From sparse SLAM to dense mapping for UAV autonomous navigation - Equipe Robot interaction, Ambient system, Machine learning, Behaviour, Optimization Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

From sparse SLAM to dense mapping for UAV autonomous navigation

Résumé

Autonomous or semi-autonomous navigation of UAVs is of great interest in the Defense and Security domains, as it significantly improves their efficiency and responsiveness during operations. The perception of the environment and in particular the dense and metric 3D mapping in real time is a priority for navigation and obstacle avoidance. We therefore present our strategy to jointly estimate a dense 3D map by combining a sparse map estimated by a state-of-the-art Simultaneous Localization and Mapping (SLAM) system and a dense depth map predicted by a monocular self-supervised method. Then, a lightweight and volumetric multi-view fusion solution is used to build and update a voxel map.
Fichier principal
Vignette du fichier
manuscript_v2 (1).pdf (2.42 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04106441 , version 1 (25-05-2023)

Identifiants

Citer

Yassine Habib, Panagiotis Papadakis, Antoine Fagette, Cédric Le Barz, Tiago Gonçalves, et al.. From sparse SLAM to dense mapping for UAV autonomous navigation. SPIE Defense + Commercial Sensing, Apr 2023, Orlando, United States. ⟨10.1117/12.2663706⟩. ⟨hal-04106441⟩
53 Consultations
227 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More