233]*.8;[44,123,182]*.8};
+
+set3 = dim(set3,.93);
+
+if colorblindFlag
+ switch N
+ % sorry about this line folks. kind of legacy here because I used to
+ % use individual 1x3 cells instead of nx3 arrays
+ case 4
+ lineStyles = colorBrew2mat(colorblindSet);
+ otherwise
+ colorblindFlag = false;
+ warning('sorry unsupported colorblind set for this number, using regular types');
+ end
+end
+if ~colorblindFlag
+ switch N
+ case 1
+ lineStyles = { [ 55, 126, 184]/255};
+ case {2, 3, 4, 5 }
+ lineStyles = set1(1:N);
+ case {6 , 7, 8, 9}
+ lineStyles = set1JL(1:N)';
+ case {10, 11, 12}
+ if qualFlag % force qualitative graphs
+ lineStyles = set3(1:N)';
+ else % 10 is a good number to start with the sequential ones.
+ lineStyles = cmap2linspecer(colorm(N));
+ end
+ otherwise % any old case where I need a quick job done.
+ lineStyles = cmap2linspecer(colorm(N));
+ end
+end
+lineStyles = cell2mat(lineStyles);
+
+end
+
+% extra functions
+function varIn = colorBrew2mat(varIn)
+for ii=1:length(varIn) % just divide by 255
+ varIn{ii}=varIn{ii}/255;
+end
+end
+
+function varIn = brighten(varIn,varargin) % increase the brightness
+
+if isempty(varargin),
+ frac = .9;
+else
+ frac = varargin{1};
+end
+
+for ii=1:length(varIn)
+ varIn{ii}=varIn{ii}*frac+(1-frac);
+end
+end
+
+function varIn = dim(varIn,f)
+ for ii=1:length(varIn)
+ varIn{ii} = f*varIn{ii};
+ end
+end
+
+function vOut = cmap2linspecer(vIn) % changes the format from a double array to a cell array with the right format
+vOut = cell(size(vIn,1),1);
+for ii=1:size(vIn,1)
+ vOut{ii} = vIn(ii,:);
+end
+end
+%%
+% colorm returns a colormap which is really good for creating informative
+% heatmap style figures.
+% No particular color stands out and it doesn't do too badly for colorblind people either.
+% It works by interpolating the data from the
+% 'spectral' setting on http://colorbrewer2.org/ set to 11 colors
+% It is modified a little to make the brightest yellow a little less bright.
+function cmap = colorm(varargin)
+n = 100;
+if ~isempty(varargin)
+ n = varargin{1};
+end
+
+if n==1
+ cmap = [0.2005 0.5593 0.7380];
+ return;
+end
+if n==2
+ cmap = [0.2005 0.5593 0.7380;
+ 0.9684 0.4799 0.2723];
+ return;
+end
+
+frac=.95; % Slight modification from colorbrewer here to make the yellows in the center just a bit darker
+cmapp = [158, 1, 66; 213, 62, 79; 244, 109, 67; 253, 174, 97; 254, 224, 139; 255*frac, 255*frac, 191*frac; 230, 245, 152; 171, 221, 164; 102, 194, 165; 50, 136, 189; 94, 79, 162];
+x = linspace(1,n,size(cmapp,1));
+xi = 1:n;
+cmap = zeros(n,3);
+for ii=1:3
+ cmap(:,ii) = pchip(x,cmapp(:,ii),xi);
+end
+cmap = flipud(cmap/255);
+end
+
+function cmap = whiteFade(varargin)
+n = 100;
+if nargin>0
+ n = varargin{1};
+end
+
+thisColor = 'blue';
+
+if nargin>1
+ thisColor = varargin{2};
+end
+switch thisColor
+ case {'gray','grey'}
+ cmapp = [255,255,255;240,240,240;217,217,217;189,189,189;150,150,150;115,115,115;82,82,82;37,37,37;0,0,0];
+ case 'green'
+ cmapp = [247,252,245;229,245,224;199,233,192;161,217,155;116,196,118;65,171,93;35,139,69;0,109,44;0,68,27];
+ case 'blue'
+ cmapp = [247,251,255;222,235,247;198,219,239;158,202,225;107,174,214;66,146,198;33,113,181;8,81,156;8,48,107];
+ case 'red'
+ cmapp = [255,245,240;254,224,210;252,187,161;252,146,114;251,106,74;239,59,44;203,24,29;165,15,21;103,0,13];
+ otherwise
+ warning(['sorry your color argument ' thisColor ' was not recognized']);
+end
+
+cmap = interpomap(n,cmapp);
+end
+
+% Eat a approximate colormap, then interpolate the rest of it up.
+function cmap = interpomap(n,cmapp)
+ x = linspace(1,n,size(cmapp,1));
+ xi = 1:n;
+ cmap = zeros(n,3);
+ for ii=1:3
+ cmap(:,ii) = pchip(x,cmapp(:,ii),xi);
+ end
+ cmap = (cmap/255); % flipud??
+end
+
+
+
diff --git a/src/fast_evaluator_radar/main.m b/src/fast_evaluator_radar/main.m
new file mode 100644
index 0000000..c9da55f
--- /dev/null
+++ b/src/fast_evaluator_radar/main.m
@@ -0,0 +1,162 @@
+clear; clc;
+
+addpath(genpath('src'));
+addpath(genpath('data'));
+
+%% data preparation
+global data_path;
+% data_path = '/your/mulran/sequence/dir/Riverside02/';
+data_path = '/media/user/My Passport/data/MulRan_eval/Riverside_2_20190816/20190816/';
+% ### NOTE: Use this sequence directory structure
+% example:
+% /your/MulRan/sequence/dir/Riverside02/
+% L sensor_data/
+% L radar/
+% L polar/
+% L {unix_times}.png
+% L global_pose.csv
+
+down_shape = [40, 120];
+[data_scancontexts, data_ringkeys, data_poses] = loadData(down_shape);
+
+
+%% main - global recognizer
+revisit_criteria = 5; % in meter (recommend test for 5, 10, 20 meters)
+keyframe_gap = 1; % for_fast_eval (if 1, no skip)
+
+global num_candidates; num_candidates = 5;
+% NOTE about num_candidates
+% - 50 was used in the MulRan paper
+% - But we found, interestingly, using less keys showed similar
+% performance - also we can save the computation time of course.
+% - That means our ring key has good disriminative power.
+
+global num_node_enough_apart; num_node_enough_apart = 50;
+
+% policy (top N)
+num_top_n = 25;
+top_n = linspace(1, num_top_n, num_top_n);
+
+% Entropy thresholds
+middle_thres = 0.01;
+thresholds1 = linspace(0, middle_thres, 50);
+thresholds2 = linspace(middle_thres, 1, 50);
+thresholds = [thresholds1, thresholds2];
+num_thresholds = length(thresholds);
+
+% Main variables to store the result for drawing PR curve
+num_hits = zeros(num_top_n, num_thresholds);
+num_false_alarms = zeros(num_top_n, num_thresholds);
+num_correct_rejections = zeros(num_top_n, num_thresholds);
+num_misses = zeros(num_top_n, num_thresholds);
+
+% main
+loop_log = [];
+
+exp_poses = [];
+exp_ringkeys = [];
+exp_scancontexts = {};
+
+num_queries = length(data_poses);
+for query_idx = 1:num_queries - 1
+
+ % save to (online) DB
+ query_sc = data_scancontexts{query_idx};
+ query_rk = data_ringkeys(query_idx, :);
+ query_pose = data_poses(query_idx,:);
+
+ exp_scancontexts{end+1} = query_sc;
+ exp_poses = [exp_poses; query_pose];
+ exp_ringkeys = [exp_ringkeys; query_rk];
+
+ if(rem(query_idx, keyframe_gap) ~= 0)
+ continue;
+ end
+
+ if( length(exp_scancontexts) < num_node_enough_apart )
+ continue;
+ end
+
+ tree = createns(exp_ringkeys(1:end-(num_node_enough_apart-1), :), 'NSMethod', 'kdtree'); % Create object to use in k-nearest neighbor search
+
+ % revisitness
+ [revisitness, how_far_apart] = isRevisitGlobalLoc(query_pose, exp_poses(1:end-(num_node_enough_apart-1), :), revisit_criteria);
+
+ % find candidates
+ candidates = knnsearch(tree, query_rk, 'K', num_candidates);
+
+ % find the nearest (top 1) via pairwise comparison
+ nearest_idx = 0;
+ min_dist = inf; % initialization
+ for ith_candidate = 1:length(candidates)
+ candidate_node_idx = candidates(ith_candidate);
+ candidate_img = exp_scancontexts{candidate_node_idx};
+
+% if( abs(query_idx - candidate_node_idx) < num_node_enough_apart)
+% continue;
+% end
+
+ distance_to_query = sc_dist(query_sc, candidate_img);
+
+ if( distance_to_query < min_dist)
+ nearest_idx = candidate_node_idx;
+ min_dist = distance_to_query;
+ end
+ end
+
+ % prcurve analysis
+ for topk = 1:num_top_n
+ for thres_idx = 1:num_thresholds
+ threshold = thresholds(thres_idx);
+
+ reject = 0;
+ if( min_dist > threshold)
+ reject = 1;
+ end
+
+ if(reject == 1)
+ if(revisitness == 0)
+ % TN: Correct Rejection
+ num_correct_rejections(topk, thres_idx) = num_correct_rejections(topk, thres_idx) + 1;
+ else
+ % FN: MISS
+ num_misses(topk, thres_idx) = num_misses(topk, thres_idx) + 1;
+ end
+ else
+ % if under the theshold, it is considered seen.
+ % and then check the correctness
+ if( dist_btn_pose(query_pose, exp_poses(nearest_idx, :)) < revisit_criteria)
+ % TP: Hit
+ num_hits(topk, thres_idx) = num_hits(topk, thres_idx) + 1;
+ else
+ % FP: False Alarm
+ num_false_alarms(topk, thres_idx) = num_false_alarms(topk, thres_idx) + 1;
+ end
+ end
+
+ end
+ end
+
+ if( rem(query_idx, 100) == 0)
+ disp( strcat(num2str(query_idx/num_queries * 100), ' % processed') );
+ end
+
+end
+
+
+%% save the log
+savePath = strcat("pr_result/within ", num2str(revisit_criteria), "m/");
+if((~7==exist(savePath,'dir')))
+ mkdir(savePath);
+end
+save(strcat(savePath, 'nCorrectRejections.mat'), 'num_correct_rejections');
+save(strcat(savePath, 'nMisses.mat'), 'num_misses');
+save(strcat(savePath, 'nHits.mat'), 'num_hits');
+save(strcat(savePath, 'nFalseAlarms.mat'), 'num_false_alarms');
+
+
+
+
+
+
+
diff --git a/src/fast_evaluator_radar/prcurve_drawer.m b/src/fast_evaluator_radar/prcurve_drawer.m
new file mode 100644
index 0000000..f5d409d
--- /dev/null
+++ b/src/fast_evaluator_radar/prcurve_drawer.m
@@ -0,0 +1,132 @@
+rmpath(genpath('../../'));
+
+ResultsDir = './pr_result/';
+
+%%
+title_str = strcat('MulRan Sequence (radar polar)');
+
+%% Params
+FigIdx = 2;
+figure(FigIdx); clf;
+
+TopNindexes = [1];
+name = 'top1';
+
+nTopNindexes = length(TopNindexes);
+
+%% Main
+SequenceNames = dir(ResultsDir); SequenceNames(1:2, :) = []; SequenceNames = {SequenceNames(:).name};
+nSequences = length(SequenceNames);
+
+all_Precisions = {};
+all_Recalls = {};
+
+for ithTopN = 1:nTopNindexes
+
+ TopNidx = TopNindexes(ithTopN);
+
+ line_width = 4;
+
+ LineColors = colorcube(nSequences);
+ LineColors = linspecer(nSequences,'qualitative');
+% LineColors = linspecer(nSequences,'sequential');
+ LineColors = flipud(LineColors);
+
+ AUCs = zeros(1, nSequences);
+ for ithSeq = 1:nSequences
+
+ % seq info
+ ithSeqName = SequenceNames{ithSeq};
+ SequenceNames{ithSeq} = string(ithSeqName);
+
+ ithSeqPath = strcat(ResultsDir, ithSeqName, '/');
+ ithSeqPRcurveData = dir(ithSeqPath); ithSeqPRcurveData(1:2, :) = []; ithSeqPRcurveData = {ithSeqPRcurveData(:).name};
+
+ % load
+ nCorrectRejectionsAll = load(strcat(ithSeqPath, ithSeqPRcurveData{1}));
+ nCorrectRejectionsAll = nCorrectRejectionsAll.num_correct_rejections;
+ nCorrectRejectionsForThisTopN = nCorrectRejectionsAll(TopNidx, :);
+
+ nFalseAlarmsAll = load(strcat(ithSeqPath, ithSeqPRcurveData{2}));
+ nFalseAlarmsAll = nFalseAlarmsAll.num_false_alarms;
+ nFalseAlarmsForThisTopN = nFalseAlarmsAll(TopNidx, :);
+
+ nHitsAll = load(strcat(ithSeqPath, ithSeqPRcurveData{3}));
+ nHitsAll = nHitsAll.num_hits;
+ nHitsForThisTopN = nHitsAll(TopNidx, :);
+
+ nMissesAll = load(strcat(ithSeqPath, ithSeqPRcurveData{4}));
+ nMissesAll = nMissesAll.num_misses;
+ nMissesForThisTopN = nMissesAll(TopNidx, :);
+
+ % info
+ nTopNs = size(nCorrectRejectionsAll, 1);
+ nThres = size(nCorrectRejectionsAll, 2);
+
+ % main
+ Precisions = [];
+ Recalls = [];
+ Accuracies = [];
+ for ithThres = 1:nThres
+ nCorrectRejections = nCorrectRejectionsForThisTopN(ithThres);
+ nFalseAlarms = nFalseAlarmsForThisTopN(ithThres);
+ nHits = nHitsForThisTopN(ithThres);
+ nMisses = nMissesForThisTopN(ithThres);
+
+ nTotalTestPlaces = nCorrectRejections + nFalseAlarms + nHits + nMisses;
+
+ Precision = nHits / (nHits + nFalseAlarms);
+ Recall = nHits / (nHits + nMisses);
+ Acc = (nHits + nCorrectRejections)/nTotalTestPlaces;
+
+ Precisions = [Precisions; Precision];
+ Recalls = [Recalls; Recall];
+ Accuracies = [Accuracies; Acc];
+ end
+
+ num_points = length(Precisions);
+ Precisions(1) = 1;
+ AUC = 0;
+ for ith = 1:num_points-1
+ small_area = 1/2 * (Precisions(ith) + Precisions(ith+1)) * (Recalls(ith+1)-Recalls(ith));
+ AUC = AUC + small_area;
+ end
+ AUCs(ithSeq) = AUC;
+
+ all_Precisions{ithSeq} = Precisions;
+ all_Recalls{ithSeq} = Recalls;
+
+ % draw
+ figure(FigIdx);
+ set(gcf, 'Position', [10 10 800 500]);
+
+ fontsize = 10;
+ p = plot(Recalls, Precisions, 'LineWidth', line_width); % commonly x axis is recall
+ title(title_str, 'FontSize', fontsize);
+ xlabel('Recall', 'FontSize', fontsize); ylabel('Precision', 'FontSize', fontsize);
+ set(gca, 'FontSize', fontsize+5)
+ xticks([0 0.2 0.4 0.6 0.8 1.0])
+ xticklabels({'0','0.2','0.4','0.6','0.8','1'})
+ yticks([0 0.2 0.4 0.6 0.8 1.0])
+ yticklabels({'0','0.2','0.4','0.6','0.8','1'})
+
+ p(1).Color = LineColors(ithSeq, :);
+ p(1).MarkerEdgeColor = LineColors(ithSeq, :);
+ % axis equal;
+ xlim([0, 1]); ylim([0,1]);
+ grid on; grid minor;
+ hold on;
+
+ end
+
+ lgd = legend(SequenceNames, 'Location', 'best');
+ lgd.FontSize = fontsize + 3;
+ lgd.FontWeight = 'bold';
+
+ grid minor;
+
+ name = 'prcurve';
+ print('-bestfit', name,'-dpdf')
+
+end
+
diff --git a/src/fast_evaluator_radar/src/deg2utm.m b/src/fast_evaluator_radar/src/deg2utm.m
new file mode 100644
index 0000000..a6527fa
--- /dev/null
+++ b/src/fast_evaluator_radar/src/deg2utm.m
@@ -0,0 +1,121 @@
+function [x,y,utmzone] = deg2utm(Lat,Lon)
+% -------------------------------------------------------------------------
+% [x,y,utmzone] = deg2utm(Lat,Lon)
+%
+% Description: Function to convert lat/lon vectors into UTM coordinates (WGS84).
+% Some code has been extracted from UTM.m function by Gabriel Ruiz Martinez.
+%
+% Inputs:
+% Lat: Latitude vector. Degrees. +ddd.ddddd WGS84
+% Lon: Longitude vector. Degrees. +ddd.ddddd WGS84
+%
+% Outputs:
+% x, y , utmzone. See example
+%
+% Example 1:
+% Lat=[40.3154333; 46.283900; 37.577833; 28.645650; 38.855550; 25.061783];
+% Lon=[-3.4857166; 7.8012333; -119.95525; -17.759533; -94.7990166; 121.640266];
+% [x,y,utmzone] = deg2utm(Lat,Lon);
+% fprintf('%7.0f ',x)
+% 458731 407653 239027 230253 343898 362850
+% fprintf('%7.0f ',y)
+% 4462881 5126290 4163083 3171843 4302285 2772478
+% utmzone =
+% 30 T
+% 32 T
+% 11 S
+% 28 R
+% 15 S
+% 51 R
+%
+% Example 2: If you have Lat/Lon coordinates in Degrees, Minutes and Seconds
+% LatDMS=[40 18 55.56; 46 17 2.04];
+% LonDMS=[-3 29 8.58; 7 48 4.44];
+% Lat=dms2deg(mat2dms(LatDMS)); %convert into degrees
+% Lon=dms2deg(mat2dms(LonDMS)); %convert into degrees
+% [x,y,utmzone] = deg2utm(Lat,Lon)
+%
+% Author:
+% Rafael Palacios
+% Universidad Pontificia Comillas
+% Madrid, Spain
+% Version: Apr/06, Jun/06, Aug/06, Aug/06
+% Aug/06: fixed a problem (found by Rodolphe Dewarrat) related to southern
+% hemisphere coordinates.
+% Aug/06: corrected m-Lint warnings
+%-------------------------------------------------------------------------
+% Argument checking
+%
+error(nargchk(2, 2, nargin)); %2 arguments required
+n1=length(Lat);
+n2=length(Lon);
+if (n1~=n2)
+ error('Lat and Lon vectors should have the same length');
+end
+% Memory pre-allocation
+%
+x=zeros(n1,1);
+y=zeros(n1,1);
+utmzone(n1,:)='60 X';
+% Main Loop
+%
+for i=1:n1
+ la=Lat(i);
+ lo=Lon(i);
+ sa = 6378137.000000 ; sb = 6356752.314245;
+
+ %e = ( ( ( sa ^ 2 ) - ( sb ^ 2 ) ) ^ 0.5 ) / sa;
+ e2 = ( ( ( sa ^ 2 ) - ( sb ^ 2 ) ) ^ 0.5 ) / sb;
+ e2cuadrada = e2 ^ 2;
+ c = ( sa ^ 2 ) / sb;
+ %alpha = ( sa - sb ) / sa; %f
+ %ablandamiento = 1 / alpha; % 1/f
+ lat = la * ( pi / 180 );
+ lon = lo * ( pi / 180 );
+ Huso = fix( ( lo / 6 ) + 31);
+ S = ( ( Huso * 6 ) - 183 );
+ deltaS = lon - ( S * ( pi / 180 ) );
+ if (la<-72), Letra='C';
+ elseif (la<-64), Letra='D';
+ elseif (la<-56), Letra='E';
+ elseif (la<-48), Letra='F';
+ elseif (la<-40), Letra='G';
+ elseif (la<-32), Letra='H';
+ elseif (la<-24), Letra='J';
+ elseif (la<-16), Letra='K';
+ elseif (la<-8), Letra='L';
+ elseif (la<0), Letra='M';
+ elseif (la<8), Letra='N';
+ elseif (la<16), Letra='P';
+ elseif (la<24), Letra='Q';
+ elseif (la<32), Letra='R';
+ elseif (la<40), Letra='S';
+ elseif (la<48), Letra='T';
+ elseif (la<56), Letra='U';
+ elseif (la<64), Letra='V';
+ elseif (la<72), Letra='W';
+ else Letra='X';
+ end
+ a = cos(lat) * sin(deltaS);
+ epsilon = 0.5 * log( ( 1 + a) / ( 1 - a ) );
+ nu = atan( tan(lat) / cos(deltaS) ) - lat;
+ v = ( c / ( ( 1 + ( e2cuadrada * ( cos(lat) ) ^ 2 ) ) ) ^ 0.5 ) * 0.9996;
+ ta = ( e2cuadrada / 2 ) * epsilon ^ 2 * ( cos(lat) ) ^ 2;
+ a1 = sin( 2 * lat );
+ a2 = a1 * ( cos(lat) ) ^ 2;
+ j2 = lat + ( a1 / 2 );
+ j4 = ( ( 3 * j2 ) + a2 ) / 4;
+ j6 = ( ( 5 * j4 ) + ( a2 * ( cos(lat) ) ^ 2) ) / 3;
+ alfa = ( 3 / 4 ) * e2cuadrada;
+ beta = ( 5 / 3 ) * alfa ^ 2;
+ gama = ( 35 / 27 ) * alfa ^ 3;
+ Bm = 0.9996 * c * ( lat - alfa * j2 + beta * j4 - gama * j6 );
+ xx = epsilon * v * ( 1 + ( ta / 3 ) ) + 500000;
+ yy = nu * v * ( 1 + ta ) + Bm;
+ if (yy<0)
+ yy=9999999+yy;
+ end
+ x(i)=xx;
+ y(i)=yy;
+ utmzone(i,:)=sprintf('%02d %c',Huso,Letra);
+end
diff --git a/src/fast_evaluator_radar/src/dist_btn_pose.m b/src/fast_evaluator_radar/src/dist_btn_pose.m
new file mode 100644
index 0000000..0d8e55e
--- /dev/null
+++ b/src/fast_evaluator_radar/src/dist_btn_pose.m
@@ -0,0 +1,3 @@
+function dist = dist_btn_pose(pose1, pose2)
+ dist = sqrt( (pose1(1) - pose2(1))^2 + (pose1(2) - pose2(2))^2);
+end
diff --git a/src/fast_evaluator_radar/src/find_topk_from_candidates.m b/src/fast_evaluator_radar/src/find_topk_from_candidates.m
new file mode 100644
index 0000000..4ae5175
--- /dev/null
+++ b/src/fast_evaluator_radar/src/find_topk_from_candidates.m
@@ -0,0 +1,28 @@
+function [ nearest_idx, min_dist ] = find_topk_from_candidates(query_img, query_idx, candidates, thres)
+
+global num_node_enough_apart;
+global radar_imgs;
+
+nearest_idx = 0;
+min_dist = inf; % initialization
+for ith_candidate = 1:length(candidates)
+ candidate_node_idx = candidates(ith_candidate);
+ candidate_img = radar_imgs{candidate_node_idx};
+
+ if( abs(query_idx - candidate_node_idx) < num_node_enough_apart)
+ continue;
+ end
+
+ distance_to_query = dist(query_img, candidate_img);
+ if( distance_to_query > thres)
+ continue;
+ end
+
+ if( distance_to_query < min_dist)
+ nearest_idx = candidate_node_idx;
+ min_dist = distance_to_query;
+ end
+end
+
+end
+
diff --git a/src/fast_evaluator_radar/src/isRevisitGlobalLoc.m b/src/fast_evaluator_radar/src/isRevisitGlobalLoc.m
new file mode 100644
index 0000000..ef532c2
--- /dev/null
+++ b/src/fast_evaluator_radar/src/isRevisitGlobalLoc.m
@@ -0,0 +1,20 @@
+function [is_revisit, min_dist] = isRevisitGlobalLoc(query_pose, db_poses, thres)
+
+num_dbs = size(db_poses, 1);
+
+dists = zeros(1, num_dbs);
+for ii=1:num_dbs
+ dist = norm(query_pose - db_poses(ii, :));
+ dists(ii) = dist;
+end
+
+if ( min(dists) < thres )
+ is_revisit = 1;
+else
+ is_revisit = 0;
+end
+
+min_dist = min(dists);
+
+end
+
diff --git a/src/fast_evaluator_radar/src/is_revisit.m b/src/fast_evaluator_radar/src/is_revisit.m
new file mode 100644
index 0000000..14132a8
--- /dev/null
+++ b/src/fast_evaluator_radar/src/is_revisit.m
@@ -0,0 +1,23 @@
+function [ revisitness ] = is_revisit(query_idx, query_pose, radar_poses, revisit_criteria, num_node_enough_apart)
+
+num_db = size(radar_poses, 1);
+
+revisitness = 0;
+for ii = 1:num_db
+
+ if( abs(query_idx - ii) < num_node_enough_apart)
+ continue;
+ end
+
+ pose = radar_poses(ii, :);
+
+ dist = dist_btn_pose(query_pose, pose);
+
+ if(dist < revisit_criteria)
+ revisitness = 1;
+ break;
+ end
+end
+
+end
+
diff --git a/src/fast_evaluator_radar/src/loadData.m b/src/fast_evaluator_radar/src/loadData.m
new file mode 100644
index 0000000..ba366b7
--- /dev/null
+++ b/src/fast_evaluator_radar/src/loadData.m
@@ -0,0 +1,47 @@
+function [scancontexts, ringkeys, poses] = loadData(down_shape)
+
+%%
+global data_path;
+
+data_save_path = fullfile('.', 'data/');
+
+%%
+% newly make
+is_already_made_data_exist = exist(data_save_path);
+if is_already_made_data_exist == 0
+ % make
+ [scancontexts, ringkeys, poses] = makeExperience(data_path, down_shape);
+
+ % save
+ mkdir(data_save_path);
+
+ filename = strcat(data_save_path, 'scancontexts', num2str(down_shape(1)), 'x', num2str(down_shape(2)), '.mat');
+ save(filename, 'scancontexts');
+ filename = strcat(data_save_path, 'ringkeys', num2str(down_shape(1)), 'x', num2str(down_shape(2)), '.mat');
+ save(filename, 'ringkeys');
+ filename = strcat(data_save_path, 'poses', num2str(down_shape(1)), 'x', num2str(down_shape(2)), '.mat');
+ save(filename, 'poses');
+
+% or load
+else
+ filename = strcat(data_save_path, 'scancontexts', num2str(down_shape(1)), 'x', num2str(down_shape(2)), '.mat');
+ load(filename);
+ % fix
+ for iii = 1:length(scancontexts)
+ sc = double(scancontexts{iii});
+ scancontexts{iii} = sc;
+ end
+
+ filename = strcat(data_save_path, 'ringkeys', num2str(down_shape(1)), 'x', num2str(down_shape(2)), '.mat');
+ load(filename);
+ filename = strcat(data_save_path, 'poses', num2str(down_shape(1)), 'x', num2str(down_shape(2)), '.mat');
+ load(filename);
+
+ disp('- successfully loaded.');
+end
+
+%%
+disp(' ');
+
+end
+
diff --git a/src/fast_evaluator_radar/src/makeExperience.m b/src/fast_evaluator_radar/src/makeExperience.m
new file mode 100644
index 0000000..d52515f
--- /dev/null
+++ b/src/fast_evaluator_radar/src/makeExperience.m
@@ -0,0 +1,54 @@
+function [scancontexts, ringkeys, xy_poses] = makeExperience(data_dir, shape)
+
+%%
+num_rings = shape(1);
+num_sectors = shape(2);
+
+%%
+radar_data_dir = fullfile(data_dir, 'sensor_data/radar/polar/');
+data_names = osdir(radar_data_dir);
+
+%% gps to xyz
+gtpose = csvread(strcat(data_dir, 'global_pose.csv'));
+gtpose_time = gtpose(:, 1);
+gtpose_xy = gtpose(:, [5,9]);
+
+% figure(1); hold on;
+% plot(traj_x, traj_y);
+
+%%
+num_data = length(data_names);
+
+scancontexts = cell(1, num_data);
+ringkeys = zeros(num_data, num_rings);
+xy_poses = zeros(num_data, 2);
+
+for data_idx = 1:num_data
+ file_name = data_names{data_idx};
+ data_time = str2double(file_name(1:end-4));
+ data_path = strcat(radar_data_dir, file_name);
+
+ % get
+ sc = imread(data_path);
+ sc = imresize(sc, shape);
+ sc = double(sc);
+
+ rk = ringkey(sc);
+
+ [nearest_time_gap, nearest_idx] = min(abs(repmat(data_time, length(gtpose_time), 1) - gtpose_time));
+ xy_pose = gtpose_xy(nearest_idx, :);
+
+ % save
+ scancontexts{data_idx} = sc;
+ ringkeys(data_idx, :) = rk;
+ xy_poses(data_idx, :) = xy_pose;
+
+ % log
+ if(rem(data_idx, 100) == 0)
+ message = strcat(num2str(data_idx), " / ", num2str(num_data), " processed.");
+ disp(message);
+ end
+end
+
+end
+
diff --git a/src/fast_evaluator_radar/src/osdir.m b/src/fast_evaluator_radar/src/osdir.m
new file mode 100644
index 0000000..2698637
--- /dev/null
+++ b/src/fast_evaluator_radar/src/osdir.m
@@ -0,0 +1,4 @@
+function [files] = osdir(path)
+ files = dir(path); files(1:2) = []; files = {files(:).name};
+end
+
diff --git a/src/fast_evaluator_radar/src/resize_polar_img.m b/src/fast_evaluator_radar/src/resize_polar_img.m
new file mode 100644
index 0000000..7db285e
--- /dev/null
+++ b/src/fast_evaluator_radar/src/resize_polar_img.m
@@ -0,0 +1,29 @@
+function [down_img] = resize_polar_img(varargin)
+
+%%
+% arg 1: target image
+% arg 2: size of the downsized image; the number of [r, theta] for 200m, 360 deg
+% arg 3: interpolation type
+
+%%
+if nargin == 1
+ img = varargin{1};
+ rescale_pixel = [40, 60];
+ interpolation_method = 'box';
+end
+
+if nargin == 2
+ img = varargin{1};
+ rescale_pixel = varargin{2};
+ interpolation_method = 'box';
+end
+
+if nargin == 3
+ img = varargin{1};
+ rescale_pixel = varargin{2};
+ interpolation_method = varargin{3};
+end
+
+down_img = imresize(img, rescale_pixel, 'method', interpolation_method);
+
+end
\ No newline at end of file
diff --git a/src/fast_evaluator_radar/src/ringkey.m b/src/fast_evaluator_radar/src/ringkey.m
new file mode 100644
index 0000000..45685a6
--- /dev/null
+++ b/src/fast_evaluator_radar/src/ringkey.m
@@ -0,0 +1,12 @@
+function [ ring_key ] = ringkey(sc)
+
+num_rings = size(sc, 1);
+
+ring_key = zeros(1, num_rings);
+for ith=1:num_rings
+ ith_ring = sc(ith,:);
+ ring_key(ith) = mean(ith_ring);
+end
+
+end
+
diff --git a/src/fast_evaluator_radar/src/sc_dist.m b/src/fast_evaluator_radar/src/sc_dist.m
new file mode 100644
index 0000000..9463936
--- /dev/null
+++ b/src/fast_evaluator_radar/src/sc_dist.m
@@ -0,0 +1,41 @@
+function [dist] = dist(sc1,sc2)
+
+num_sectors = size(sc1, 2);
+
+% repeate to move 1 columns
+sim_for_each_cols = zeros(1, num_sectors);
+
+for i = 1:num_sectors
+ %% Shift
+ one_step = 1; % const
+ sc1 = circshift(sc1, one_step, 2); % 2 means columne shift
+
+ %% compare
+ sum_of_cos_sim = 0;
+ num_col_engaged = 0;
+
+ for j = 1:num_sectors
+ col_j_1 = sc1(:,j);
+ col_j_2 = sc2(:,j);
+
+ if( ~any(col_j_1) || ~any(col_j_2))
+ continue;
+ end
+
+ % calc sim
+ cos_similarity = dot(col_j_1, col_j_2) / (norm(col_j_1)*norm(col_j_2));
+ sum_of_cos_sim = sum_of_cos_sim + cos_similarity;
+
+ num_col_engaged = num_col_engaged +1;
+ end
+
+ % devided by num_col_engaged: So, even if there are many columns that are excluded from the calculation, we can get high scores if other columns are well fit.
+ sim_for_each_cols(i) = sum_of_cos_sim/num_col_engaged;
+
+end
+
+sim = max(sim_for_each_cols);
+
+dist = 1 - sim;
+
+end
diff --git a/src/matlab/descriptor/Ptcloud2ScanContext.m b/src/matlab/descriptor/Ptcloud2ScanContext.m
new file mode 100644
index 0000000..2ad3834
--- /dev/null
+++ b/src/matlab/descriptor/Ptcloud2ScanContext.m
@@ -0,0 +1,102 @@
+function [ img ] = Ptcloud2ScanContext( ptcloud, num_sector, num_ring, max_range )
+
+%% Preprocessing
+
+% Downsampling for fast search
+gridStep = 0.5; % 0.5m cubic grid downsampling is applied in the paper.
+ptcloud = pcdownsample(ptcloud, 'gridAverage', gridStep);
+
+% point cloud information
+num_points = ptcloud.Count;
+gap = max_range / num_ring;
+angle_one_sector = 360/num_sector;
+
+
+%% vacant bins
+cell_bins = cell(num_ring, num_sector);
+cell_bin_counter = ones(num_ring, num_sector);
+
+enough_large = 500; % for fast and constant time save, We contain maximum 500 points per each bin.
+enough_small = -10000;
+for ith_ring = 1:num_ring
+ for ith_sector = 1:num_sector
+ bin = enough_small * ones(enough_large, 3);
+ cell_bins{ith_ring, ith_sector} = bin;
+ end
+end
+
+
+%% Save a point to the corresponding bin
+for ith_point =1:num_points
+
+ % Point information
+ ith_point_xyz = ptcloud.Location(ith_point,:);
+ ith_point_r = sqrt(ith_point_xyz(1)^2 + ith_point_xyz(2)^2);
+ ith_point_theta = XY2Theta(ith_point_xyz(1), ith_point_xyz(2)); % degree
+
+ % Find the corresponding ring index
+ tmp_ring_index = floor(ith_point_r/gap);
+ if(tmp_ring_index >= num_ring)
+ ring_index = num_ring;
+ else
+ ring_index = tmp_ring_index + 1;
+ end
+
+ % Find the corresponding sector index
+ tmp_sector_index = ceil(ith_point_theta/angle_one_sector);
+ if(tmp_sector_index == 0)
+ sector_index = 1;
+ elseif(tmp_sector_index > num_sector || tmp_sector_index < 1)
+ sector_index = num_sector;
+ else
+ sector_index = tmp_sector_index;
+ end
+
+ % Assign point to the corresponding bin cell
+ try
+ corresponding_counter = cell_bin_counter(ring_index, sector_index); % 1D real value.
+ catch
+ continue;
+ end
+ cell_bins{ring_index, sector_index}(corresponding_counter, :) = ith_point_xyz;
+ cell_bin_counter(ring_index, sector_index) = cell_bin_counter(ring_index, sector_index) + 1; % increase count 1
+
+end
+
+
+%% bin to image format (2D matrix)
+img = zeros(num_ring, num_sector);
+
+min_num_thres = 5; % a bin with few points, we consider it is noise.
+
+% Find maximum Z value of each bin and Save into img
+for ith_ring = 1:num_ring
+ for ith_sector = 1:num_sector
+ value_of_the_bin = 0;
+ points_in_bin_ij = cell_bins{ith_ring, ith_sector};
+
+ if( IsBinHaveMoreThanMinimumPoints(points_in_bin_ij, min_num_thres, enough_small) )
+ value_of_the_bin = max(points_in_bin_ij(:, 3));
+ else
+ value_of_the_bin = 0;
+ end
+
+ img(ith_ring, ith_sector) = value_of_the_bin;
+ end
+end
+
+
+end % end of the main function
+
+
+function bool = IsBinHaveMoreThanMinimumPoints(mat, minimum_thres, enough_small)
+
+min_thres_point = mat(minimum_thres, :);
+
+if( isequal(min_thres_point, [ enough_small, enough_small, enough_small]) )
+ bool = 0;
+else
+ bool = 1;
+end
+
+end
diff --git a/src/matlab/descriptor/ScanContext2RingKey.m b/src/matlab/descriptor/ScanContext2RingKey.m
new file mode 100644
index 0000000..f4a478f
--- /dev/null
+++ b/src/matlab/descriptor/ScanContext2RingKey.m
@@ -0,0 +1,14 @@
+function [ ring_key ] = ScanContext2RingKey(sc)
+
+num_rings = size(sc, 1);
+num_sectors = size(sc, 2);
+
+ring_key = zeros(1, num_rings);
+for ith=1:num_rings
+ ith_ring = sc(ith,:);
+ num_zeros_ith_shell = nnz(~ith_ring);
+ ring_key(ith) = (num_sectors - num_zeros_ith_shell)/num_sectors;
+end
+
+end
+
diff --git a/src/matlab/descriptor/XY2Theta.m b/src/matlab/descriptor/XY2Theta.m
new file mode 100644
index 0000000..d2d2d1a
--- /dev/null
+++ b/src/matlab/descriptor/XY2Theta.m
@@ -0,0 +1,17 @@
+function [ theta ] = XY2Theta( x, y )
+
+ if (x >= 0 && y >= 0)
+ theta = 180/pi * atan(y/x);
+ end
+ if (x < 0 && y >= 0)
+ theta = 180 - ((180/pi) * atan(y/(-x)));
+ end
+ if (x < 0 && y < 0)
+ theta = 180 + ((180/pi) * atan(y/x));
+ end
+ if ( x >= 0 && y < 0)
+ theta = 360 - ((180/pi) * atan((-y)/x));
+ end
+
+end
+
diff --git a/src/matlab/matcher/DistanceBtnScanContexts.m b/src/matlab/matcher/DistanceBtnScanContexts.m
new file mode 100644
index 0000000..40d4954
--- /dev/null
+++ b/src/matlab/matcher/DistanceBtnScanContexts.m
@@ -0,0 +1,42 @@
+function [dist] = DistanceBtnScanContexts(sc1,sc2)
+
+num_sectors = size(sc1, 2);
+
+% repeate to move 1 columns
+sim_for_each_cols = zeros(1, num_sectors);
+
+for i = 1:num_sectors
+ %% Shift
+ one_step = 1; % const
+ sc1 = circshift(sc1, one_step, 2); % 2 means columne shift
+
+ %% compare
+ sum_of_cos_sim = 0;
+ num_col_engaged = 0;
+
+ for j = 1:num_sectors
+ col_j_1 = sc1(:,j);
+ col_j_2 = sc2(:,j);
+
+ if( ~any(col_j_1) || ~any(col_j_2))
+ continue;
+ end
+
+ % calc sim
+ cos_similarity = dot(col_j_1, col_j_2) / (norm(col_j_1)*norm(col_j_2));
+ sum_of_cos_sim = sum_of_cos_sim + cos_similarity;
+
+ num_col_engaged = num_col_engaged +1;
+ end
+
+ % devided by num_col_engaged: So, even if there are many columns that are excluded from the calculation, we can get high scores if other columns are well fit.
+ sim_for_each_cols(i) = sum_of_cos_sim/num_col_engaged;
+
+end
+
+sim = max(sim_for_each_cols);
+
+dist = 1 - sim;
+
+end
+
diff --git a/src/matlab/reader/KITTIbin2Ptcloud.m b/src/matlab/reader/KITTIbin2Ptcloud.m
new file mode 100644
index 0000000..b64f2ac
--- /dev/null
+++ b/src/matlab/reader/KITTIbin2Ptcloud.m
@@ -0,0 +1,10 @@
+function ptcloud = KITTIbin2Ptcloud(bin_path)
+
+%% Read
+fid = fopen(bin_path, 'rb'); raw_data = fread(fid, [4 inf], 'single'); fclose(fid);
+points = raw_data(1:3,:)';
+points(:, 3) = points(:, 3) + 1.9; % z in car coord.
+
+ptcloud = pointCloud(points);
+
+end % end of function
diff --git a/src/matlab/reader/NCLTbin2Ptcloud.m b/src/matlab/reader/NCLTbin2Ptcloud.m
new file mode 100644
index 0000000..bb0d7b3
--- /dev/null
+++ b/src/matlab/reader/NCLTbin2Ptcloud.m
@@ -0,0 +1,22 @@
+function Ptcloud = NCLTbin2Ptcloud(BinPath)
+
+%% Hardware Constants
+SCALING = 0.005; % 5mm
+OFFSET = -100.0;
+
+%% Reading
+fidBin = fopen(BinPath, 'r');
+ XYZ_Raw = fread(fidBin, 'uint16');
+ XYZ_Scaled = XYZ_Raw*SCALING + OFFSET;
+fclose(fidBin);
+
+XYZ = reshape(XYZ_Scaled, 4, []);
+XYZ = transpose(XYZ(1:3, :));
+X = XYZ(:, 1);
+Y = XYZ(:, 2);
+Z = -1.*XYZ(:, 3);
+XYZ = [X, Y, Z];
+
+Ptcloud = pointCloud(XYZ);
+
+end % end of function
diff --git a/src/python/Distance_SC.py b/src/python/Distance_SC.py
new file mode 100644
index 0000000..fe8ee99
--- /dev/null
+++ b/src/python/Distance_SC.py
@@ -0,0 +1,72 @@
+import numpy as np
+def distance_sc(sc1, sc2):
+ num_sectors = sc1.shape[1]
+ # repeate to move 1 columns
+ sim_for_each_cols = np.zeros(num_sectors)
+
+ for i in range(num_sectors):
+ # Shift
+ one_step = 1 # const
+ sc1 = np.roll(sc1, one_step, axis=1) # columne shift
+
+ #compare
+ sum_of_cos_sim = 0
+ num_col_engaged = 0
+
+ for j in range(num_sectors):
+ col_j_1 = sc1[:, j]
+ col_j_2 = sc2[:, j]
+
+ if (~np.any(col_j_1) or ~np.any(col_j_2)):
+ continue
+
+ # calc sim
+ cos_similarity = np.dot(col_j_1, col_j_2) / (np.linalg.norm(col_j_1) * np.linalg.norm(col_j_2))
+ sum_of_cos_sim = sum_of_cos_sim + cos_similarity
+
+ num_col_engaged = num_col_engaged + 1
+
+ # devided by num_col_engaged: So, even if there are many columns that are excluded from the calculation, we
+ # can get high scores if other columns are well fit.
+ sim_for_each_cols[i] = sum_of_cos_sim / num_col_engaged
+
+ sim = max(sim_for_each_cols)
+
+ dist = 1 - sim
+
+ return dist
+
+if __name__ == "__main__":
+ from python.make_sc_example import *
+ bin_dir = '../sample_data/KITTI/00/velodyne/'
+ bin_db = kitti_vlp_database(bin_dir)
+ SCs = []
+ for bin_idx in range(bin_db.num_bins):
+ bin_file_name = bin_db.bin_files[bin_idx]
+ bin_path = bin_db.bin_dir + bin_file_name
+
+ sc = ScanContext(bin_dir, bin_file_name)
+
+ # fig_idx = 1
+ # # sc.plot_multiple_sc(fig_idx)
+ #
+ # print(len(sc.SCs))
+
+ SCs.append(sc.SCs[0])
+
+ sc_1a = SCs[0]
+ sc_1b = SCs[1]
+ sc_2a = SCs[2]
+ sc_2b = SCs[3]
+
+ dist_1a_1b = distance_sc(sc_1a, sc_1b)
+ dist_1b_2a = distance_sc(sc_1b, sc_2a)
+ dist_1a_2a = distance_sc(sc_1a, sc_2a)
+ dist_2a_2b = distance_sc(sc_2a, sc_2b)
+
+ print("--------------------")
+ print(dist_1a_1b)
+ print(dist_1b_2a)
+ print(dist_1a_2a)
+ print(dist_2a_2b)
+
diff --git a/src/python/README.md b/src/python/README.md
new file mode 100644
index 0000000..d24cbb0
--- /dev/null
+++ b/src/python/README.md
@@ -0,0 +1,2 @@
+# Practical Python classes of Scan Context for LiDAR SLAM
+- In the [PyICP SLAM](https://github.com/kissb2/PyICP-SLAM) repository, Scan Context Manager class is implemented.
diff --git a/src/python/data/000095.bin b/src/python/data/000095.bin
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index 0000000..75d4dc4
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diff --git a/src/python/make_sc_example.py b/src/python/make_sc_example.py
new file mode 100644
index 0000000..3f7a0c4
--- /dev/null
+++ b/src/python/make_sc_example.py
@@ -0,0 +1,172 @@
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from open3d import *
+
+
+class kitti_vlp_database:
+ def __init__(self, bin_dir):
+ self.bin_dir = bin_dir
+ self.bin_files = os.listdir(bin_dir); self.bin_files.sort()
+
+ self.num_bins = len(self.bin_files)
+
+
+class ScanContext:
+
+ # static variables
+ viz = 0
+
+ downcell_size = 0.5
+
+ kitti_lidar_height = 2.0;
+
+ # sector_res = np.array([45, 90, 180, 360, 720])
+ # ring_res = np.array([10, 20, 40, 80, 160])
+ sector_res = np.array([60])
+ ring_res = np.array([20])
+ max_length = 80
+
+
+ def __init__(self, bin_dir, bin_file_name):
+
+ self.bin_dir = bin_dir
+ self.bin_file_name = bin_file_name
+ self.bin_path = bin_dir + bin_file_name
+
+ self.scancontexts = self.genSCs()
+
+
+ def load_velo_scan(self):
+ scan = np.fromfile(self.bin_path, dtype=np.float32)
+ scan = scan.reshape((-1, 4))
+ ptcloud_xyz = scan[:, :-1]
+
+ return ptcloud_xyz
+
+
+ def xy2theta(self, x, y):
+ if (x >= 0 and y >= 0):
+ theta = 180/np.pi * np.arctan(y/x);
+ if (x < 0 and y >= 0):
+ theta = 180 - ((180/np.pi) * np.arctan(y/(-x)));
+ if (x < 0 and y < 0):
+ theta = 180 + ((180/np.pi) * np.arctan(y/x));
+ if ( x >= 0 and y < 0):
+ theta = 360 - ((180/np.pi) * np.arctan((-y)/x));
+
+ return theta
+
+
+ def pt2rs(self, point, gap_ring, gap_sector, num_ring, num_sector):
+ x = point[0]
+ y = point[1]
+ z = point[2]
+
+ if(x == 0.0):
+ x = 0.001
+ if(y == 0.0):
+ y = 0.001
+
+ theta = self.xy2theta(x, y)
+ faraway = np.sqrt(x*x + y*y)
+
+ idx_ring = np.divmod(faraway, gap_ring)[0]
+ idx_sector = np.divmod(theta, gap_sector)[0]
+
+ if(idx_ring >= num_ring):
+ idx_ring = num_ring-1 # python starts with 0 and ends with N-1
+
+ return int(idx_ring), int(idx_sector)
+
+
+ def ptcloud2sc(self, ptcloud, num_sector, num_ring, max_length):
+
+ num_points = ptcloud.shape[0]
+
+ gap_ring = max_length/num_ring
+ gap_sector = 360/num_sector
+
+ enough_large = 1000
+ sc_storage = np.zeros([enough_large, num_ring, num_sector])
+ sc_counter = np.zeros([num_ring, num_sector])
+
+ for pt_idx in range(num_points):
+
+ point = ptcloud[pt_idx, :]
+ point_height = point[2] + ScanContext.kitti_lidar_height
+
+ idx_ring, idx_sector = self.pt2rs(point, gap_ring, gap_sector, num_ring, num_sector)
+
+ if sc_counter[idx_ring, idx_sector] >= enough_large:
+ continue
+ sc_storage[int(sc_counter[idx_ring, idx_sector]), idx_ring, idx_sector] = point_height
+ sc_counter[idx_ring, idx_sector] = sc_counter[idx_ring, idx_sector] + 1
+
+ sc = np.amax(sc_storage, axis=0)
+
+ return sc
+
+
+ def genSCs(self):
+ ptcloud_xyz = self.load_velo_scan()
+ print("The number of original points: " + str(ptcloud_xyz.shape) )
+
+ pcd = PointCloud()
+ pcd.points = Vector3dVector(ptcloud_xyz)
+ downpcd = voxel_down_sample(pcd, voxel_size = ScanContext.downcell_size)
+ ptcloud_xyz_downed = np.asarray(downpcd.points)
+ print("The number of downsampled points: " + str(ptcloud_xyz_downed.shape) )
+ # draw_geometries([downpcd])
+
+ if(ScanContext.viz):
+ draw_geometries([downpcd])
+
+ self.SCs = []
+ for res in range(len(ScanContext.sector_res)):
+ num_sector = ScanContext.sector_res[res]
+ num_ring = ScanContext.ring_res[res]
+
+ sc = self.ptcloud2sc(ptcloud_xyz_downed, num_sector, num_ring, ScanContext.max_length)
+ self.SCs.append(sc)
+
+
+ def plot_multiple_sc(self, fig_idx=1):
+
+ num_res = len(ScanContext.sector_res)
+
+ fig, axes = plt.subplots(nrows=num_res)
+
+ axes[0].set_title('Scan Contexts with multiple resolutions', fontsize=14)
+ for ax, res in zip(axes, range(num_res)):
+ ax.imshow(self.SCs[res])
+
+ plt.show()
+
+
+if __name__ == "__main__":
+
+ bin_dir = './data/'
+ bin_db = kitti_vlp_database(bin_dir)
+
+ for bin_idx in range(bin_db.num_bins):
+
+ bin_file_name = bin_db.bin_files[bin_idx]
+ bin_path = bin_db.bin_dir + bin_file_name
+
+ sc = ScanContext(bin_dir, bin_file_name)
+
+ fig_idx = 1
+ # sc.plot_multiple_sc(fig_idx)
+
+ print(len(sc.SCs))
+ print(sc.SCs[0].shape)
+
+
+
+
+
+
+
+
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