一种基于关键点选择的快速点云配准算法  

A Fast Point Cloud Registration Algorithm Based on Key Point Selection

作　　者：张哲 许宏丽[1] 尹辉[1] 

机构地区：[1]北京交通大学计算机与信息技术学院,北京100044

出　　处：《激光与光电子学进展》2017年第12期149-157,共9页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(61472029;61473031);科技部国家重点研发计划(2016YFB1200100);北京市自然科学基金(4152042);中央高校基本科研业务费专项资金(2016JBZ005;2016JBM019;2016JBM016)

摘　　要：为了提高三维点云数据配准的效率,提出一种基于法向量分布特征的关键点初始匹配与迭代最近点(ICP)的精确配准的两步点云配准算法。首先,定义点云的邻接区域和法向量分布特征计算模型,提出基于该模型的关键点选择算法;其次,为每个关键点建立局部坐标系,计算关键点的快速点特征直方图,使用采样一致性配准算法匹配关键点的特征,去除错误匹配点,求解出变换矩阵,完成初始配准;最后,使用ICP算法,对多视点云的初始配准结果进行精确配准。实验结果表明,在散乱点云数据和自获取的深度点云数据配准中,该算法能够在确保配准精度的同时有效提升配准效率。In order to improve the registration efficiency of three-dimensional point cloud, a two-step point cloud registration algorithm is proposed based on the key point initial matching using the normal vector distribution feature and the accurate registration using the iterative closest point （ICP）. Firstly, the definition of the adjacency region and the normal vector distribution feature model of point cloud are presented, and a key point selection algorithm is proposed based on the model. Secondly, the fast point feature histograms of key points are calculated using the local coordinate system, and the false matches are eliminated by the sampling conformance registration algorithm. According to the corresponding relation, the rotation and translation matrices are calculated and the initial registration is completed. Finally, the final registration result is obtained using ICP algorithm. The experimental results show that the proposed algorithm can effectively improve the registration efficiency while ensuring the accuracy of the registration in the data of unordered point cloud and the self-acquired depth point cloud.

关 键 词：图像处理 点云配准 关键点 快速点特征直方图 迭代最近点算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]