The Bayesian Occupation Filter

Abstract : Perception of and reasoning about dynamic environments is pertinent for mobile robotics and still constitutes one of the major challenges. To work in these environments, the mobile robot must perceive the environment with sensors; measurements are uncertain and normally treated within the estimation framework. Such an approach enables the mobile robot to model the dynamic environment and follow the evolution of its environment. With an internal representation of the environment, the robot is thus able to perform reasoning and make predictions to accomplish its tasks successfully. Systems for tracking the evolution of the environment have traditionally been a major component in robotics. Industries are now beginning to express interest in such technologies. One particular example is the application within the automotive industry for adaptive cruise control [Coué et al., 2002], where the challenge is to reduce road accidents by using better collision detection sys- tems. The major requirement of such a system is a robust tracking system. Most of the existing target-tracking algorithms use an object-based represen- tation of the environment. However, these existing techniques must explicitly consider data association and occlusion. In view of these problems, a grid- based framework, the Bayesian occupancy filter (BOF) [Coué et al., 2002, 2003], has been proposed.
Type de document :
Chapitre d'ouvrage
Bessière, P. and Laugier, C. and Siegwart, R. Probabilistic Reasoning and Decision Making in Sensory-Motor Systems, 46, Springer, 2008, Springer Tracts in Advanced Robotics Series
Liste complète des métadonnées

Littérature citée [13 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00295084
Contributeur : Thierry Fraichard <>
Soumis le : vendredi 11 juillet 2008 - 11:24:35
Dernière modification le : mercredi 17 janvier 2018 - 10:44:41
Document(s) archivé(s) le : vendredi 28 mai 2010 - 20:00:00

Fichier

08-star-tay-etal.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : inria-00295084, version 1

Collections

Citation

Christopher Tay, Kamel Mekhnacha, Manuel Yguel, Christophe Coue, Cédric Pradalier, et al.. The Bayesian Occupation Filter. Bessière, P. and Laugier, C. and Siegwart, R. Probabilistic Reasoning and Decision Making in Sensory-Motor Systems, 46, Springer, 2008, Springer Tracts in Advanced Robotics Series. 〈inria-00295084〉

Partager

Métriques

Consultations de la notice

628

Téléchargements de fichiers

530