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Conference Papers Year : 2017

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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Dates and versions

hal-01691568 , version 1 (24-01-2018)

Identifiers

  • HAL Id : hal-01691568 , version 1

Cite

Imeen Ben 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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