Point Cloud Registration Method Based on Improved Covariance Matrix Descriptor

Authors

Yuan Zhang, 1.Computer Science and Technology Department, North University of China, Taiyuan 030051, China;2.Shanxi Provincial Key Laboratory of Machine Vision and Virtual Reality, Taiyuan 030051, China;3.Shanxi Province Visual Information Processing and Intelligent Robot Engineering Research Center, Taiyuan 030051, China;Follow
Haoyu Han, 1.Computer Science and Technology Department, North University of China, Taiyuan 030051, China;2.Shanxi Provincial Key Laboratory of Machine Vision and Virtual Reality, Taiyuan 030051, China;3.Shanxi Province Visual Information Processing and Intelligent Robot Engineering Research Center, Taiyuan 030051, China;Follow
Xie Han, 1.Computer Science and Technology Department, North University of China, Taiyuan 030051, China;2.Shanxi Provincial Key Laboratory of Machine Vision and Virtual Reality, Taiyuan 030051, China;3.Shanxi Province Visual Information Processing and Intelligent Robot Engineering Research Center, Taiyuan 030051, China;
Jiaxu Fu, 1.Computer Science and Technology Department, North University of China, Taiyuan 030051, China;2.Shanxi Provincial Key Laboratory of Machine Vision and Virtual Reality, Taiyuan 030051, China;3.Shanxi Province Visual Information Processing and Intelligent Robot Engineering Research Center, Taiyuan 030051, China;

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.

Recommended Citation

Zhang, Yuan; Han, Haoyu; Han, Xie; and Fu, Jiaxu (2023) "Point Cloud Registration Method Based on Improved Covariance Matrix Descriptor," Journal of System Simulation: Vol. 35: Iss. 5, Article 7.
DOI: 10.16182/j.issn1004731x.joss.22-0010
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss5/7

First Page

979

Revised Date

2022-02-28

DOI Link

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

Last Page

986

CLC

TP391.9

Recommended Citation

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.

Corresponding Author

Haoyu Han,985811696@qq.com

DOI

10.16182/j.issn1004731x.joss.22-0010