基于特征匹配的三维点云粗配准方法  

3D Point Cloud Rough Registration Method based on Feature Matching

作　　者：郭克凡 王威娜[2] 林楷 GUO Kefan;WANG Weina;LIN Kai(School of Information and Control Engineering,Jilin Institute of Chemical Technology,Jilin City 132022,China;School of Science,Jilin Institute of Chemical Technology,Jilin City 132022,China;Suzhou Maxwell Technology Co.,Ltd,Suzhou 215200,China)

机构地区：[1]吉林化工学院信息与控制工程学院,吉林吉林132022 [2]吉林化工学院理学院,吉林吉林132022 [3]苏州迈为科技股份有限公司,江苏苏州215200

出　　处：《吉林化工学院学报》2022年第9期37-42,51,共7页Journal of Jilin Institute of Chemical Technology

基　　金：吉林省自然科学基金项目(YDZJ202201ZYTS603)。

摘　　要：针对无初值情况下点云配准精度不高以及配准效率低下的问题,提出一种基于特征匹配的三维点云粗配准方法.首先,利用点云局部法向量的变化描述其特征,加入特征保留权值筛选突出特征信息,提高配准效率;然后,根据保留的特征信息建立特征直方图,通过比较特征直方图描述的信息获取初始匹配的点对;最后,将刚性不变约束与随机抽样一致性算法结合筛选正确的匹配点对,再利用四元素法得到旋转矩阵和平移向量.实验结果表明,提出的算法与其他粗配准算法相比具有较高的精度和效率,为后续的点云精配准工作提供了良好的初值.Aiming at the problems of low accuracy and low efficiency in point cloud registration without any initial value,a rough registration method of 3D point cloud based on feature matching was proposed.Firstly,the local normal vector of the point cloud is used to describe its features,and feature retention weights are added to filter feature information to improve the efficiency of registration;Then feature histograms are built according to the reserved feature information,and the initial matching point pairs are obtained by comparing the information described by the feature histograms;Finally,the rigid invariant constraint and random sample consensus algorithm were combined to screen the correct matching point pairs,and the rotation matrix and translation vector were calculated by the quaternion method.Experimental results show that the proposed algorithm has higher accuracy and efficiency compared with other rough registration algorithms,and provides a good initial value for the subsequent fine point cloud registration.

关 键 词：点云粗配准 特征直方图 随机采样一致性 四元素法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]