基于信息论的KL-Reg点云配准算法

doi:10.11999/JEIT141248

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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.

Key words： Machine vision Point clouds registration KL-divergence Gaussian mixture model

Received: 25 September 2014

PACS:

TP391

Corresponding Authors: Qin Hong-xing E-mail: qinhx@cqupt.edu.cn

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	Qin Hong-xing
	Xu Lei

Cite this article:

Qin Hong-xing,Xu Lei. Information Theory Based KL-Reg Point Cloud Registration[J]. JEIT, 2015, 37(6): 1520-1524.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT141248 OR http://jeit.ie.ac.cn/EN/Y2015/V37/I6/1520

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