Point Cloud Registration Method Based on Improved Covariance Matrix Descriptor

doi:10.16182/j.issn1004731x.joss.22-0010

Abstract

Abstract:

Point cloud registration is a key part of the digital protection of cultural relics. Improving registration accuracy and noise resistance is the main goal of point cloud registration for cultural relics. In order to solve this problem, a three-dimensional (3D) point cloud registration method based on a covariance matrix descriptor is proposed. The tensor voting method is used to eliminate the noise points, and the internal shape signature method is used to extract the key points from the point cloud after removing the noise. Then, the neighborhood information is constructed for the extracted key points, and the covariance matrix descriptor is established by using the information. In addition, the matching point pair is found by calculating the nearest distance, and the angle constraint of the normal vector is used to eliminate the wrong matching point pair. The matching point pair is selected, and the transformation matrix is calculated to complete the rough registration. Then the iterative nearest point method is used for the fine registration. Experimental results show that compared with common registration algorithms, the algorithm proposed in this paper has higher registration accuracy and is suitable for models with low overlap rates and noisy models.

Key words: point cloud registration, covariance matrix descriptor, key points, tensor voting, intrinsic shape signature

CLC Number:

TP391.9

Yuan Zhang, Haoyu Han, Xie Han, Jiaxu Fu. Point Cloud Registration Method Based on Improved Covariance Matrix Descriptor[J]. Journal of System Simulation, 2023, 35(5): 979-986.

Figures/Tables 9

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References 20

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点云模型	Go-ICP	RANSAC+ICP	文献[1]方法	本文方法
斯坦福bun045 and bun090	0.010 6	0.009 8	0.007 1	0.006 4
斯坦福bun000 and bun090	0.113 0	0.057 5	0.021 3	0.010 7
斯坦福dragon000 and dragon048	0.005 4	0.005 3	0.005 1	0.004 4
青铜鸟尊	1.05	0.93	0.71	0.55
唐三彩模型	1.97	1.51	1.46	1.38

点云模型	Go-ICP	RANSAC+ICP	文献[1]方法	本文方法
斯坦福bun045 and bun090	73.24	69.81	128.63	57.85
斯坦福bun000 and bun090	93.31	63.27	127.19	57.99
斯坦福dragon000 and dragon048	81.57	55.63	125.74	51.54
青铜鸟尊	72.05	63.35	126.48	53.31
唐三彩模型	113.15	89.23	163.95	77.06

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