Advertisement

Improvement of Measurement Accuracy of Optical 3D Scanners by Discrete Systematic Error Estimation

  • Christian Bräuer-BurchardtEmail author
  • Peter Kühmstedt
  • Gunther Notni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11255)

Abstract

A new methodology is introduced which enables the improvement of measurement accuracy of optical 3D scanners. This improvement is based on geometric compensation of the systematic measurement error over the measurement volume. Possible sources for systematic measurement errors are introduced and discussed. Estimation of the systematic error is performed by determination of length measurement error of a ballbar in different positions in the measurement volume. Description of the systematic error may be done using polynomials or sampling points in an equidistant volumetric grid. Simulations as well as experimental measurements using real data were performed in order to evaluate the new methodology. The results show that a reduction of the systematic error to about half of the original error is possible. The method is discussed, and potential improvements are given as prospective future work.

Keywords

Computer vision 3D reconstruction Image analysis Optical 3D scanner 

References

  1. 1.
    Brinkmann, B.: Internationales Wörterbuch der Metrologie: Grundlegende und allgemeine Begriffe und zugeordnete Benennungen (VIM) Deutsch-englische Fassung ISO/IEC-Leitfaden 99. Beuth, Berlin (2012)Google Scholar
  2. 2.
    Bräuer-Burchardt, C., Kühmstedt, P., Notni, G.: Error compensation by sensor re-calibration in fringe projection based optical 3D stereo scanners. In: Maino, G., Foresti, G.L. (eds.) ICIAP 2011. LNCS, vol. 6979, pp. 363–373. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-24088-1_38CrossRefGoogle Scholar
  3. 3.
    Bräuer-Burchardt, C., Ölsner, S., Kühmstedt, P., Notni, G.: Comparison of calibration strategies for optical 3D scanners based on structured light projection using a new evaluation methodology. In: Videometrics, Range Imaging, and Applications XIV. Proceedings of SPIE, vol. 10332, pp. 103320F1–103320F10 (2017)Google Scholar
  4. 4.
    Griva, I., Nash, S.G., Sofer, A.: Linear and Nonlinear Optimization, 2nd edn. George Mason University, Fairfax (2009)CrossRefGoogle Scholar
  5. 5.
    Gu, S., McNamara, J.E., Johnson, K., Gennert, M.A., King, M.A.: Calibration accuracy evaluation with stereo reconstruction. In: IEEE Nuclear Science Symposium Conference Record, San Diego, CA, pp. 3242–3246 (2006)Google Scholar
  6. 6.
    Hasetedt, H., Luhmann, T.: Analyse der Kamerakalibrierung mit OpenCV. In: Luhmann, T., Müller, C. (eds.) Photogrammetrie, Laserscanning, Optische 3D-Messtechnik, Proceedings of Oldenburger 3D-Tage 2015, pp. 259–268 (2015)Google Scholar
  7. 7.
    He, Y., Liang, B., Zou, Y., He, J., Yang, J.: Depth errors analysis and correction for time-of-flight (ToF) cameras. Sensors 17(92), 1–18 (2017)Google Scholar
  8. 8.
    Isheil, A., Gonnet, J.-P., Joannic, D., Fontaine, J.-F.: Systematic error correction of a 3D laser scanning measurement device. Opt. Lasers Eng. 49, 16–24 (2011)CrossRefGoogle Scholar
  9. 9.
    JCGM 200: International vocabulary of metrology—basic and general concepts and associated terms (VIM), 3rd edn. (2012). https://www.bipm.org/utils/common/documents/jcgm/JCGM_200_2012.pdf. Accessed 02 Aug 2018
  10. 10.
    Kahlmann, T., Ingensand, H.: Calibration and development for increased accuracy of 3D range imaging cameras. J. Appl. Geod. 2(2008), 1–11 (2008)Google Scholar
  11. 11.
    Luhmann, T., Fraser, C., Maas, H.-G.: Sensor modelling and camera calibration for close-range photogrammetry. ISPRS J. Photogramm. Remote. Sens. 115, 37–46 (2016)CrossRefGoogle Scholar
  12. 12.
    Muralikrishnan, B., et al.: Volumetric performance evaluation of a laser scanner based on geometric error model. Precis. Eng. 40, 139–150 (2015)CrossRefGoogle Scholar
  13. 13.
    Remondino, F., Fraser, C.: Digital camera calibration methods: considerations and comparisons. Int. Arch. Photogramm., Remote. Sens., Spat. Inf. Sci. XXXVI(5), 266–272 (2006)Google Scholar
  14. 14.
    Rieke-Zapp, D., Tecklenburg, W., Peipe, J., Hastedt, H., Haig, C.: Evaluation of the geometric stability and the accuracy potential of digital cameras – comparing mechanical stabilisation versus parameterization. ISPRS J. 64(3), 248–258 (2009)CrossRefGoogle Scholar
  15. 15.
    VDI/VDE 2634: Optical 3D-measuring systems. VDI/VDE guidelines, Part 2. Beuth, Berlin (2008). https://m.vdi.de/uploads/tx_vdirili/pdf/1456386.pdf. Accessed 02 Aug 2018
  16. 16.
    Wang, B., Pan, B., Tao, R., Lubineau, G.: Systematic errors in digital volume correlation due to the self-heating effect of a laboratory x-ray CT scanner. Meas. Sci. Technol. 28, 055402 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Christian Bräuer-Burchardt
    • 1
    Email author
  • Peter Kühmstedt
    • 1
  • Gunther Notni
    • 1
    • 2
  1. 1.Fraunhofer Institute for Applied Optics and Precision EngineeringJenaGermany
  2. 2.Technical University IlmenauIlmenauGermany

Personalised recommendations