Xiao Qian, Gao Xueshan, Zhang Zhi, Zhang Yanjie, Xu Zhongxu, Shi Kaizhi
Railway Industry Key Laboratory of Intelligent Operation and Maintenance of Rolling stock, EastChina Jiaotong University, Nanchang, 330013, China.
China Railway Nanchang Group Co. Ltd., Nanchang, 330002, China.
Sci Rep. 2025 Sep 1;15(1):32156. doi: 10.1038/s41598-025-18099-3.
Wheels are critical components of railway vehicles, and the dynamic measurement of wheel parameters is of paramount importance for the safe operation of trains.To enhance the matching accuracy in the existing dynamic measurement processes for train wheel parameters, this paper proposes an improved point cloud registration algorithm based on key point fusion of the Super Four-Points Congruent Sets (Super-4PCS) and Iterative Closest Point (ICP) algorithm. Firstly, point cloud filtering and normal estimation are performed on the wheel point cloud data to obtain source and target point clouds with normal information. Subsequently, the Intrinsic Shape Signatures (ISS) algorithm is employed to extract key points, and the Fast Point Feature Histograms (FPFH) point cloud feature descriptor is utilized to characterize the extracted key points. Then, a two-level registration strategy is used to improve registration accuracy, in which the Super-4PCS algorithm is applied for primary coarse registration and the ICP algorithm is used for the secondary fine registration, respectively. Finally, the experiment is conducted to validate the proposed algorithm and the performance of the algorithm is further comparative analyzed through the listed registration evaluation metrics. Experimental results demonstrate that the proposed algorithm significantly improves registration accuracy and robustness for wheel point cloud data, with the Root Mean Square Error (RMSE) reduced from 0.0631 to 0.0002, and the Mean Absolute Error (MAE) reduced from 0.0671 to 0.00026, compared to traditional algorithms. However, the algorithm's performance is sensitive to point cloud density and noise levels, and its effectiveness may vary under different environmental conditions.
车轮是铁路车辆的关键部件，车轮参数的动态测量对于列车的安全运行至关重要。为了提高现有列车车轮参数动态测量过程中的匹配精度，本文提出了一种基于超级四点全等集（Super-4PCS）关键点融合和迭代最近点（ICP）算法的改进点云配准算法。首先，对车轮点云数据进行点云滤波和法向量估计，以获得带有法向量信息的源点云和目标点云。随后，采用内在形状签名（ISS）算法提取关键点，并利用快速点特征直方图（FPFH）点云特征描述符对提取的关键点进行表征。然后，采用两级配准策略提高配准精度，其中分别应用Super-4PCS算法进行初次粗配准，ICP算法进行二次精配准。最后，进行实验验证所提算法，并通过列出的配准评估指标对算法性能进行进一步的对比分析。实验结果表明，与传统算法相比，所提算法显著提高了车轮点云数据的配准精度和鲁棒性，均方根误差（RMSE）从0.0631降至0.0002，平均绝对误差（MAE）从0.0671降至0.00026。然而，该算法的性能对点云密度和噪声水平敏感，在不同环境条件下其有效性可能会有所不同。
