Information Theory Based KL-Reg Point Cloud Registration
Qin Hong-xing Xu Lei
(Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)
(College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)
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.
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