Improving Percept Reliability in the Sony Four-Legged Robot League

  • Walter Nisticò
  • Thomas Röfer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4020)

Abstract

This paper presents selected methods used by the vision system of the GermanTeam, the World Champion in the Sony Four-Legged League in 2004. Color table generalization is introduced as a means to achieve a larger independence of the lighting situation. Camera calibration is necessary to deal with the weaknesses of the new platform used in the league, the Sony Aibo ERS-7. Since the robot camera uses a rolling shutter, motion compensation is required to improve the information extracted from the camera images.

References

  1. 1.
    Bakstein, H.: A complete dlt-based camera calibration, including a virtual 3D calibration object. Master’s thesis, Charles University, Prague (1999)Google Scholar
  2. 2.
    Bouguet, J.-Y.: Camera calibration toolbox for matlab, http://www.vision.caltech.edu/bouguetj/calib_doc/
  3. 3.
    Bruce, J., Balch, T., Veloso, M.: Fast and inexpensive color image segmentation for interactive robots. In: Proceedings of the 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000), vol. 3, pp. 2061–2066 (2000)Google Scholar
  4. 4.
    Heikkilä, J., Silvé, O.: A four-step camera calibration procedure with implicit image correction. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 1997), pp. 1106–1112 (1997)Google Scholar
  5. 5.
    Jüngel, M.: Using layered color precision for a self-calibrating vision system. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds.) RoboCup 2004. LNCS (LNAI), vol. 3276, pp. 209–220. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Kang, S.B., Weiss, R.S.: Can we calibrate a camera using an image of a flat, textureless lambertian surface? (2000)Google Scholar
  7. 7.
    Lenser, S., Bruce, J., Veloso, M.: Vision - the lower levels. Carnegie Mellon University Lecture Notes (October 2003), http://www-2.cs.cmu.edu/robosoccer/cmrobobits/lectures/vision-low-level-lec/vision.pdf
  8. 8.
    Mohr, R., Triggs, B.: Projective geometry for image analysis (1996)Google Scholar
  9. 9.
    Nanda, H., Cutler, R.: Practical calibrations for a real-time digital omnidirectional camera. Technical report, CVPR 2001 Technical Sketch (2001)Google Scholar
  10. 10.
    Nistico, W., Schwiegelshohn, U., Hebbel, M., Dahm, I.: Real-time structure preserving image noise reduction for computer vision on embedded platforms. In: Proceedings of the International Symposium on Artificial Life and Robotics, AROB 10th (2005)Google Scholar
  11. 11.
    Russel, S., Norvig, P.: Artificial Intelligence, a Modern Approach. Prentice-Hall, Englewood Cliffs (1995)Google Scholar
  12. 12.
    Schulz, D., Fox, D.: Bayesian color estimation for adaptive vision-based robot localization. In: Proceedings of IROS (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Walter Nisticò
    • 1
  • Thomas Röfer
    • 2
  1. 1.Institute for Robot Research (IRF)Universität Dortmund 
  2. 2.Center for Computing Technology (TZI), FB 3Universität Bremen 

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