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Mobile robot self-localisation using occupancy histograms and a mixture of Gaussian location hypotheses
Örebro University, Department of Technology. (Learning Systems Lab)
University of Manchester. (Department of Computer Science)
2001 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 34, no 2-3, p. 117-129Article in journal (Refereed) Published
Abstract [en]

The topic of mobile robot self-localisation is often divided into the sub-problems of global localisation and position tracking. Both are now well understood individually, but few mobile robots can deal simultaneously with the two problems in large, complex environments. In this paper, we present a unified approach to global localisation and position tracking which is based on a topological map augmented with metric information. This method combines a new scan matching technique, using histograms extracted from local occupancy grids, with an efficient algorithm for tracking multiple location hypotheses over time. The method was validated with experiments in a series of real world environments, including its integration into a complete navigating robot. The results show that the robot can localise itself reliably in large, indoor environments using minimal computational resources

Place, publisher, year, edition, pages
2001. Vol. 34, no 2-3, p. 117-129
Keyword [en]
Mobile robot navigation; Place recognition; Occupancy grids; Multiple hypothesis tracking; State estimation; Kalman filtering
National Category
Computer Sciences
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-3570DOI: 10.1016/S0921-8890(00)00116-0OAI: oai:DiVA.org:oru-3570DiVA, id: diva2:137868
Available from: 2007-07-22 Created: 2007-07-22 Last updated: 2018-01-13Bibliographically approved

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Duckett, Tom

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