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 - Graphisme, Vision et Robotique, 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.
Type de document :
Communication dans un congrès
Proc. of the IEEE-RSJ Int. Conf. on Intelligent Robots and Systems, 2005, Edmonton, Canada. pp.668--673, 2005
Liste complète des métadonnées

Littérature citée [15 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00182041
Contributeur : Christian Laugier <>
Soumis le : mercredi 24 octobre 2007 - 18:34:20
Dernière modification le : mercredi 17 janvier 2018 - 10:44:41
Document(s) archivé(s) le : lundi 12 avril 2010 - 00:32:13

Fichier

diard05.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : inria-00182041, version 1

Collections

INRIA | UGA | IMAG

Citation

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, 2005. 〈inria-00182041〉

Partager

Métriques

Consultations de la notice

313

Téléchargements de fichiers

138