基于主成分分析和特征图匹配的点云配准方法  

Point cloud registration method based on principal component analysis and feature map matching

作　　者：郑伟斌 练国富 张学明 郭方 ZHENG Weibin;LIAN Guofu;ZHANG Xueming;GUO Fang(School of Mechanical and Automotive Engineering,Fujian University of Technology,Fuzhou 350118,China;School of Computer Science and Mathematics,Fujian University of Technology,Fuzhou 350118,China)

机构地区：[1]福建理工大学机械与汽车工程学院,福建福州350118 [2]福建理工大学计算机科学与数学学院,福建福州350118

出　　处：《智能科学与技术学报》2023年第4期543-552,共10页Chinese Journal of Intelligent Science and Technology

基　　金：福建省科技重大专项专题项目(No.2020HZ03018)。

摘　　要：由于点云模型存在不同程度的重叠,点云配准容易出现特征匹配错误、配准难度大等问题。因此,提出了一种基于主成分分析和特征图匹配的点云配准方法。配准前,首先采用带主轴校正的主成分分析方法进行点云初始位姿调整,建立KD树进行重叠区域搜索。其次,根据两幅点云的重叠区域计算采样点的快速点特征直方图特征,进行点云特征图匹配以及裁剪迭代最近点精配准。在现有数据集以及实际扫描模型上进行配准实验,实验结果表明该方法的稳定性好,精度更高,相较于其他算法精度能提高25%以上。Due to varying degrees of overlap in point cloud models,point cloud registration is prone to problems,such as feature matching errors and high difficulty in registration.Therefore,a point cloud registration method based on principal component analysis and feature map matching is proposed.Before registration,the principal component analysis method with spindle correction was used to adjust the initial pose,then the K-dimensional tree was established to search the overlapping area.Secondly,the fast point feature histograms features of the sampling points were calculated according to the overlapping area of the two-point cloud,and the point cloud feature graph matching and trimmed iterative closest point(TrICP)fine registration were performed.Registration experiments were carried out according to the existing datasets and the actual scanning model.The experimental results show that the method has good stability and higher accuracy,and the accuracy can be improved by more than 25%compared with other algorithms.

关 键 词：重叠区域 KD树 图匹配 裁剪迭代最近点 点云配准 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]