Knowing your place in real world environments

Abstract

The topic of mobile robot self-localisation is usually divided into the sub-problems of global localisation and position tracking. Both are now well understood individually, but few mobile robots can deal simultaneously with the two problems in large, complex environments. While efficient solutions have been found for metric maps, topological maps have, by nature of their compactness, the potential for representing environments which are several orders of magnitude larger than those which can be tractably navigated using metric maps. In this paper, we present a unified approach to global localisation and position tracking which is based on a topological map augmented with metric information. The method was validated through a series of experiments conducted in four real-world environments, including its integration into a complete navigating mobile robot. Quantitative performance measures were used to assess localisation quality versus computational efficiency. The results show that our robot can localise and navigate reliably in large, complex environments using only minimal computational resources.