Active global localisation for a mobile robot using multiple hypothesis tracking
1999 (English)Conference paper (Refereed)
In this paper we present a probabilistic approach for mobile robot localization using an incomplete topological world model. The method, which we have termed multi-hypothesis localization (MHL), uses multi-hypothesis Kalman filter based pose tracking combined with a probabilistic formulation of hypothesis correctness to generate and track Gaussian pose hypotheses online. 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 utilized. 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.
Place, publisher, year, edition, pages
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-52981OAI: oai:DiVA.org:kth-52981DiVA: diva2:468373
Proc. of the IJCAI-99 Workshop on Reasoning with Uncertainty in Robot Navigation
NR 201408052011-12-202011-12-202016-05-30Bibliographically approved