Mobile robot self-localisation using occupancy histograms and a mixture of Gaussian location hypotheses
The topic of mobile robot self-localisation is often divided into the sub-problems of global localisation and position tracking. Both are now well understood individually, but few mobile robots fan deal simultaneously with the two problems in large, complex environments. 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. This method combines a new scan matching technique, using histograms extracted from local occupancy grids, with an efficient algorithm for tracking multiple location hypotheses over time. The method was validated with experiments in a series of real world environments, including its integration into a complete navigating robot. The results show that the robot can localise itself reliably in large, indoor environments using minimal computational resources. (C) 2001 Elsevier Science B.V. All rights reserved.
occupancy grids mobile robot navigation place recognition multiple hypothesis tracking state estimation kalman filtering