基于K-means++的多视图点云配准技术

针对大规模点集可能存在噪声、离群点及遮挡等情况，提出一种基于K-means+〖KG-*3〗+的多视图点云配准方法。首先，利用K-means+〖KG-*3〗+算法的随机播种技术对下采样后的多视图点集选取初始化的质心，并根据算法的基本原理完成聚类；其次，将点云数据存入K-D树结构，并利用最近邻搜索算法建立点集间的对应关系，从而提升对应点集的搜索效率；最后，通过迭代最近点算法依照扫描顺序计算各视图聚类得到的点云数据与所有视图间的刚性变换参数，将成对配准造成的误差均匀扩散到每个视图中，直至获得最终配准结果。在Stanford三维点云数据集上进行实验的结果表明，本文提出的方法比近年的部分多视图配准算法具有更高的配准精度及鲁棒性。

Multi-view Point Cloud Registration Technology Based on K-means++

A multi-view point cloud registration method based on K-means++is proposed for the possibility of noise,outliers and occlusion in large scale point sets.Firstly,the random seeding technique of K-means++algorithm is used to select the initialized center of mass from the subsampled multi-view point sets,and the clustering is completed according to the basic principle of the algorithm.Secondly,the point cloud data are stored in the K-D tree structure,and the nearest neighbor search algorithm is used to establish the corresponding relationship between the point sets,so as to improve the search efficiency of the corresponding point sets.Finally,the rigid transformation parameters between the point cloud data obtained by the clustering of each view and all views are calculated according to the scanning sequence by the iterative closest point algorithm,and the errors caused by pairwise registration are evenly spread to each view until the final registration result is obtained.Experiments on Stanford 3D point cloud datasets show that the proposed method has higher registration accuracy and robustness than partial multi-view registration algorithms in recent years.