基于4PCS和SICP的点云配准方法在钢轨磨耗计算中的应用

Application of the point cloud registration method based on 4PCS and SICP in rail wear calculation

针对基于三维结构光扫描的钢轨磨耗快速精确测量,本文提出了一种基于4PCS(4-points congruent sets)和SICP(sparse iterative closest point)的点云配准组合算法,用于快速精确配准不完整且含噪声的磨耗钢轨与标准钢轨点云。由于三维结构光设备一次扫描得到的磨耗钢轨数据是不完整且含噪声的,因此首先利用针对低重叠率点云配准鲁棒性较好的4PCS对钢轨点云进行粗配准,为精确配准提供较好的初始变换矩阵。然后,再利用针对含噪声点云配准鲁棒性较好的SICP进行精确配准。最后,根据精确配准结果计算出轨头磨耗。文中定量分析了不同程度降采样对配准精度、时间及轨头磨耗计算精度的影响,展现了4PCS+SICP在快速精确配准不完整且含噪声的钢轨点云的优越性,得出了不同程度降采样对轨头磨耗计算精度无影响的结论。与此同时,对钢轨点云含不同程度的噪声点云配准做了定量对比分析,验证了SICP在含噪声的磨耗钢轨点云精确配准中的鲁棒性。

Aiming at the fast and accurate measurement of rail wear based on 3D structured light scanning,this paper proposes a point cloud registration algorithm based on 4PCS(4-points congruent sets)and SICP(sparse iterative closest point),which is used to quickly and accurately register the standard rail point cloud and incomplete worn rail point clouds with noise.Since the wear rail data obtained by one-time scanning of the three-dimensional structured light scanner is usually incomplete and contains noise,4PCS with good robustness for low overlap point cloud registration is firstly used to coarse registration of the rail point cloud,which provides a good initial transformation matrix for accurate registration.Then,the SICP with good robustness for noisy point cloud registration is used for accurate registration.Finally,the rail head wear is calculated according to the accurate registration results.It quantitatively analyzes the influence of different levels of down-sampling on registration accuracy,time and calculation accuracy of rail head wear,which demonstrates the advantage of 4PCS and SICP in fast and accurate registration of incomplete and noisy rail point clouds.It is concluded that different levels of down-sampling have no influence on the calculation accuracy of rail head wear.Meanwhile,the robustness of SICP in accurate registration of worn rail point cloud with noise is verified by quantitative comparison analysis of the point cloud registrations with different levels of noise.