A Novel Approach to Efficient Monte-Carlo Localization in RoboCup

  • Patrick Heinemann
  • Jürgen Haase
  • Andreas Zell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4434)


Recently, efficient self-localization methods have been developed, among which probabilistic Monte-Carlo localization (MCL) is one of the most popular. However, standard MCL algorithms need at least 100 samples to compute an acceptable position estimation. This paper presents a novel approach to MCL that uses an adaptive number of samples that drops down to a single sample if the pose estimation is sufficiently accurate. Experiments show that the method remains in this efficient single sample tracking mode for more than 90% of the cycles.


Tracking Mode Line Point Iterative Improvement Omnidirectional Image Omnidirectional Camera 
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 2007

Authors and Affiliations

  • Patrick Heinemann
    • 1
  • Jürgen Haase
    • 1
  • Andreas Zell
    • 1
  1. 1.Wilhelm-Schickard-Institute, Department of Computer Architecture, University of Tübingen, Sand 1, 72076 TübingenGermany

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