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active global localisation for a mobile robot using multiple hypothesis tracking
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0002-1170-7162
2001 (English)In: IEEE transactions on robotics and automation, ISSN 1042-296X, Vol. 17, no 5, 748-760 p.Article in journal (Refereed) Published
Abstract [en]

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
2001. Vol. 17, no 5, 748-760 p.
Keyword [en]
Bayesian; feature based; global localization; multiple hypothesis
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-69624DOI: 10.1109/70.964673OAI: diva2:485639
NR 20140805Available from: 2012-01-29 Created: 2012-01-29Bibliographically approved

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Jensfelt, Patric
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