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# scan context
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# Scan Context: Egocentric Spatial Descriptor for Place Recognition within 3D Point Cloud Map
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**2018 IROS Giseop Kim and Ayoung Kim**
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## Background
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- 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
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## challenge
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- 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
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## Framework
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![](http://www.write-bug.com/myres/static/uploads/2021/10/19/8a17e34d3f6faf54ea8c0e47e6ba9172.writebug)
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## scan-context
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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。
![](http://www.write-bug.com/myres/static/uploads/2021/10/19/599a52d6cffd7c2004f900720e2cc849.writebug)
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## Similarity Score between Scan Contexts
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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。
![](http://www.write-bug.com/myres/static/uploads/2021/10/19/8d44cf044dc2a086b4d8f318b96bdf9d.writebug)
## Two-phase Search Algorithm
- 利用ring key 构造KD—Tree后最近邻检索
![](http://www.write-bug.com/myres/static/uploads/2021/10/19/5d858d0b4b53d0163f0833203c678591.writebug)
- 相似度评分
- 找到闭环对应帧后使用ICP

© 2021 WRITE BUG

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© 2021 WRITE BUG
备案号: 京ICP备16025663号-5