An embarrassingly simple approach for visual navigation of forest environments.
compliant obstacles
depth prediction
forest simulation
low-cost sensors
low-viewpoint forest navigation
off-road navigation
small-sized rovers
sparse swarms
Journal
Frontiers in robotics and AI
ISSN: 2296-9144
Titre abrégé: Front Robot AI
Pays: Switzerland
ID NLM: 101749350
Informations de publication
Date de publication:
2023
2023
Historique:
received:
01
11
2022
accepted:
14
06
2023
medline:
14
7
2023
pubmed:
14
7
2023
entrez:
14
7
2023
Statut:
epublish
Résumé
Navigation in forest environments is a challenging and open problem in the area of field robotics. Rovers in forest environments are required to infer the traversability of
Identifiants
pubmed: 37448877
doi: 10.3389/frobt.2023.1086798
pii: 1086798
pmc: PMC10338120
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1086798Informations de copyright
Copyright © 2023 Niu, Newlands, Zauner and Tarapore.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Références
Sci Robot. 2018 Jan 31;3(14):
pubmed: 33141701
Front Robot AI. 2020 Jul 02;7:83
pubmed: 33501250
Sci Robot. 2022 May 4;7(66):eabm5954
pubmed: 35507682
PLoS One. 2017 Jun 21;12(6):e0178448
pubmed: 28636611
Front Robot AI. 2022 Aug 22;9:887910
pubmed: 36071857