基于关键点特征匹配的点云配准方法  

Point Cloud Registration Method Based on Key Point Extraction with Small Overlap

作　　者：陆军[1] 邵红旭 王伟[1] 范哲君 夏桂华[1] LU Jun;SHAO Hong-xu;WANG Wei;FAN Zhe-jun;XIA Gui-hua(School of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China)

机构地区：[1]哈尔滨工程大学自动化学院,黑龙江哈尔滨150001

出　　处：《北京理工大学学报》2020年第4期409-415,共7页Transactions of Beijing Institute of Technology

基　　金：黑龙江省自然科学基金资助项目(F201123)。

摘　　要：针对ICP配准算法对点云的初始位置要求高、处理低重叠率的点云配准能力低的问题,提出了一种基于关键点特征匹配的点云配准方法.设计一种多尺度加权法向投影均值差的关键点提取算法,结合SHOT描述子对关键点进行特征描述,融合几何一致性以及RANSAC算法去除匹配过程中的误匹配点对,优化关键点之间的对应关系,通过奇异值分解计算刚体变换矩阵,完成点云粗配准,使用ICP进行精确配准.实验表明,本文提出的关键点提取算法能有效提取点云表面特征变化明显的点,使用SHOT特征对关键点进行描述,能够快速、精确地完成点云数据配准,并且对于较低重叠率的点云,也具有较好的配准效果.The ICP registration algorithm has high requirements for the initial position of point clouds and low registration ability for point clouds with low overlapping rate.To solve these problems,a point cloud registration method based on feature matching of key points was proposed.A key point extraction algorithm based on the differences of mean values of multiscale weighted normal projection was designed,and then key points were characterized by SHOT descriptors.Fusing of geometric consistency and RANSAC algorithm,the mismatched point pairs were removed in the matching process,and the correspondences between key points were optimized.The transformation matrix was obtained by using singular value decomposition,cloud rough registration was completed,and fine registration was performed by using ICP.The experiment results show that the key point extraction algorithm proposed in this paper can effectively extract points with obvious changes in the surface of the point cloud.And using SHOT descriptor to characterize the key points can complete registration of point cloud data quickly and accurately.At the same time,for the point cloud with low overlapping rate,the proposed method also has better registration effect.

关 键 词：点云配准 关键点提取 SHOT 低重叠率 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]