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Conference papers

Incremental and adaptive multi-robot mapping for human scene observation

Jonathan Cohen 1 Laëtitia Matignon 2, 3, 4 Olivier Simonin 3
1 Equipe MAD - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
3 CHROMA - Robots coopératifs et adaptés à la présence humaine en environnements dynamiques
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
4 SMA - Systèmes Multi-Agents
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : This paper aims to use a fleet of mobile robots, each embedding a camera, to optimize the observation of a human dynamic scene. The scene is defined as a sequence of activities, performed by a person in a same place. Mobile robots have to cooperate to find a spatial configuration around the scene that maximizes the joint observation of the human pose skeleton. It is assumed that the robots can communicate but have no map of the environment and no external localisation. This paper presents a concentric navigation topology allowing to keep easily each robot camera towards the scene. This topology is combined with an incremental mapping of the environment in order to limit the complexity of the exploration state space. We also introduce the marginal contribution of each robot observation, to facilitate stability in the search, while the exploration is guided by a meta-heuristics. We developped a simulator that uses skeleton data from real human pose captures. It allows to compare the variants of the approach and to show its features such as adaptation to the dynamic of the scene and robustness to the noise in the observations.
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Contributor : Olivier Simonin <>
Submitted on : Tuesday, August 30, 2016 - 3:18:28 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:50 PM


  • HAL Id : hal-01357857, version 1


Jonathan Cohen, Laëtitia Matignon, Olivier Simonin. Incremental and adaptive multi-robot mapping for human scene observation. ICTAI 2016 28th International Conference on Tools with Artificial Intelligence, IEEE, Nov 2016, San Jose, United States. pp.678--685. ⟨hal-01357857⟩



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