电子测量与仪器学报

傅瑶,陈鹏,郭贵松,刘雪垠.基于 4PCS 和 SICP 的点云配准方法在钢轨磨耗计算中的应用[J].电子测量与仪器学报,2022,36(12):210-218

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 on4PCS and SICP in rail wear calculation

DOI：

英文关键词:railway track rail wear inspection point cloud registration point cloud noise 4PCS SICP

基金项目:四川省科技计划项目（2021ZHYZ0019，2021YFG0194，2022YFS0021）资助

作者	单位
傅瑶	1. 西南交通大学机械工程学院，2. 国家知识产权局专利局专利审查协作四川中心
陈鹏	1. 西南交通大学机械工程学院
郭贵松	1. 西南交通大学机械工程学院，3. 中山大学航空航天学院
刘雪垠	1. 西南交通大学机械工程学院，4. 四川省机械研究设计院(集团)有限公司

Author	Institution
Fu Yao	1. School of Mechanical Engineering, Southwest Jiaotong University，2. Sichuan Center for Patent Examination Cooperation of the Patent Office of the State Intellectual Property Office
Chen Peng	1. School of Mechanical Engineering, Southwest Jiaotong University
Guo Guisong	1. School of Mechanical Engineering, Southwest Jiaotong University，3. School of Aeronautics and Astronautics, Sun Yat-Sen University
Liu Xueyin	1. School of Mechanical Engineering, Southwest Jiaotong University，4. Sichuan Provincial Machinery Research & Design Institute(Group) Co. , Ltd.

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中文摘要:

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。

英文摘要:

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.

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