Information Theory Based KL-Reg Point Cloud Registration

Qin Hong-xing; Xu Lei

doi:10.11999/JEIT141248

Volume 37 Issue 6

Jun. 2015

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Qin Hong-xing, Xu Lei. Information Theory Based KL-Reg Point Cloud Registration[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1520-1524. doi: 10.11999/JEIT141248

Citation:

Qin Hong-xing, Xu Lei. Information Theory Based KL-Reg Point Cloud Registration[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1520-1524. doi: 10.11999/JEIT141248

Qin Hong-xing, Xu Lei. Information Theory Based KL-Reg Point Cloud Registration[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1520-1524. doi: 10.11999/JEIT141248

Citation:

Qin Hong-xing, Xu Lei. Information Theory Based KL-Reg Point Cloud Registration[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1520-1524. doi: 10.11999/JEIT141248

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Information Theory Based KL-Reg Point Cloud Registration

doi: 10.11999/JEIT141248

Qin Hong-xing¹,
Xu Lei¹

Received Date: 2014-09-25
Rev Recd Date: 2015-02-27
Publish Date: 2015-06-19

Abstract

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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References(22)

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