Calibration of binocular vision measurement system by line structured light for rail full profile

被引：0
|
作者：
Li W. [1 ]
Wang P. [1 ]
Chen Y. [1 ]
Li B. [1 ]
Hu J. [2 ]
机构：
[1] School of Mechanical Engineering, Southwest Jiaotong University, Chengdu
[2] Liuzhou Engineering Machinery District, Liuzhou
来源：
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2019年 / 40卷 / 03期
关键词：
Binocular calibration; Line structured light vision measurement; Rail full profile; Rodriguez transformation;
D O I：
10.19650/j.cnki.cjsi.J1804500
中图分类号：
学科分类号：
摘要：
The measurement technology of line structured light vision is utilized for the rail wear measurement due to its non-contact and high precision features. The measurement of rail full profile and field calibration of line structured light visual measurement system are two typical problems. To enhance its performance, a binocular calibration method of line structured light based on free plane targets is proposed for field application. Firstly, the vision measurement system is established based on line structured light binocular visual measurement model. The chessboard images containing line structured light and the chessboard images without line structured light at any positions in the public viewing angle of both sides of the camera are collected respectively. Then, the internal parameters of both sides of the camera are obtained by using the chessboard plane calibration method. The feature points which are generated by intersection of line structured light plane and target plane at different positions are used to fit the planar equations of the line structured light plane in both sides of the camera coordinate system. According to the Rodriguez transform principle, the external parameters between the line structured light plane and two cameras are solved. Finally, the internal parameters and the external parameters are utilized to realize the measurement of rail full profile. The field test is also carried out. Experimental results show that the calibration error of the cameras is about 0.03 pixels, the fitting degree of line structured light plane is 0.999, and the total measurement error of rail profile data is 0.54mm, which meets the requirement of measurement accuracy. © 2019, Science Press. All right reserved.
引用
收藏
页码：203 / 211
页数：8
相关论文
共 17 条
  • [1] Zou Y.Y., Zhao M.Y., Zhang L., Direct calibration method of laser stripe vision sensor on gauge block, Chinese Journal of Lasers, 41, 11, pp. 195-200, (2014)
  • [2] Chen T.F., Ma Z., Wu X., Calibration of light plane in line structured light sensor based on active vision, Optics and Precision Engineering, 20, 2, pp. 256-263, (2012)
  • [3] Kiddee P., Fang Z., Tan M., A practical and intuitive calibration technique for cross-line structured light, Optik, 127, 20, pp. 9582-9602, (2016)
  • [4] Zhang X., Zhang J., Summary on calibration method of line-structured light, Laser & Optoelectronics Progress, 55, 2, pp. 7-17, (2018)
  • [5] Sun J.H., Zhang G.J., Liu Q.Z., Et al., Universal method for calibrating structured-light vision sensor on the spot, Journal of Mechanical Engineering, 45, 3, pp. 174-177, (2009)
  • [6] Sun J.H., Wang W.H., Liu Z., Et al., Rail wear measurement method based on structured-light vision, Journal of Beijing University of Aeronautics and Astronautics, 36, 9, pp. 1026-1029, (2010)
  • [7] Yang K., Yu H.Y., Yang C., Calibration of line structured-light vision measurement system based on free-traget, Journal of Mechnical & Electricail Engineering, 33, 9, pp. 1066-1070, (2016)
  • [8] Zhan D., Xiao J., Study on the flexible multiple camera calibration approach based on the reference plane emitted by line structured light, Chinese Journal of Scientific Instrument, 36, 9, pp. 2030-2036, (2015)
  • [9] Zhan D., Yu L., Xiao J., Et al., Study on multi-cameras and structured-light vision system calibration approach study in large filed view measurement, Chinese Journal of Scientific Instrument, 36, 4, pp. 903-912, (2015)
  • [10] Zhang Z.Y., A flexible new technique for camera calibration, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 11, pp. 1330-1334, (2000)
12