Advertisement

Calibration of Galvanometric Laser Scanners Using Statistical Learning Methods

  • Stefan LüdtkeEmail author
  • Benjamin Wagner
  • Ralf Bruder
  • Patrick Stüber
  • Floris Ernst
  • Achim Schweikard
  • Tobias Wissel
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Abstract

Galvanometric laser scanners can be used for optical tracking. Model-based calibration of these systems is inaccurate and not adaptable to variations in the system. Therefore, a calibration method based on statistical learning methods is presented which directly incorporates the triangulation problem. We investigate linear regression as well as Artificial Neural Networks. The results are validated using (1) the cross-validated prediction accuracy within the calibration space, and (2) plane reconstruction accuracy. All statistical learning methods outperformed the model-based approach leading to an improvement of up to 74% for the cross-validated 3D root-mean-square error and 70-74% for the plane reconstruction. While the neural network achieved mean errors below 0.5 mm, the linear regression results suggest a good compromise between accuracy and computational load.

Keywords

Root Mean Square Error Laser Spot Ridge Regression Plane Reconstruction Hardware Setup 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bridgelall R, Dvorkis P, Goren DP, et al.. Laser scanning system and scanning method for reading 1-d and 2-d barcode symbols; 2000.Google Scholar
  2. 2.
    Gorham EW, Risser CJ, Schultz DW, et al.. Laser material processing system with multiple laser sources apparatus and method; 2001.Google Scholar
  3. 3.
    Wissel T, St¨uber P, Wagner B, et al. Tissue thickness estimation for high precision head-tracking using a galvanometric laser scanner: a case study. Proc EMBC. 2014; p. 3106–9.Google Scholar
  4. 4.
    Wagner B, St¨uber P, Wissel T, et al. Accuracy analysis for triangulation and tracking based on time-multiplexed structured light. Med Phys. 2014;41(8).Google Scholar
  5. 5.
    Manakov A, Seidel HP, Ihrke I. A mathematical model and calibration procedure for galvanometric laser scanning systems. Proc VMV. 2011; p. 207–14.Google Scholar
  6. 6.
    Smith LN, Smith ML. Automatic machine vision calibration using statistical and neural network methods. Image Vis Comput. 2005;23(10):887–99.CrossRefGoogle Scholar
  7. 7.
    Bradski G. The OpenCV Library. Dr Dobb’s J Softw Tools. 2000.Google Scholar
  8. 8.
    Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. Springer Series in Statistics. Springer New York Inc.; 2001.Google Scholar
  9. 9.
    Hartley R, Zisserman A. Multiple View Geometry in Computer Vision. 2nd ed. Cambridge University Press; 2004.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Stefan Lüdtke
    • 1
    Email author
  • Benjamin Wagner
    • 1
    • 2
  • Ralf Bruder
    • 1
  • Patrick Stüber
    • 1
    • 2
  • Floris Ernst
    • 1
  • Achim Schweikard
    • 1
  • Tobias Wissel
    • 1
    • 2
  1. 1.Institute for Robotics and Cognitive SystemsUniversity of LübeckLübeckDeutschland
  2. 2.Graduate School for Computing in Medicine and Life SciencesUniversity of LübeckLübeckDeutschland

Personalised recommendations