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Merging probabilistic models of navigation: 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 - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes
Abstract : This paper deals with the probabilistic modeling of space, in the context of mobile robot navigation. We define a formalism called the Bayesian Map, which allows incremental building of models, thanks to the Superposition operator, which is a formally well-defined operator. Firstly, we present a syntactic version of this operator, and secondly, a version where the previously obtained model is enriched by experimental learning. In the resulting 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.
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Submitted on : Wednesday, October 24, 2007 - 6:34:20 PM
Last modification on : Wednesday, February 2, 2022 - 3:58:38 PM
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  • HAL Id : inria-00182041, version 1



Julien Diard, Pierre Bessiere, Emmanuel Mazer. Merging probabilistic models of navigation: the Bayesian Map and the Superposition operator. Proc. of the IEEE-RSJ Int. Conf. on Intelligent Robots and Systems, 2005, Edmonton, Canada. pp.668--673. ⟨inria-00182041⟩



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