The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지
DOI QR코드

DOI QR Code

Rail Profile Matching Method using ICP Algorithm

ICP 알고리즘을 이용한 레일 프로파일 매칭 기법

  • Received : 2015.01.29
  • Accepted : 2016.04.07
  • Published : 2016.05.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we describe a method for precisely measuring the abrasion of the railway using an image processing technique. To calculate the wear of the rails, we provided a method for accurately matching the standard rail profile data and the profile data acquired by the rail inspection vehicle. After the lens distortion correction and the perspective transformation of the measured profile data, we used ICP Algorithm for accurate profile data matching with the reference profile extracted from the standard rail drawing. We constructed the prototype of the Rail Profile Measurement System for High-speed Railway and the experimental result on the three type of the standard rail used in Korea showed the excellent profile matching accuracy within 0.1mm.

Keywords

References

  1. Sun, Y., Zhou, G. H., & Zhao, L. C. (2000). Fast template matching algorithm based on the projection. JOURNALSHANGHAI JIAOTONG UNIVERSITY-CHINESE EDITION-, 34(5), 702-704.
  2. Yu, Z.X., Chen, L., Chen, H.P. and Zhang, X.L. (2013) A Method Integrated Multi-Feature for Recognizing Fire Based on Video Image Analysis. Industrial Control Computer, 26, 85-87.
  3. Yoneyama, S., Kikuta, H., Kitagawa, A., & Kitamura, K. (2006). Lens distortion correction for digital image correlation by measuring rigid body displacement. Optical engineering, 45(2), 023602-023602. https://doi.org/10.1117/1.2168411
  4. Jagannathan, L., & Jawahar, C. V. (2005). Perspective correction methods for camera based document analysis. In Proc. First Int. Workshop on Camera-based Document Analysis and Recognition (pp. 148-154).
  5. Suzuki, K., Horiba, I., & Sugie, N. (2003). Linear-time connected-component labeling based on sequential local operations. Computer Vision and Image Understanding, 89(1), 1-23. https://doi.org/10.1016/S1077-3142(02)00030-9
  6. Chetverikov, D., Svirko, D., Stepanov, D., & Krsek, P. (2002). The trimmed iterative closest point algorithm. In Pattern Recognition, 2002. Proceedings. 16th International Conference on (Vol. 3, pp. 545-548). IEEE.
  7. Minguez, J., Montesano, L., & Lamiraux, F. (2006). Metric-based iterative closest point scan matching for sensor displacement estimation. Robotics, IEEE Transactions on, 22(5), 1047-1054. https://doi.org/10.1109/TRO.2006.878961
  8. Kaneko, S. I., Kondo, T., & Miyamoto, A. (2003). Robust matching of 3D contours using iterative closest point algorithm improved by M-estimation. Pattern Recognition, 36(9), 2041-2047. https://doi.org/10.1016/S0031-3203(03)00050-5
  9. Nuchter, A., Lingemann, K., & Hertzberg, J. (2007, August). Cached kd tree search for ICP algorithms. In 3-D Digital Imaging and Modeling, 2007. 3DIM'07. Sixth International Conference on (pp. 419-426). IEEE.
  10. Greenspan, M., & Yurick, M. (2003, October). Approximate kd tree search for efficient ICP. In 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings. Fourth International Conference on (pp. 442-448). IEEE.
  11. Xu, Q., Chai, X., Zheng, S., Zhu, W., & Zhang, L. (2014). Template Matching for Profile Measurement Based on Levenberg-Marquardt Algorithm. American Journal of Industrial and Business Management, 4(07), 370. https://doi.org/10.4236/ajibm.2014.47045