Advertisement

Comparing Sensor Fusion Techniques for Ball Position Estimation

  • Alexander Ferrein
  • Lutz Hermanns
  • Gerhard Lakemeyer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4020)

Abstract

In robotic soccer a good ball position estimate is essential for successful play. Given the uncertainties in the perception of each individual robot, merging the local perceptions of the robots into a global ball estimate often results in a more reliable estimate and helps to increase team performance. Robots can use the global ball position even if they themselves do not see the ball or they can use it to adjust their own perception faults. In this paper we report on our results of comparing state-of-the-art sensor fusion techniques like Kalman filters or the Monte Carlo approach in RoboCup’s Middle-size league. We compare our results to previously published work from other Middle-size league teams and show how the quality of perceiving the ball position is increased.

Keywords

Mobile Robot Sensor Fusion Ground Truth Data Monte Carlo Approach Ball Position 
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.
    Bonarini, A., Matteucci, M., Restelli, M.: Anchoring: do we need new solutions to an old problem or do we have old solutions for a new problem? In: Proc. AAAI Fall Symposium on Achoring Symbols to Sensor Data in Sungle and Multiple Robot Systems (2001)Google Scholar
  2. 2.
    Dellaert, F., Fox, D., Burgard, W., Thrun, S.: Monte Carlo localization for mobile robots. In: Proc. IEEE/RSJ International Conference on Robotics and Automation (ICRA) (May 1999 )Google Scholar
  3. 3.
    Dietl, M., Gutmann, J.-S., Nebel, B.: Cooperative sensing in dynamic environments. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2001) (2001)Google Scholar
  4. 4.
    Fox, D.: Kld-sampling: Adaptive particle filters. In: Advances in Neural Information Processing Systems, vol. 14. MIT Press, Cambridge (2001)Google Scholar
  5. 5.
    Fox, D., Burgard, W., Dellaert, F., Thrun, S.: Monte carlo localization: Efficient position estimation for mobile robots. In: AAAI/IAAI, pp. 343–349 (1999)Google Scholar
  6. 6.
    Fox, D., Burgard, W., Thrun, S.: Markov Localization for Mobile Robots in Dynamic Environments. Journal of Artificial Intelligence Research 11 (1999)Google Scholar
  7. 7.
    Gönner, C., Rous, M., Kraiss, K.-F.: Robust color based picture segmentation for mobile robots. In: Autonome Mobile Systeme, Karlsruhe, Germany, pp. 64–74. Springer, Heidelberg (2003) (in German)Google Scholar
  8. 8.
    Hermanns, L.: Fusing uncertain world information of cooperating robots into a global world model. Diploma thesis, Knowledge-based Systems Group, Computer Science V, RWTH Aachen, Aachen, Germany (2004) (in German)Google Scholar
  9. 9.
    Kalman, R.E.: A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 35–45 (March 1960)Google Scholar
  10. 10.
    Maybeck, P.: Stochastic Models, Estimation and Control, vol. 1. Academic Press, London (1979)MATHGoogle Scholar
  11. 11.
    Milstein, A., Sànchez, J.N., Williamson, E.: Robust Global Localization Using Clustered Particle Filtering. In: Proc. of the National Conference on Artificial Intelligence (AAAI), pp. 581–586 (2002)Google Scholar
  12. 12.
    Moravec, H., Elfes, A.: High resolution maps from wide angular sensors. In: Proc. of the IEEE International Conference on Robotics and Automation (ICRA), pp. 116–121 (1985)Google Scholar
  13. 13.
    Pinheiro, P., Lima, P.: Bayesian sensor fusion for cooperative object localization and world modeling. In: Proc. 8th Conference on Intelligent Autonomous Systems (2004)Google Scholar
  14. 14.
    Steinbauer, G., Faschinger, M., Fraser, G., Mühlenfeld, A., Richter, S., Wöber, G., Wolf, J.: Mostly harmless team description. In: Proc. RoboCup Symposium (2003)Google Scholar
  15. 15.
    Strack, A., Ferrein, A., Lakemeyer, G.: Laser-based localization with sparse landmarks. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) RoboCup 2005. LNCS, vol. 4020, pp. 569–576. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Stroupe, A., Martin, M., Balch, T.: Distributed sensor fusion for object position estimation by multi-robot systems. In: Proc. of 2001 IEEE Int. Conf. on Robotics & Automation (ICRA 2001) (2001)Google Scholar
  17. 17.
    Stulp, F., Gedikli, S., Beetz, M.: Evaluating multi-agent robotic systems using ground truth. In: Proceedings of the Workshop on Methods and Technology for Empirical Evaluation of Multi-agent Systems and Multi-robot Teams (MTEE) (2004)Google Scholar
  18. 18.
    The RoboCup Federation (2004), http://www.robcup.org
  19. 19.
    Thrun, S., Fox, D., Burgard, W., Dellaert, F.: Robust monte carlo localization for mobile robots. Artificial Intelligence 128(1-2), 99–141 (2001)MATHCrossRefGoogle Scholar
  20. 20.
    Wunderlich, J.: Technical description of the allemaniacs soccer robots. Technical report, LTI / KBSG, Aachen University (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Alexander Ferrein
    • 1
  • Lutz Hermanns
    • 2
  • Gerhard Lakemeyer
    • 1
  1. 1.Knowledge-Based Systems Group, Computer Science Department, RWTH AachenAachenGermany
  2. 2.SMA Technologie AGNiestetalGermany

Personalised recommendations