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)


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.


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.


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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

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