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Multi-sensor semantic mapping and exploration of indoor environments

Abstract : The human perception of the external world appears as a natural, immediate and effortless task. It is achieved through a number of "low-level" sensory-motor processes that provide a high-level representation adapted to complex reasoning and decision. Compared to these representations, mobile robots usually provide only low-level obstacle maps that lack such high- level information. We present a mobile robot whose goal is to autonomously explore an unknown indoor environment and to build a semantic map containing high-level information similar to those extracted by humans and that will be rapidly and easily interpreted by users to assess the situation. This robot was developed under the1 Panoramic and Active Camera for Object Mapping (PACOM) project whose goal is to participate2 in a French exploration and mapping contest called CAROTTE . We will detail in particular how we integrated visual object recognition, room detection, semantic mapping, and exploration. We demonstrate the performances of our system in an indoor environment.
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Contributor : David Filliat <>
Submitted on : Thursday, December 15, 2011 - 4:53:41 PM
Last modification on : Thursday, January 21, 2021 - 9:26:01 AM
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Islem Jebari, Stéphane Bazeille, Emmanuel Battesti, Hassène Tekaya, Markus Klein, et al.. Multi-sensor semantic mapping and exploration of indoor environments. 3rd International Conference on Technologies for Practical Robot Applications (TePRA), 2011, United States. pp.151 - 156, ⟨10.1109/TEPRA.2011.5753498⟩. ⟨hal-00652471⟩



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