Laser & Optoelectronics Progress, Volume. 58, Issue 4, 0415008(2021)
Registration and Optimization Algorithm of Key Points in Three-Dimensional Point Cloud
In the traditional three-dimensional (3D) point cloud registration process, there are some problems such as high registration error, large amount of calculation and time-consuming. Aiming at these problems, a registration and optimization algorithm of key points in 3D point cloud is proposed in this paper. In the key point selection stage, the edge point detection algorithm is proposed to eliminate the edge points, improve the comprehensiveness and repeatability of the feature description of key points, and reduce the registration error of 3D point cloud. In the 3D point cloud registration stage, K-dimensional tree (KD-tree) accelerated nearest neighbor algorithm and iterative nearest point algorithm are used to eliminate key misregistration points in the coarse registration results, reduce the registration errors, and improve the speed and accuracy of 3D point cloud registration. Experimental results show that the algorithm can obtain good registration results under different cloud data. Compared with the traditional 3D point cloud registration algorithm, the average registration rate and the average registration accuracy of the algorithm are improved by 68.725% and 49.65%, respectively.
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Tao Song, Libo Cao, Mingfu Zhao, Shuai Liu, Yuhang Luo, Xin Yang. Registration and Optimization Algorithm of Key Points in Three-Dimensional Point Cloud[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0415008
Category: Machine Vision
Received: Jul. 4, 2020
Accepted: Aug. 13, 2020
Published Online: Feb. 22, 2021
The Author Email: Song Tao (easton.cao@foxmail.com), Cao Libo (easton.cao@foxmail.com)