摘要
传统三维(3D)点云配准过程中存在配准误差高、计算量大及耗时长等问题,针对该问题,提出了一种3D点云中关键点的配准与优化算法。在关键点选取阶段,用边缘点检测算法剔除边缘关键点,以提高关键点特征描述的全面性和重复性,降低3D点云配准误差。在3D点云配准阶段,用K-维树(KD-tree)加速的最近邻算法和迭代最近点算法剔除粗配准结果中的误配准关键点,降低配准误差,提高3D点云配准的速度与精度。实验结果表明,本算法在不同点云数据下,均能获得良好的配准结果。与传统3D点云配准算法相比,本算法的平均配准速率提高了68.725%,平均配准精度提高了49.65%。
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.
作者
宋涛
曹利波
赵明富
刘帅
罗宇航
杨鑫
Song Tao;Cao Libo;Zhao Mingfu;Liu Shuai;Luo Yuhang;Yang Xin(College of Electrical and Electronic Engineering,Chongqing University of Technology,Chongqing 400054,China;Elevator Intelligent Operation and Maintenance Chongqing Universities Engineering Center,Chongqing 402260,China;Optical Fiber Sensing and Photoelectric Detection Chongqing Key Laboratory,Chongqing 400054,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2021年第4期367-375,共9页
Laser & Optoelectronics Progress
基金
国家自然科学基金青年科学基金(61701056)
重庆市教委基础研究项目(KJQN201901123)
重庆市科技局技术创新与应用发展重点项目(cstc2019jscx-mbdxX0002)
重庆理工大学研究生创新基金(ycx20192051,ycx20192052)。
关键词
图像处理
关键点检测
边缘检测
三维重建
点云处理
误差优化
image processing
key point detection
edge detection
three-dimensional reconstruction
point cloud processing
error optimization
