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Auto Rack Girders Assembly Holes Measurement Based on Multi-camera Vision

  • Li-dong Wang
  • Hua Wang
  • Zhi-peng Sun
  • Hang He
  • Shuang Zhang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 503)

Abstract

Since single camera’s visual field is limited, the measurement method for auto rack girders assembly holes based on multi-group of binocular vision is proposed. The measurement area is divided into several subregions, the measurement data of each subregion is obtained from the binocular vision measurement system, and a larger planar target is used to achieve three-dimensional data registration among adjacent subregion. Since the texture information of truck side-member surface is not abundant, it is difficult to seek the match points on the edge of assembly holes. It is proposed that pasting marked points around the edge of assembly holes for seeking match points. Every two marked points can be connected into one line, and the intersections of the lines and assembly holes’ edge are seen as match points. At last, the geometric parameters of spatial circle are fitted according to its geometrical properties. Experimental results show that the matching difficulty will be avoided effectively, the measurement error caused by perspective projection distortion can be reduced, and the method has higher measurement accuracy.

Keywords

Feature points Assembly holes Planar target 

Notes

Acknowledgements

This work was supported by Jilin province science and technology development funding project. The title of the research project: On-line Inspection Key Technology Research for the Train Wheelset Manufacture Quality, and project serial number: 20160204005GX.

References

  1. 1.
    D’Amato R, Caja J, Maresca P et al (2014) Use of coordinate measuring machine to measure angles by geometric characterization of perpendicular planes. Estimating uncertainty. Measurement 47(1):598–606CrossRefGoogle Scholar
  2. 2.
    Zhang YX, Wang S, Zhang XP et al (2013) Freight train gauge-exceeding detection based on three-dimensional stereo vision measurement. Mach Vis Appl 24(3):461–475CrossRefGoogle Scholar
  3. 3.
    Zhang JX et al (1996) Application technology of binocular stereo vision in industrial detection. Tianjin University, TianjinGoogle Scholar
  4. 4.
    Wu B, Xue T, Ye SH et al (2010) A two-step method for spatial circle orientation with a structured light vision sensor and error analysis. Measur Sci Technol 21(7):075105-1–075105-7CrossRefGoogle Scholar
  5. 5.
    Lins RG, Kurka PRG (2013) Architecture for multi-camera vision system for automated measurement of automotive components. In: 7th annual IEEE international systems conference, pp 520–527Google Scholar
  6. 6.
    Zhang Y, Rockett PI (2006) The Bayesian operating point of the Canny edge detector. IEEE Trans Image Process 15(11):3409–3416MathSciNetCrossRefGoogle Scholar
  7. 7.
    Prasad DK, Leung MKH, Quek C et al (2013) ElliFit: an unconstrained, non-iterative, least squares based geometric Ellipse Fitting method. Pattern Recogn: J Pattern Recogn Soc 46(5):1449–1465Google Scholar
  8. 8.
    Li YS, Yang F, Yuan ZK et al (2013) A detection method for 3D circle fitting. Sci Surveying Mapp 38(6):147–148Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Li-dong Wang
    • 1
  • Hua Wang
    • 2
  • Zhi-peng Sun
    • 1
  • Hang He
    • 2
  • Shuang Zhang
    • 2
  1. 1.Engineering Technology DepartmentCRRC Changchun Railway Vehicles Co., Ltd.ChangchunChina
  2. 2.School of Mechatronic EngineeringChangchun Institute of TechnologyChangchunChina

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