Distributed Sensor Fusion for Object Tracking

  • Alankar Karol
  • Mary-Anne Williams
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4020)


In a dynamic situation like robot soccer any individual player can only observe a limited portion of their environment at any given time. As such to develop strategies based upon planning and cooperation between different players it is imperative that they be able to share information which may or may not be in any individual player’s field of vision. In this paper we propose a method for multi-agent cooperation for perception based upon the Extended Kalman Filter (EKF) which enables players to track objects absent from their field of vision and also to improve the accuracy of position and velocity estimates of objects in their field of vision.


Extend Kalman Filter Object Tracking Ball Position Robot Localisation Robot Soccer 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ho, Y.C., Lee, R.C.K.: A Bayesian Approach to Problems in Stochastic Estimation and Control. IEEE Trans. Automat. Contr. AC-9, 333–339 (1964)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Karol, A., Nebel, B., Stanton, C., Williams, M.-A.: Case based game play in the roboCup four-legged league part I the theoretical model. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS, vol. 3020, pp. 739–747. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Maybeck, P.S.: Stochastic Models, Estimation and Control, vol. 1. Academic Press, London (1979)MATHGoogle Scholar
  4. 4.
    Sasiadek, J.Z., Hartana, P.: Sensor data fusion using kalman filter. In: Proceedings of the Third International Conference on Information Fusion, vol. 2, pp. 19–25 (2000)Google Scholar
  5. 5.
    Smith, R.C., Cheeseman, P.: On the Representation and Estimation of Spatial Uncertainty. International Journal of Robotics Research 5(4), 56–68 (1986)CrossRefGoogle Scholar
  6. 6.
    Stroupe, A., Martin, M.C., Balch, T.: Distributed Sensor Fusion for Object Position Estimation by Multi-Robot Systems. In: Proceedings of the IEEE International Conference on Robotics and Automation. IEEE, Los Alamitos (2001)Google Scholar
  7. 7.
    Tang, C.Y., Hung, Y.P., Shih, S.W., Chen, Z.: A Feature Based tracker for multiple object tracking. In: Proceedings of the National Science Council, Republic of China, Part A, vol. 23(1), pp. 151–168 (1999)Google Scholar
  8. 8.
    Wang, H., Chua, C.S., Sim, C.T.: Real Time Object Tracking from Corners. Robotica 16(1), 109–116 (1998)CrossRefGoogle Scholar
  9. 9.
    Weigel, T., Gutmann, J.-S., Dietl, M., Kleiner, A., Nebel, B.: CS Freiburg: Coordinating Robots for Successful Soccer Playing. IEEE Transactions on Robotics and Automation 18(5), 685–699 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Alankar Karol
    • 1
  • Mary-Anne Williams
    • 1
  1. 1.Faculty of Information TechnologyUniversity of TechnologySydneyAustralia

Personalised recommendations