提出一种针对三维点云的快速全局配准算法,用于估计两组相似点云在空间中的刚性位姿变化关系。首先通过计算两组点云的三个主成分向量,配合各自中心点形成自身主成分(PC)坐标系。然后对两组点云分别进行坐标系转换,再利用最近点的欧氏距离均值校正对应PC坐标轴的方向,得到两组相似点云的大致位姿变化关系。经过上述粗配准后利用快速迭代最近点(ICP)算法,即可实现任意位姿关系下两组点云的快速精确配准。实验结果表明,该方法对任意两组形状和完整度相似的点云都可以实现任意位姿下的全局配准,并且具有较高的速度与精确度。

A fast global registration algorithm for 3D point cloud is proposed, which is used to estimate the rigid pose relationship of any two sets of similar point clouds in space. First, the three principal component vectors of two groups of point clouds are calculated to form their own principal component (PC) coordinate systems with their respective center points. Then, in order to obtain the approximate pose relationship between the two similar groups of point clouds, the coordinate transformations of the two groups of point clouds are respectively performed, and the directions corresponding to the PC coordinate axes are corrected by the mean of Euclidean distances of some close points. After the above coarse registration, the two groups of point clouds can be matched fast and accurately at arbitrary position by the fast iterative closest point (ICP) algorithm. The experimental results show that the proposed method can achieve global registration for any two sets of point clouds with similar shape and completeness at any position in any pose, and has higher speed and accuracy.