Skip to Main content Skip to Navigation
Conference papers

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 - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, 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.
Document type :
Conference papers
Complete list of metadata

Cited literature [15 references]  Display  Hide  Download

https://hal.inria.fr/inria-00182081
Contributor : Christian Laugier Connect in order to contact the contributor
Submitted on : Wednesday, October 24, 2007 - 6:47:58 PM
Last modification on : Wednesday, February 2, 2022 - 3:58:38 PM
Long-term archiving on: : Monday, April 12, 2010 - 12:36:32 AM

File

diard03b.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : inria-00182081, version 1

Collections

Citation

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⟩

Share

Metrics

Record views

120

Files downloads

320