Combining probabilistic models of space for mobile robots: the Bayesian Map and the Superposition operator

voir basilic : http://emotion.inrialpes.fr/bibemotion/2003/DBM03a/ note: Int. Workshop on Service, Assistive and Personal Robots. Technical Challenges and Real World Application Perspectives address: Madrid (ES) editor: Armada, M. and {Gonzalez de Santos}, PaThis paper deals with the probabilistic modeling of an environment that a robot has to navigate in. We use a method for the probabilistic modeling of space cal led the Bayesian Map formalism. This formalism al lows incremental building of models: we define the Super- position operator, which is a formal ly wel l-defined op- erator. We present first a syntactic version of this operator, and second, a version where the previously obtained model is refined and enriched by experimen- tal learning. In the resulting superposed map, loca- tions are the conjunction of underlying possible loca- tions, which al lows for more precise localization and more complex tasks. A theoretical example validates the concept, and hints at its usefulness for realistic robotic scenarios