Towards Illumination Invariance in the Legged League

  • Mohan Sridharan
  • Peter Stone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3276)


To date, RoboCup games have all been played under constant, bright lighting conditions. However, in order to meet the overall goal of RoboCup, robots will need to be able to seamlessly handle changing, natural light. One method for doing so is to be able to identify colors regardless of illumination: color constancy. Color constancy is a relatively recent, but increasingly important, topic in vision research. Most approaches so far have focussed on stationary cameras. In this paper we propose a methodology for color constancy on mobile robots. We describe a technique that we have used to solve a subset of the problem, in real-time, based on color space distributions and the KL-divergence measure. We fully implement our technique and present detailed empirical results in a robot soccer scenario.


Illumination invariance Color constancy KL-divergence mobile robots 


  1. 1.
    The International RoboSoccer Competition,
  2. 2.
    The Sony Aibo robots,
  3. 3.
    Brainard, D.H., Freeman, W.T.: Bayesian color constancy. Journal of Optical Soceity of America A 14(7), 1393–1411 (1997)CrossRefGoogle Scholar
  4. 4.
    Brainard, D.H., Wandell, B.A.: Analysis of the retinex theory of color vision. Journal of Optical Soceity of America A 3(10), 1651–1661 (1986)CrossRefGoogle Scholar
  5. 5.
    Buchsbaum, G.: A spatial processor model for object color perception. Journal of Franklin Institute 310, 1–26 (1980)CrossRefGoogle Scholar
  6. 6.
    Finlayson, G.: Color in perspective. IEEE Transactions of Pattern Analysis and Machine Intelligence 18(10), 1034–1038 (1996)CrossRefGoogle Scholar
  7. 7.
    Finlayson, G., Hordley, S.: Improving gamut mapping color constancy. IEEE Transactions on Image Processing 9(10) (October 2000)Google Scholar
  8. 8.
    Finla yson, G., Hordley, S., Hub el, P.: Color by correlation: A simple, unifying framework for color constancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(11) (November 2001)Google Scholar
  9. 9.
    Forsyth, D.: A novel algorithm for color constancy. International Journal of Computer Vision 5(1), 5–36 (1990)CrossRefGoogle Scholar
  10. 10.
    Hyams, J., Powell, M.W., Murphy, R.R.: Cooperative navigation of micro-rovers using color segmentation. Journal of Autonomous Robots 9(1), 7–16 (2000)CrossRefGoogle Scholar
  11. 11.
    Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., Osawa, E.: Robocup:the robot world cup initiative. proceedings of the first international conference on autonomous agents. In: Proceedings of the International Conference of Robotics and Automation, pp. 340–347 (February 1997)Google Scholar
  12. 12.
    Land, E.H.: The retinex theory of color constancy. Scientific American, 108–129 (1977)Google Scholar
  13. 13.
    Lenser, S., Veloso, M.: Automatic detection and response to environmental change. In: Proceedings of the International Conference of Robotics and Automation (May 2003)Google Scholar
  14. 14.
    Minten, B.W., Murphy, R.R., Hyams, J., Micire, M.: Low-order-complexity vision-based docking. IEEE Transactions on Robotics and Automation 17(6), 922–930 (2001)CrossRefGoogle Scholar
  15. 15.
    Rosenberg, C., Hebert, M., Thrun, S.: Color constancy using kl-divergence. In: IEEE International Conference on Computer Vision (2001)Google Scholar
  16. 16.
    Sridharan, M., Stone, P.: Towards illumination invariance on mobile robots. In: The First Canadian Conference on Computer and Robot Vision (2004)Google Scholar
  17. 17.
    Stone, P., Dresner, K., Erdoğan, S.T., Fidelman, P., Jong, N.K., Kohl, N., Kuhlmann, G., Lin, E., Sridharan, M., Stronger, D., Hariharan, G.: Ut austin villa 2003: A new robocup four-legged team, ai technical report 03-304. Technical report, Department of Computer Sciences, University of Texas at Austin (October 2003)Google Scholar
  18. 18.
    Thrun, S., Fox, D., Burgard, W., Dellaert, F.: Robust monte carlo localization for mobile robots. Journal of Artificial Intelligence (2001)Google Scholar
  19. 19.
    Tsin, Y., Collins, R.T., Ramesh, V., Kanade, T.: Bayesian color constancy for outdoor object recognition. IEEE Pattern Recognition and Computer Vision (December 2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Mohan Sridharan
    • 1
  • Peter Stone
    • 2
  1. 1.Electrical and Computer EngineeringThe University of Texas at Austin 
  2. 2.Department of Computer SciencesThe University of Texas at Austin 

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