Combining 3D SLAM and visual tracking to reach and retrieve objects in daily-life indoor environments

Abstract : In this paper, we draw perspectives to endow a humanoid robot with capabilities to reach known object in an indoor environment by combining continuous monitoring and building using SLAM and visual tracking. We integrates and exploits two key features: object recognition using the toolbox BLORT, and a SLAM (Simultaneous Localization And Mapping) software, that unifies volumetric 3D modeling and image-based key-frame modeling to be used in tracking. Using these two modules , we show that it is possible to reach a given object in the environment providing its model is registered and known. Our integration software is exemplified using a humanoid robot HRP-2, we present experimental results that illustrates the performance of our approach.
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Communication dans un congrès
URAI: Ubiquitous Robots and Ambient Intelligence, Nov 2015, Kuala Lumpur, Malaysia. Ubiquitous Robots and Ambient Intelligence (URAI), 2014 11th International Conference on, 2014, 〈10.1109/URAI.2014.7057501〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01247142
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Pierre Gergondet, Damien Petit, Maxime Meilland, Abderrahmane Kheddar, Andrew Comport, et al.. Combining 3D SLAM and visual tracking to reach and retrieve objects in daily-life indoor environments. URAI: Ubiquitous Robots and Ambient Intelligence, Nov 2015, Kuala Lumpur, Malaysia. Ubiquitous Robots and Ambient Intelligence (URAI), 2014 11th International Conference on, 2014, 〈10.1109/URAI.2014.7057501〉. 〈lirmm-01247142〉

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