Enhanced phase measurement profilometry for industrial 3D inspection automation

ORIGINAL ARTICLE

Abstract

Industrial metrology and inspection systems commonly rely on phase measurement profilometry (PMP) using sinusoidal fringe patterns projecting, yielding dense, and accurate 3D reconstruction regardless of the presence of texture. However, applying PMP method to industrial 3D inspection is still a big challenging problem due to rigorous industrial measurement conditions including large surface reflectivity variation range and vibration. Aiming to solve these problems, an enhanced phase measurement profilometry (EPMP) is proposed. In EPMP, an optimal exposure time (OET) calibration method is proposed to solve large surface reflectivity variation range problem, and it can avoid saturating the camera sensor in areas of specular reflection while keep the signal-to-noise ratio (SNR) of fringe image in areas of weak reflection at most. To resist the influence of vibration, an improved pose calibration method (IPC) is used to allow fast calibration of pose of cameras by acquiring only one image of planar target. Moreover, an automatic online 3D inspection system for evaluating 3D geometric dimension quality of railway truck adapter (RTA) is developed, and according to the experiments, the EPMP indicates a satisfactory result in accuracy and repeatability, which can meet the requirements of the 3D inspection task in industrial measurement conditions.

Keywords

Automatic industrial 3D inspection Phase measurement profilometry Large surface reflectivity variation Optimal exposure time Pose calibration 

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References

  1. 1.
    Sansoni G, Trebeschi M, Docchio F (2009) State-of-the-art and applications of 3D imaging sensors in industry, cultural heritage, medicine, and criminal investigation. Sensors 9:568–601CrossRefGoogle Scholar
  2. 2.
    Zhang Y-c, Han J-x, Xian-bin F, Lin H-b (2014) An online measurement method based on line laser scanning for large forgings. Int J Adv Manuf Technol 70:439–448CrossRefGoogle Scholar
  3. 3.
    Chen J, Wang C, Zhao C, Hua M (2007) Design of a servo motion system and an image sampling and processing system on a 3D laser scanner. Int J Adv Manuf Technol 33:1143–1148CrossRefGoogle Scholar
  4. 4.
    Gåsvik KJ, Robbersmyr KG, Vadseth T, Karimi HR (2013) Deformation measurement of circular steel plates using projected fringes. Int J Adv Manuf Technol 70:321–326CrossRefGoogle Scholar
  5. 5.
    Lin AC, Hui-Chin C (2011) Automatic 3D measuring system for optical scanning of axial fan blades. Int J Adv Manuf Technol 57:701–717CrossRefGoogle Scholar
  6. 6.
    Retnasamy V, Ratnam MM (2008) Measurement of PCI connector tilts using phase-shift fringe projection. Int J Adv Manuf Technol 38:1172–1180CrossRefGoogle Scholar
  7. 7.
    Zhong K, Li Z, Shi Y, Wang C, Lei Y (2013) Fast phase measurement profilometry for arbitrary shape objects without phase unwrapping. Opt Lasers Eng 51:1213–1222CrossRefGoogle Scholar
  8. 8.
    Li Z, Zhong K, Li Y, Zhou X, Shi Y (2013) Multiview phase-shifting: a high-speed and full-resolution 3D measurement framework for arbitrary shape dynamic object. Opt Lett 38:1389–1391CrossRefGoogle Scholar
  9. 9.
    Tao J, Juntong X, Junqi Y (2006) An accurate three-dimensional scanning system with a new phase error compensation method. Int J Adv Manuf Technol 29:1178–1185CrossRefGoogle Scholar
  10. 10.
    Huang Z, Ni J, Shih AJ (2008) Quantitative evaluation of powder spray effects on stereovision measurements. Meas Sci Technol 19:1–12Google Scholar
  11. 11.
    Kowarschik R, Kuhmstedt P, Gerber J (2000) Adaptive optical three-dimensional measurement with structured light. Opt Eng 39:150–158CrossRefGoogle Scholar
  12. 12.
    Li ZC, Kang YC, Moon J-H, Pahk HJ (2013) The optimum projection angle of fringe projection for ban grid array inspection based on reflectance analysis. Int J Adv Manuf Technol 67:1597–1607CrossRefGoogle Scholar
  13. 13.
    Q Hu, KG Harding, X Du, D Hamilton (2005) Shiny parts measurement using color separation. Proc. of SPIE Vol. 6000: 6000D_1-6000D_8Google Scholar
  14. 14.
    Waddington C, Kofman J (2010) Analysis of measurement sensitivity to illuminance and fringe-pattern gray levels for fringe-pattern projection adaptive to ambient lighting. Opt Lasers Eng 48:251–256CrossRefGoogle Scholar
  15. 15.
    C Waddington, J Kofman (2010) Saturation avoidance by adaptive fringe projection in phase-shifting 3D surface-shape measurement. IEEE International Conference on Optomechatronic Technologies Toronto ON: 1–4Google Scholar
  16. 16.
    C Waddington, J Kofman (2010) Sinusoidal fringe-pattern projection for 3D surface measurement with variable illuminance. IEEE International Conference on Optomechatronic Technologies Toronto ON: 1–5Google Scholar
  17. 17.
    S Zhang, S-T Yau (2009) High dynamic range scanning technique. Opt Eng 48(3): 033604_1-033604_7Google Scholar
  18. 18.
    Ekstrand L, Zhang S (2011) Auto-exposure for three-dimensional shape measurement using a digital-light-processing projector. Opt Eng 50:123603CrossRefGoogle Scholar
  19. 19.
    Jiang H, Zhao H, Li X (2012) High dynamic range fringe acquisition: a novel 3D scanning technique for high-reflective surfaces. Optics Lasers Eng 50:1484–1493CrossRefGoogle Scholar
  20. 20.
    H Zhao, X Liang, X Diao, H Jiang (2014) Rapid in-situ 3D measurement of shiny object based on fast and high dynamic range digital fringe projector 54: 170–174Google Scholar
  21. 21.
    Li Y, Chen S (2003) Automatic recalibration of an active structured light vision system. IEEE Robot Autom Mag 19:259–268CrossRefMATHGoogle Scholar
  22. 22.
    Lepetit V, Moreno-Noguer F, Fua P (2009) EPnP: an accurate O(n) solution to the PnP problem. Int J Comput Vis 81:155–166CrossRefGoogle Scholar
  23. 23.
    Quan L, Lan Z (1999) Linear N-point camera pose determination. IEEE Trans Pattern Anal Mach Intell 21:774–780CrossRefGoogle Scholar
  24. 24.
    Chien-Ping L, Hager GD (2000) Fast and globally convergent pose estimation from video images. IEEE Trans Pattern Anal Mach Intell 22:610–622CrossRefGoogle Scholar
  25. 25.
    Oberkampf D, Dementhon DF, Davis LS (1996) Iterative pose estimation using coplanar feature points. Comput Vis Image Underst 63:495–511CrossRefGoogle Scholar
  26. 26.
    Schweighofer G, Pinz A (2006) Robust pose estimation from a planar target. IEEE Trans Pattern Anal Mach Intell 28:2024–2030CrossRefGoogle Scholar
  27. 27.
    Zhang ZY (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22:1330–1334CrossRefGoogle Scholar
  28. 28.
    P Arbelaez, M Maire, C Fowlkes, J Malik (2010) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 99Google Scholar
  29. 29.
    G Lukacs, R Martin, D Marshall (1998) Faithful least-squares fitting of spheres, cylinder, cones and tori for reliable segmentation. In Proceedings of 5th European Conference on Computer Vision Volume I: 671–686Google Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.State Key Laboratory of Material Processing and Die & Mould TechnologyHuazhong University of Science and TechnologyWuhanChina
  2. 2.School of Geodesy and GeomaticsWuhan UniversityWuhanChina
  3. 3.Department of Mechanical and Biomedical EngineeringCity University of Hong KongHong KongChina

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