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Learning to select distinctive landmarks for mobile robot navigation
University of Manchester. (Department of Computer Science)
The University of Essex. (Department of Computer Science)
Örebro University, Department of Technology. (Learning Systems Lab)
2001 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 37, no 4, p. 241-260Article in journal (Refereed) Published
Abstract [en]

In landmark-based navigation systems for mobile robots, sensory perceptions (e.g., laser or sonar scans) are used to identify the robot’s current location or to construct internal representations, maps, of the robot’s environment. Being based on an external frame of reference (which is not subject to incorrigible drift errors such as those occurring in odometry-based systems), landmark-based robot navigation systems are now widely used in mobile robot applications.

The problem that has attracted most attention to date in landmark-based navigation research is the question of how to deal with perceptual aliasing, i.e., perceptual ambiguities. In contrast, what constitutes a good landmark, or how to select landmarks for mapping, is still an open research topic. The usual method of landmark selection is to map perceptions at regular intervals, which has the drawback of being inefficient and possibly missing ‘good’ landmarks that lie between sampling points.

In this paper, we present an automatic landmark selection algorithm that allows a mobile robot to select conspicuous landmarks from a continuous stream of sensory perceptions, without any pre-installed knowledge or human intervention during the selection process. This algorithm can be used to make mapping mechanisms more efficient and reliable. Experimental results obtained with two different mobile robots in a range of environments are presented and analysed.

Place, publisher, year, edition, pages
2001. Vol. 37, no 4, p. 241-260
Keyword [en]
Landmark-based navigation; Mobile robots; Kalman filter; Automated landmark selection
National Category
Computer Sciences
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-3563DOI: 10.1016/S0921-8890(01)00162-2OAI: oai:DiVA.org:oru-3563DiVA, id: diva2:137861
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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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
  • html
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  • asciidoc
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