Knowledge-based adaptive thresholding from qualitative robot localisation using cast shadows

This paper presents results of a mobile robot qualitative self-localisation experiment using information from cast shadows. Shadow detection was accomplished by mapping the images from the robot’s monocular colour camera into a HSV colour space and then thresholding on V. We present results of self-localisation using two methods for obtaining the threshold automatically: in one method the images are segmented according to their grey-scale histograms, in the other the threshold is set according to a prediction about the robot’s location, given a shadow-based map defined upon a qualitative spatial reasoning theory. This map-related threshold search is the main contribution of the present work, and to the best of our knowledge this is the first work that uses qualitative spatial representations both to perform egolocation and to calibrate a robot’s interpretation of its perceptual input.