Abstract
Typically, the spatial features of a robot's environment are specified using metric coordinates, and well-known mobile robot localisation techniques are used to track the exact robot position. In this paper, a qualitative-probabilistic approach is proposed to address the problem of mobile robot localisation. This approach combines a recently proposed logic theory called Perceptual Qualitative Reasoning about Shadows (PQRS) with a Bayesian filter. The approach herein proposed was systematically evaluated through experiments using a mobile robot in a real environment, where the sequential prediction and measurement steps of the Bayesian filter are used to both self-localisation and self-calibration of the robot's vision system from the observation of object's and their shadows. The results demonstrate that the qualitative-probabilistic approach effectively improves the accuracy of robot localisation, keeping the vision system well calibrated so that shadows can be properly detected.
Original language | English |
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Title of host publication | 2013 Brazilian Conference on Intelligent Systems |
Place of Publication | Fortaleza |
Publisher | IEEE Computer Society |
Pages | 157-162 |
Number of pages | 6 |
ISBN (Electronic) | 978-0-7695-5092-3 |
DOIs | |
Publication status | Published - 2013 |
Externally published | Yes |
Event | 2nd Brazilian Conference on Intelligent Systems, BRACIS 2013 - Fortaleza, Ceara, Brazil Duration: 20 Oct 2013 → 24 Oct 2013 |
Conference
Conference | 2nd Brazilian Conference on Intelligent Systems, BRACIS 2013 |
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Country | Brazil |
City | Fortaleza, Ceara |
Period | 20/10/13 → 24/10/13 |
Keywords
- Bayesian Filtering
- Mobile Robot
- Qualitative Spatial Reasoning
- Self-localisation