Hierarchies of probabilistic models of space for mobile robots: the bayesian map and the abstraction 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 presents a new method for probabilistic modelling of space, called the Bayesian Map for- malism. It offers a generalization of some com- mon approaches found in the literature, as it does not constrain the dependency structure of the prob- abilistic model. The formalism allows incremental building of hierarchies of models, by the use of the Abstraction Operator. In the resulting hierarchy, lo- calization in the high level model is based on prob- abilistic competition of the lower level models. Ex- perimental results validate the concept, and hint at its usefulness for large scale scenarios.
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Julien Diard, Pierre Bessiere, Emmanuel Mazer. Hierarchies of probabilistic models of space for mobile robots: the bayesian map and the abstraction operator. Proc. of the Workshop on Reasoning with Uncertainty in Robotics, Aug 2003, Acapulco (MX), France. ⟨inria-00182081⟩

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