Advertisement

Exploiting the Unexpected: Negative Evidence Modeling and Proprioceptive Motion Modeling for Improved Markov Localization

  • Jan Hoffmann
  • Michael Spranger
  • Daniel Göhring
  • Matthias Jüngel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4020)

Abstract

This paper explores how sensor and motion modeling can be improved to better Markov localization by exploiting deviations from expected sensor readings. Proprioception is achieved by monitoring target and actual motions of robot joints. This provides information about whether or not an action was executed as desired, yielding a quality measure of the current odometry. Odometry is usually extremely prone to errors for legged robots, especially in dynamic environments where collisions are often unavoidable, due to the many degrees of freedom of the robot and the numerous possibilities of motion hindrance. A quality measure helps differentiate the periods of unhindered motion from periods where robot motion was impaired for whatever reason. Negative evidence is collected when a robot fails to detect a landmark that it expects to see. Therefore the gaze direction of the camera has to be modeled accordingly. This enables the robot to localize where it could not when only using landmarks. In the general localization task, the probability distribution converges more quickly when negative information is taken into account.

Keywords

Mobile Robot Motion Model Obstacle Avoidance Collision Detection Negative Information 
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.
    Fox, D., Burgard, W., Dellart, F., Thrun, S.: Monte Carlo Localization: Efficient Position Estimation for Mobile Robots. In: Proc. of AAAI (1999)Google Scholar
  2. 2.
    Fox, D., Burgard, W., Thrun, S.: Active Markov Localization for Mobile Robots. In: Robotics and Autonomous Systems (1998)Google Scholar
  3. 3.
    Hoffmann, J., Göhring, D.: Sensor-actuator-comparison as a basis for collision detection for a quadruped robot. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds.) RoboCup 2004. LNCS (LNAI), vol. 3276, pp. 150–159. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Hoffmann, J., Jüngel, M., Lötzsch, M.: A vision based system for goal-directed obstacle avoidance. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds.) RoboCup 2004. LNCS (LNAI), vol. 3276, pp. 418–425. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Koch, W.: On Negative Information in Tracking and Sensor Data Fusion. In: Proceedings of the Seventh International Conference on Information Fusion, pp. 91–98 (2004)Google Scholar
  6. 6.
    Kwok, C., Fox, D.: Map-based multiple model tracking of a moving object. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds.) RoboCup 2004. LNCS (LNAI), vol. 3276, pp. 18–33. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Lenser, S., Bruce, J., Veloso, M.: CMPack: A Complete Software System for Autonomous Legged Soccer Robots. In: AGENTS 2001: Proceedings of the fifth international conference on Autonomous agents, pp. 204–211. ACM Press, New York (2001)CrossRefGoogle Scholar
  8. 8.
    Lenser, S., Veloso, M.: Visual Sonar: Fast Obstacle Avoidance Using Monocular Vision. In: Proceedings of IROS 2003 (2003)Google Scholar
  9. 9.
    Montemerlo, M., Thrun, S.: Simultaneous Localization and Mapping with Unknown Data Association Using FastSLAM (2003)Google Scholar
  10. 10.
    Röfer, T., Jüngel, M.: Vision-Based Fast and Reactive Monte-Carlo Localization. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2003), Taipei, Taiwan, pp. 856–861 (2003)Google Scholar
  11. 11.
    Röfer, T., Jüngel, M.: Fast and robust edge-based localization in the sony four-legged robot league. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS (LNAI), vol. 3020, pp. 262–273. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Särkkä, S., Tamminen, T., Vehtari, A., Lampinen, J.: Probabilistic Methods in Multiple Target Tracking, Research Report B36. In: Technical report, Laboratory of Computational Engineering Helsinki University of Technology (2004)Google Scholar
  13. 13.
    Thrun, S., Fox, D., Burgard, W.: Monte Carlo Localization with Mixture Proposal Distribution. In: Proc. of the National Conference on Artificial Intelligence, pp. 859–865 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jan Hoffmann
    • 1
  • Michael Spranger
    • 1
  • Daniel Göhring
    • 1
  • Matthias Jüngel
    • 1
  1. 1.Institut für Informatik, LFG Künstliche IntelligenzHumboldt-Universität zu BerlinBerlinGermany

Personalised recommendations