Accéder directement au contenu Accéder directement à la navigation
Communication dans un congrès

Speed and Memory Efficient Dense RGB-D SLAM in Dynamic Scenes

Abstract : Real-time dense 3D localization and mapping systems are required to enable robotics platforms to interact in and with their environments. Several solutions have used surfel representations to model the world. While they produce impressive results, they require heavy and costly hardware to operate properly. Many of them are also limited to static environments and small inter-frame motions.Whereas most of the state of the art approaches focus on the accuracy of the reconstruction, we assume that many robotics applications do not require a high resolution level in the rebuilt surface and can benefit from a less accurate but less expensive map, so as to gain in run-time and memory efficiency. In this paper we propose a fast RGB-D SLAM articulated around a rough and lightweight 3D representation for dense compact mapping in dynamic indoor environment, targeting mainstream computing platforms.A simple and fast formulation to detect and filter out dynamic elements is also presented.We show the robustness of our system, its low memory requirement and the good performance it enables.
Liste complète des métadonnées

https://hal.univ-grenoble-alpes.fr/hal-03143986
Contributeur : Bruce Canovas <>
Soumis le : mercredi 17 février 2021 - 11:15:48
Dernière modification le : mercredi 7 juillet 2021 - 10:56:05
Archivage à long terme le : : mardi 18 mai 2021 - 18:32:00

Fichier

iros2020_author.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Bruce Canovas, Michèle Rombaut, Amaury Nègre, Denis Pellerin, Serge Olympieff. Speed and Memory Efficient Dense RGB-D SLAM in Dynamic Scenes. IROS 2020 - IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct 2020, Las Vegas, United States. pp.4996-5001, ⟨10.1109/IROS45743.2020.9341542⟩. ⟨hal-03143986⟩

Partager

Métriques

Consultations de la notice

152

Téléchargements de fichiers

277