Concurrent mapping and localization for mobile robots with segmented local maps

Abstract

A map of the environment map is a model of the surroundings of the robot. In many robot application domains, it is infeasible to obtain a prior map of the environment. It is therefore desirable that robots are able to generate maps by themselves. This problem is challenging, since it involves a simultaneous localization problem of the robot relative to its (incomplete) map. This article presents a new probabilistic algorithm for the simultaneous localization and mapping problem. The algorithm is based on matching of a set of local maps that have been obtained from range data. The main advantage of the proposed method is of computational nature. The use of segmented maps permits to eliminate unnecessary details, leading the method to be very appropriate for dynamical environments.