Active Global Localisation for a Mobile Robot Using Multiple Hypothesis Tracking

Abstract

In this paper we present a probabilistic approach for mobile robot localisation using an incomplete topological world model. The method uses multi--hypothesis Kalman filter based pose tracking combined with a probabilistic formulation of hypothesis correctness to generate and track Gaussian pose hypotheses on--line. Apart from a lower computational complexity, this approach has the advantage over traditional grid based methods that incomplete and topological world model information can be utilised. Furthermore, the method generates movement commands for the platform to enhance the gathering of information for the pose estimation process. Extensive experiments are presented from two different environments, a typical office environment and an old hospital building.

BibTeX Entry:

@Article{Jensfelt01b,
  author = 	 {Patric Jensfelt and Steen Kristensen},
  title = 	 {Active Global Localisation for a Mobile Robot Using
              Multiple Hypothesis Tracking},
  journal = 	 {IEEE Transactions on Robotics and Automation},
  year = 	 2001,
  volume =	 17,
  number =	 5,
  pages =	 {748--760},
  month =	 oct
}

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