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Conference Papers Year : 2003

Hierarchies of probabilistic models of space for mobile robots: the bayesian map and the abstraction operator

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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Other [cs.OH]
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Dates and versions

inria-00182081 , version 1 (24-10-2007)

Identifiers

  • HAL Id : inria-00182081 , version 1

Cite

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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