A Qualitative-Probabilistic Approach to Autonomous Mobile Robot Self Localisation and Self Vision Calibration

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.