提出了一种基于典型相关分析的点云配准算法。对目标点云和待配准点云进行中心化处理,将其绕坐标原点进行转动,两组点云满足各维度间相关系数平方值最大;采用典型相关分析法,对两组转动矩阵进行求解;使用转动矩阵,求解两点云间刚性变换的旋转矩阵和平移向量,实现了点云的配准。利用协方差矩阵特征值的比例开方值,对待配准点云进行等比例放大,完成了仿射配准。当点云无序、数据存在遮挡、缺失不完整、放缩及有噪声干扰时,仿真结果表明,相比于其他几种算法,所提算法能快速精确配准,且稳定性良好。

A point cloud registration algorithm based on canonical correlation analysis is proposed. We centralize the target point cloud and the point cloud to be registered, and rotate it around the coordinate origin. The two sets of point clouds can satisfy the maximum square of the correlation coefficient between the dimensions. The two sets of rotation matrices are solved by typical correlation analysis method. The rotation matrix and the translation vector of the rigid transformation between the two points of the clouds are solved by the rotation matrix, and the registration of the point cloud is realized. We use the proportional square value of the eigenvalues of the covariance matrix to scale the registration point cloud proportionally, and complete the affine registration. The simulation results show that, compared with several other algorithms, the proposed algorithm can be quickly and accurately registered with good stability, when point clouds are out of order, occluded, missing, size scaling and interrupted by noise.