Map-Based Multiple Model Tracking of a Moving Object

  • Cody Kwok
  • Dieter Fox
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3276)


In this paper we propose an approach for tracking a moving target using Rao-Blackwellised particle filters. Such filters represent posteriors over the target location by a mixture of Kalman filters, where each filter is conditioned on the discrete states of a particle filter. The discrete states represent the non-linear parts of the state estimation problem. In the context of target tracking, these are the non-linear motion of the observing platform and the different motion models for the target. Using this representation, we show how to reason about physical interactions between the observing platform and the tracked object, as well as between the tracked object and the environment. The approach is implemented on a four-legged AIBO robot and tested in the context of ball tracking in the RoboCup domain.


Mobile Robot Ball Sample Robot Position Ball Location Ball Estimate 
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 2005

Authors and Affiliations

  • Cody Kwok
    • 1
  • Dieter Fox
    • 1
  1. 1.Department of Computer Science & EngineeringUniversity of WashingtonSeattle

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