Research on Train Wheel Point Cloud Registration Algorithm Based on Key Points by Fusing Super-4PCS and ICP

Abstract 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-4PCS(Super Four-Points Congruent Sets) and ICP (Iterative Closest Point) 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 ISS algorithm is employed to extract key points, and the FPFH(Fast Point Feature Histograms) 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 exhibits significant improvements in registration accuracy and robustness for wheel point cloud data compared to the traditional algorithms.