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Summarizing Large Scale 3D Point Cloud for Navigation Tasks

Abstract : Democratization of 3D sensor devices makes 3D maps building easier especially in long term mapping and autonomous navigation. In this paper we present a new method for summarizing a 3D map (dense cloud of 3D points). This method aims to extract a summary map facilitating the use of this map by navigation systems with limited resources (smartphones, cars, robots...). This Vision-based summarizing process is applied in a fully automatic way using the photometric, geometric and semantic information of the studied environment.
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Contributor : Cédric Demonceaux <>
Submitted on : Wednesday, January 24, 2018 - 10:21:24 AM
Last modification on : Monday, March 30, 2020 - 8:40:57 AM
Document(s) archivé(s) le : Thursday, May 24, 2018 - 2:17:18 PM


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  • HAL Id : hal-01691568, version 1


Imeen Salah, Sebastien Kramm, Cédric Demonceaux, Pascal Vasseur. Summarizing Large Scale 3D Point Cloud for Navigation Tasks. IEEE 20th International Conference on Intelligent Transportation Systems, Oct 2017, Yokohama, Japan. ⟨hal-01691568⟩



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