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Communication Dans Un Congrès Année : 2023

Dual occupancy and knowledge maps management for optimal traversability risk analysis

Mohamed Benrabah
  • Fonction : Auteur
Jérémy Morceaux
  • Fonction : Auteur
Romuald Aufrère
Roland Chapuis

Résumé

In a context of autonomous driving, perception of the surrounding is a crucial task. It characterizes the vehicle's ability to simultaneously model its surroundings accurately and maintain its position in the environment. In this article, a new framework of mobile robot perception and risk assessment is proposed. Our approach aims to leverage the simultaneous combination of the standard occupancy grid map with a new map that we have called "knowledge map". This proposal was motivated by the fact that risk arises not only from obstacles but also from the lack of knowledge. Using this framework, we are able to assess the risk, mainly of collision, over a given path P and therefore compute an optimal navigation control of the robot. Thanks to the proposed Bayesian framework the paper also shows how we can combine both local measurements and existing map (eg. OpenStreetMap) and also take account of the robot's localization errors.
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Dates et versions

hal-04159142 , version 1 (11-07-2023)

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  • HAL Id : hal-04159142 , version 1

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Mohamed Benrabah, Charifou Orou Mousse, Jérémy Morceaux, Romuald Aufrère, Roland Chapuis, et al.. Dual occupancy and knowledge maps management for optimal traversability risk analysis. 26th International Conference on Information Fusion, Jun 2023, Charleston, SC, United States. ⟨hal-04159142⟩
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