Geometric and Bayesian Models for Safe Navigation in Dynamic Environments

Christian Laugier 1 Dizan Alejandro Vasquez Govea 1 Manuel Yguel 1 Thierry Fraichard 1 Olivier Aycard 1
1 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : Autonomous navigation in open and dynamic environments is an important challenge, requiring to solve several difficcult research problems located on the cutting edge of the state of the art. Basically, these problems may be classiffied into three main categories: a) SLAM in dynamic environments; b) Detection, characterization, and behavior prediction of the potential moving obstacles; and c) On-line motion planning and safe navigation decision based on world state predictions. This paper addresses some aspects of these problems and presents our latest approaches and results. The solutions we have implemented are mainly based on the followings paradigms: multiscale world representation of static obstacles based on the wavelet occupancy grid; adaptative clustering for moving obstacle detection inspired on Kohonen networks and the growing neural gas algorithm; and characterization and motion prediction of the observed moving entities using Hidden Markov Models coupled with a novel algorithm for structure and parameter learning.
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Article dans une revue
Intelligent Service Robotics, Springer Verlag, 2008, 1 (1)
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https://hal.inria.fr/inria-00180741
Contributeur : Thierry Fraichard <>
Soumis le : jeudi 28 février 2008 - 10:26:47
Dernière modification le : mercredi 11 avril 2018 - 01:55:15
Document(s) archivé(s) le : mardi 21 septembre 2010 - 15:52:30

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Christian Laugier, Dizan Alejandro Vasquez Govea, Manuel Yguel, Thierry Fraichard, Olivier Aycard. Geometric and Bayesian Models for Safe Navigation in Dynamic Environments. Intelligent Service Robotics, Springer Verlag, 2008, 1 (1). 〈inria-00180741v2〉

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