[1] Zhang H B, Ye H J. Researching of inspecting method for wheelsets abrasion based on image processing[J]. Machinery, 31, 51-53(2004).

[2] Feng Q B, Chen S. New method for automatically measuring geometric parameters of wheel sets by laser[J]. Proceedings of SPIE, 5253, 110-113(2003).

[3] Torabi M. Mousavi S G M, Younesian D. A high accuracy imaging and measurement system for wheel diameter inspection of railroad vehicles[J]. IEEE Transactions on Industrial Electronics, 65, 8239-8249(2018).

[4] Chen H, Zhang S J, Gao J et al. Online three-dimensional measurement technology for steel wheel based on rotating scanning of multiple line-laser sensors[J]. Chinese Journal of Lasers, 46, 0704006(2019).

[5] Li M C, Yang Y, Yi B et al. Radius constraint iteratively reweighted least squares circle fitting method and its application in line laser-based wheel tread detection[J]. Chinese Journal of Lasers, 47, 0904005(2020).

[6] Zhang Z F, Lu C, Zhang F Z et al. A Novel Method for non-contact measuring diameter parameters of wheelset based on wavelet analysis[J]. Optik, 123, 433-438(2012).

[7] Zhang Z F, Su Z, Su Y L et al. Denoising of sensor signals for the flange thickness measurement based on wavelet analysis[J]. Optik, 122, 681-686(2011). http://www.sciencedirect.com/science/article/pii/S0030402610002251

[8] Chen X M, Sun J H, Liu Z et al. Dynamic tread wear measurement method for train wheels against vibrations[J]. Applied Optics, 54, 5270-5280(2015).

[9] Xing Z Y, Chen Y J, Wang X H et al. Online detection system for wheel-set size of rail vehicle based on 2D laser displacement sensors[J]. Optik, 127, 1695-1702(2016).

[10] Gao Y, Feng Q B, Cui J Y. A simple method for dynamically measuring the diameters of train wheels using a one-dimensional laser displacement transducer[J]. Optics and Lasers in Engineering, 53, 158-163(2014).

[11] Cheng X Q, Chen Y J, Xing Z Y et al. A novel online detection system for wheelset size in railway transportation[J]. Journal of Sensors, 2016, 9507213(2016).

[12] Besl P J. McKay N D. A method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 239-256(1992).

[13] Hou B, Jin S Z, Wang Y et al. Comparison of point cloud registration methods in coarse registration[J]. Laser & Optoelectronics Progress, 57, 081502(2020).

[14] Wang C, Shu Q, Yang Y X et al. Point cloud registration in multidirectional affine transformation with variance compensation[J]. Acta Optica Sinica, 39, 0215002(2019).

[15] Du S Y, Zheng N N, Xiong L et al. Scaling iterative closest point algorithm for registration of m-D point sets[J]. Journal of Visual Communication and Image Representation, 21, 442-452(2010).

[16] Tang Z R, Liu M Z, Jiang Y et al. Point cloud registration algorithm based on canonical correlation analysis[J]. Chinese Journal of Lasers, 46, 0404006(2019).

[17] A novel method for automatically measuring wheel geometric parameters by laser scanning[J]. Chinese Journal of Lasers, 40, 0708005(2013).

[18] Yan J M, Fu M H[M]. Vehicle engineering, 37-50(2008).

[19] Yang Y, Liu L, Yi B et al. Dynamic inspection of a rail profile under affine distortion based on the reweighted-scaling iterative closest point method[J]. Measurement Science and Technology, 30, 115202(2019).

[20] Yang Y, Liu L, Li M C et al. Sparse scaling iterative closest point for rail profile inspection[J]. Journal of Computing and Information Science in Engineering, 20, 011003(2019).

[21] Charles R Q, Su H, Mo K C et al. PointNet: deep learning on point sets for 3D classification and segmentation. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 77-85(2017).

[22] Bergström P, Edlund O. Robust registration of point sets using iteratively reweighted least squares[J]. Computational Optimization and Applications, 58, 543-561(2014).

[23] Sorkine O, Alexa M. As-rigid-as-possible surface modeling. [C]∥Proceedings of the Fifth Eurographics Symposium on Geometry Processing (SGP), July 4-6, 2007, Barcelona, Spain. Demark: ACM, 109-116(2007).

[24] Arun K S, Huang T S, Machine Intelligence. PAMI-, 9, 698-700(1987).

[25] Yang Y, Liu L, Yi B et al. An accurate and fast method to inspect rail wear based on revised global registration[J]. IEEE Access, 6, 57267-57278(2018).

[26] Huttenlocher D P, Klanderman G A, Rucklidge W J. Comparing images using the Hausdorff distance[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15, 850-863(1993).

[27] Alberto G G, Sergio O E, Sergiu O et al. -08-22) [2020-05-29]. https:∥arxiv.org/pdf/1704.06857.pdf.(2017).