Abstract

Context-aware computing in location-aware environments demands the combination of real world position with a computational world model to infer context. We present a novel approach to building world models using signals inherent in positioning systems, building on the work of the robotics research field. We implement the approach using the Bat ultrasonic location system. We observe excellent results when trying to extract the height and shape of horizontal surfaces, and demonstrate how to image and characterise object volumes. Results are collected using personnel Bats and by using an autonomous vehicle which moves randomly. In both cases, the results are accurate and reliable.