Active Global Localisation Using Multiple Hypothesis Tracking

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 on-line generate and track Gaussian pose hypotheses. Apart from lower complexity, this 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 in order to optimise the information gathering for the pose estimation process.

BibTeX Entry:

@InProceedings{Jensfelt99b,
  author = 	 {Patric Jensfelt and Steen Kristensen},
  title = 	 {Active Global Localisation for a Mobile Robot Using
       Multiple Hypothesis Tracking},
  booktitle = 	 IJCAI,
  year =	 1999,
  address =	 {Stockholm, Sweden},
  month =	 aug
}

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