Dynamic inspection of a rail profile under affine distortion based on the reweighted-scaling iterative closest point method

The precision and efficiency of rail inspection are of vital importance to the operation and safety of railway systems. However, because of vibrations that occur in the rail inspection vehicle and installation error of the line laser sensor, the collected rail profile is distorted. As a result, the accuracy and robustness of conventional registration and inspection methods cannot be guaranteed. In this paper, an accurate and robust calibration method for measuring rail profile distortion is proposed. In this study, the impact of vibration on the rail profile, which was obtained with a laser sensor, was analyzed. Then, the collected rail profile was divided into two segments according to the actual condition where only the upper part of the rail was subjected to wear. An accurate and robust reweighted-scaling iterative closest point algorithm was proposed to calibrate the distorted rail profile, and the upper and lower boundaries of the scale ratio were used as constraints to solve the registration problem effectively and efficiently. Finally, the Hausdorff distance method was introduced to clearly visualize the rectified rail profile and wear. The experimental results demonstrated that the proposed method can realize the affine transformation of the distorted rail profile with a high precision and robustness. Additionally, it can achieve an online dynamic calibration and inspection of the rail by comparing the data with the conventional iterative closest point, scaling iterative closest point, and Calipri. (paper)