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

This 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 called the Bayesian Map formalism. This formalism allows incremental building of models: we define the Superposition operator, which is a formally well-defined operator. We present first a syntactic version of this operator, and second, a version where the previously obtained model is refined and enriched by experimental learning. In the resulting superposed map, locations are the conjunction of underlying possible locations, which allows for more precise localization and more complex tasks. A theoretical example validates the concept, and hints at its usefulness for realistic robotic scenarios.