激光与光电子学进展, 2019, 56 (22): 221504, 网络出版: 2019-11-02
基于相关系数平方和最大的三维点云配准 下载: 972次
Three-Dimensional Point Cloud Registration Based on Maximum Sum of Squares of Correlation Coefficients
机器视觉 点云配准 相关系数 粒子群优化 数据缺失 machine vision point cloud registration correlation coefficient particle swarm optimization data missing
摘要
点云配准是三维重建过程的核心问题之一。针对点云散乱、数据存在缺失及噪声干扰情况下配准效率差、精度低的问题,提出了一种基于相关系数平方和最大(MCC)的点云配准算法。分别对目标点云与待配准点云去均值化后进行旋转,使旋转后的两组点云各自满足行向量间相关系数平方和最大;再利用粒子群优化算法分别求解出两组中间态旋转矩阵;最后根据两组中间态旋转矩阵求解出两点云之间的旋转矩阵和平移向量,进而实现点云配准。仿真结果表明,在点云散乱、数据存在缺失以及噪声干扰的情况下,本文算法比现有其他算法的配准速度更快、精度更高,且稳健性良好。
Abstract
Point cloud registration is a fundamental element of the three-dimensional reconstruction processes. In this study, a point cloud registration algorithm is proposed based on the maximum sum of squares of the correlation coefficients (MCC) to address the issues of scattered point clouds, missing data, and low registration efficiency and accuracy under noise interference. Further, the target point cloud and the point cloud to be registered are de-averaged and rotated, so that the MCC between row vectors of the two sets of point clouds can be achieved after rotation. Subsequently, particle swarm optimization algorithm is used to derive two sets of intermediate-state rotation matrices. Finally, based on these matrix sets, the rotation matrix and translation vector between two point clouds are obtained for registering the point cloud. The simulation results show that the proposed algorithm is faster, more accurate, and more robust compared with the remaining existing algorithms when point clouds are scattered, missing, and interrupted by noise.
苗长伟, 唐志荣, 唐英杰. 基于相关系数平方和最大的三维点云配准[J]. 激光与光电子学进展, 2019, 56(22): 221504. Changwei Miao, Zhirong Tang, Yingjie Tang. Three-Dimensional Point Cloud Registration Based on Maximum Sum of Squares of Correlation Coefficients[J]. Laser & Optoelectronics Progress, 2019, 56(22): 221504.