基于信息论的KL-Reg点云配准算法

秦红星; 徐雷

doi:10.11999/JEIT141248

基于信息论的KL-Reg点云配准算法

doi: 10.11999/JEIT141248

秦红星¹,
徐雷¹

基金项目:

国家自然科学基金青年科学基金(61100113)，国家教育部留学归国基金教外司留[2012]940号，重庆市首批青年骨干教师项目(渝教人(2011)31号)，重庆市基础与前沿研究计划项目(cstc2013jcyjA 40062)，重庆邮电大学学科引进人才基金(A2010-12)和重庆市研究生科研创新项目(CYS14142)资助课题

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出版历程
- 收稿日期: 2014-09-25
- 修回日期: 2015-02-27
- 刊出日期: 2015-06-19

Information Theory Based KL-Reg Point Cloud Registration

Qin Hong-xing¹,
Xu Lei¹

摘要

摘要: 针对含有高噪声、体外点及不完整点云数据的配准失效问题，该文提出以信息论为理论基础，相对熵度量点云相似度的KL-Reg算法。该算法不需要显式地建立对应关系，首先将点云数据建模为高斯混合模型，然后用相对熵度量高斯混合模型间的分布距离，最后通过最小化分布距离计算模型变换。实验结果表明所提的KL-Reg算法配准精度高、稳定性强。
- 机器视觉 /
- 点云配准 /
- KL散度 /
- 高斯混合模型
Abstract: The registration of point clouds with high noises, outliers and missing data will be failure because the correspondence between point clouds is inaccurate. This paper proposes a information theory based point cloud registration method called KL-Reg algorithm without building correspondence. The method represents the point cloud with Gaussian mixture model, then computes the transformation through minimizing the KL divergence without build explicit correspondence. Experimental results show that KL-Reg algorithm is precise and stable.
- Machine vision /
- Point clouds registration /
- KL-divergence /
- Gaussian mixture model

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参考文献(22)

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