3D point cloud registration algorithm with IVCCS

被引：0
|
作者：
Wang C. [1 ,2 ]
Li G. [2 ]
Liu X. [1 ]
Shi C. [2 ]
Qiu W. [2 ]
机构：
[1] College of Optoelectronic Engineering, Xi'an Technological University, Xi′an
[2] College of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun
来源：
Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering | 2022年 / 51卷 / 06期
关键词：
double threshold voxel denoising; point cloud registration; voxel cloud connectivity segmentation; weighted iterative closest point;
D O I：
10.3788/IRLA20210491
中图分类号：
学科分类号：
摘要：
In order to solve the problems of long registration time and low accuracy in the case of data lossing and noise points existing in the traditional iterative closest point (ICP) algorithm, a new registration algorithm based on improved voxel cloud connectivity segmentation (IVCCS) combined with weighted nearest neighbor distance ratio was proposed. Double threshold voxel denoising was used to remove the noise voxel in the initial seed voxel, which was caused by a single constraint in the original voxel cloud connectivity segmentation algorithm (VCCS). Meanwhile, layered voxel cloud denoising was used to speed up the operation speed of registration. The feature points in the point cloud were extracted by flow constrained clustering, and whether the feature points were coincidence points was verified according to the nearest neighbor distance ratio. The minimum objective function of ICP was optimized by giving different weights, so as to accelerate the registration speed.Experimental results show that compared with the traditional ICP algorithm, the algorithm has reduced the number of iterations, and significantly improved the accuracy and speed. Compared with the ICP algorithm based on fast point feature histogram (FPFH), the algorithm has improved the registration accuracy by 8.5%-24.7%, the speed by 65.6%-92.3%, and the number of iterations decrease by 16.6%-38%. © 2022 Chinese Society of Astronautics. All rights reserved.
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