Aliasing maps for robot global localization

In this paper we present a mobile robot localization system that integrates Monte-Carlo localization with an active action-selection approach based on an aliasing map. The main novelty of the approach is in the off-line evaluation of the perceptual aliasing of the environment and in the use of this knowledge to perform localization processes faster and better. Preliminary results show improved performances compared with the classic Monte-Carlo localization approach. © 2006 The authors.