Merging probabilistic models of navigation: the Bayesian Map and the Superposition operator - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2005

Merging probabilistic models of navigation: the Bayesian Map and the Superposition operator

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.

Domains

Other [cs.OH]
Fichier principal
Vignette du fichier
diard05.pdf (331.52 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

inria-00182041 , version 1 (24-10-2007)

Identifiers

  • HAL Id : inria-00182041 , version 1

Cite

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⟩
94 View
326 Download

Share

Gmail Facebook X LinkedIn More