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Navigability Graph Extraction From Large-Scale 3D Point Cloud

Imeen Salah 1 Sebastien Kramm 1 Cédric Demonceaux 2 Pascal Vasseur 1, 2
2 VIBOT - VIsion pour la roBOTique [VIBOT CNRS ERL 6000]
Le2i - Laboratoire d'Electronique, d'Informatique et d'Image [EA 7508], CNRS - Centre National de la Recherche Scientifique : ERL6000
Abstract : This paper presents a novel method for summarizing and compression of large-scale 3D models into compact spherical representations. The information is combined into a set of optimized spheres in order to facilitate its use by systems with limited resources (smartphones, robots, UAVs, ...). This vision-based summarizing process is applied in a fully automatic way using jointly photometric, geometric and semantic information of the studied environment. The main contribution of this research is to provide a navigability graph that maximizes the significance of the contents of its nodes while maintaining the full visibility of the environment. Experimental results in summarizing large-scale 3D map demonstrate the feasibility of our approach and evaluate the performance of the algorithm.
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Contributor : Cédric Demonceaux <>
Submitted on : Wednesday, November 21, 2018 - 2:47:43 PM
Last modification on : Monday, March 30, 2020 - 8:41:52 AM
Document(s) archivé(s) le : Friday, February 22, 2019 - 3:07:57 PM


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  • HAL Id : hal-01929921, version 1


Imeen Salah, Sebastien Kramm, Cédric Demonceaux, Pascal Vasseur. Navigability Graph Extraction From Large-Scale 3D Point Cloud. 21st IEEE International Conference on Intelligent Transportation Systems, Nov 2018, Maui, Hawaii, United States. ⟨hal-01929921⟩



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