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. © 2013 IEEE.
|Original language||American English|
|Number of pages||6|
|State||Published - 1 Jan 2013|
|Event||Proceedings - 2013 Brazilian Conference on Intelligent Systems, BRACIS 2013 - |
Duration: 1 Jan 2013 → …
|Conference||Proceedings - 2013 Brazilian Conference on Intelligent Systems, BRACIS 2013|
|Period||1/01/13 → …|