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How Optical CMMs and 3D Scanning Will Revolutionize the 3D Metrology World

  • Jean-Francois LarueEmail author
  • Daniel Brown
  • Marc Viala
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Every day, optical technology plays a bigger and bigger role in metrology. No doubt optical metrology will soon be used to perform continuous, high-speed inspections of items of all shapes, sizes, and materials. However, it will take time to move from a world dominated by coordinate measuring machines that use sensors to one where optical CMM reigns, especially in industries such as automotive or aeronautics where every new thing must prove itself first. One of the biggest changes in metrology in the past 30 years has been the development of portable measuring devices. This has brought inspection right into the production line, as close to the part as possible. The change—sparked by the development of portable measuring arms in the early 1990s and the emergence of laser trackers shortly after—turned traditional industries’ inspection methods completely upside down. However, most portable measurement solutions still use technologies that have serious limitations in production environments. For example, these solutions demand extreme stability throughout the measurement process. Optical solutions, especially those using video cameras, sidestep these limitations by enabling automatic positioning and continuous measurement. Now, laser- and white-light-based digitizing technologies can also produce increasingly dense data. In this chapter, we will see the basic implementation principles and advantages offered by these optical solutions for performing direct inspections in a production environment. Key point is about using optical reflectors as metrological references on parts and using these references to (1) allow self-positioning of handheld scanners, (2) automatically align the device with a predetermined 3D reference, (3) easily move the device around the part, and (4) maintain high measurement precision in industrial production environments. We will also analyze all the factors that go into measurement precision and see how the use of optical technologies make it possible to greatly reduce the primary causes of measurement imprecision. To illustrate our analysis, the reader will find specific cases taken from real applications in the aeronautical, automotive, and naval industries.

Keywords

Coordinate Measuring Machine Optical Reflector Perspective Projection Laser Tracker Bundle Adjustment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    CMM metrology [Web] On http://www.cmmmetrology.co.uk/history_of_the_cmm.htm. Visited on 28 Dec 2011
  2. 2.
    Renishaw [Web] on http://www.renishaw.com/en/Our+company–6432. Visited on 28 Dec 2011
  3. 3.
    AFNOR [Web] on http://www.bivi.metrologie.afnor.org/ofm/metrologie/ii/ii-80/ii-80-30. Visited on 28 Dec 2011 (in French)
  4. 4.
    Mayer Roy (1999) Scientific Canadian: invention and innovation from Canada’s National Research Council. Raincoast Books, Vancouver. ISBN 1551922665. OCLC 41347212Google Scholar
  5. 5.
    Karara HM (1989) Non-topographic photogrammetry. American Society for Photogrammetry and Remote Sensing, USAGoogle Scholar
  6. 6.
    Atkinson KB (1996) Close range photogrammetry and machine vision. Whittles Publishing, Scotland. ISBN 1-870325-46-XGoogle Scholar
  7. 7.
    Hartley RI, Zisserman A (2004) Multiple view geometry in computer vision. Cambridge University Press, CambridgezbMATHCrossRefGoogle Scholar
  8. 8.
    Bartoli A (2003) Reconstruction et alignement en vision 3D: points, droites, plans et caméras. Thèse, GRAVIR, INPGGoogle Scholar
  9. 9.
    Nistér D (2001) Automatic dense reconstruction from uncalibrated video sequences. Rapport de Thèse, KTH, Université de StockholmGoogle Scholar
  10. 10.
    Lhuillier M, Quan L (2005) A quasi-dense approach to surface reconstruction from uncalibrated images. IEEE Trans Pattern Anal Mach Intell 27(3):418–433Google Scholar
  11. 11.
    Royer É et al (2007) Monocular vision for mobile robot localization and autonomous navigation. Int J Comput Vision 74(3):237–260 (Springer Science)Google Scholar
  12. 12.
    Agarwal S et al (2010) Bundle adjustment in the large. In: European conference on computer vision, Springer, Berlin, pp 29–42Google Scholar
  13. 13.
    Triggs B et al (2000) Bundle adjustment—a modern synthesis. Triggs B, Zisserman A, Szeliski R (eds) Vision algorithms: theory & practice. Springer, Berlin, pp 153–177Google Scholar
  14. 14.
    Jian, Yong-Dian, Balcan, Doru C. et Dellaert, Frank. 2011. Generalized subgraph preconditioners for large-scale bundle adjustment. In: Proceedings of 13th IEEE international conference on computer vision. IEEE, NJGoogle Scholar
  15. 15.
    Morris D (2001) Gauge freedoms and uncertainty modeling for 3d computer vision. Thèse de doctorat, Robotics institute, Carnegie Mellon UniversityGoogle Scholar
  16. 16.
    Faugeras O (1993) Three-dimensional computer vision. MIT Press, CambridgeGoogle Scholar
  17. 17.
    Shortis MR, Clarke TA (1994) A comparison of some techniques for the subpixel location of discrete target images, Vdeometrics III 2350:239–250Google Scholar
  18. 18.
    Shortis MR, Clarke TA, Robson S (1995) Practical testing of the precision and accuracy of target image centring algorithms. Videometrics IV 2598:65–76Google Scholar
  19. 19.
    Otepka J (2004) Precision target mensuration in vision metrology. Technische Universität Wien, VienneGoogle Scholar
  20. 20.
    Hartley RI, Sturm P (1997) Triangulation. Comput Vision Image Understand 68(12):146–157Google Scholar
  21. 21.
    Horn BKP (1987) Closed-form solution of absolute orientation using unit quaternions. J Opt Soc America 4:629–642CrossRefGoogle Scholar
  22. 22.
    Eggert DW, Lorusso A, Fisher RB (1997) Estimating 3-D rigid body transformations: a comparison of four major algorithms Machine Vision and Applications 9(5–6):272–290Google Scholar
  23. 23.
    Wikipedia [Web] on http://en.wikipedia.org/wiki/Structured-light_3D_scanner. Visited on 28 Dec 2011
  24. 24.
    Fringe (2005) The 5th international workshop on automatic processing of fringe patterns. Springer, Berlin. ISBN 3-540-26037-4; ISBN 978-3-540-26037-0Google Scholar
  25. 25.
    Wikipedia [Web] on http://en.wikipedia.org/wiki/File:Laserprofilometer_EN.svg. Visited on 28 Dec 2011

Copyright information

© Springer-Verlag London (outside the USA) 2015

Authors and Affiliations

  1. 1.Creaform IncLévisCanada

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