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

Julien Diard 1 Pierre Bessiere 1 Emmanuel Mazer 1
1 E-MOTION - Geometry and Probability for Motion and Action
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes
Abstract : 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 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.
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Julien Diard, Pierre Bessiere, Emmanuel Mazer. Combining probabilistic models of space for mobile robots: the Bayesian Map and the Superposition operator. Proc. of the Int. Advanced Robotics Programme, Oct 2003, Madrid (ES), France. pp.65--72. ⟨inria-00182076⟩

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