IEEE robotics and automation letters, 2022-04, Vol.7 (2), p.4969-4974
2022
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Details
- Autor(en) / Beteiligte
- Titel
- Cross-View and Cross-Domain Underwater Localization Based on Optical Aerial and Acoustic Underwater Images
- Ist Teil von
- IEEE robotics and automation letters, 2022-04, Vol.7 (2), p.4969-4974
- Ort / Verlag
- Piscataway: IEEE
- Erscheinungsjahr
- 2022
- Quelle
- IEEE/IET Electronic Library
- Beschreibungen/Notizen
- Cross-view image matches have been widely explored on terrestrial image localization using aerial images from drones or satellites. This study expands the cross-view image match idea and proposes a cross-domain and cross-view localization framework. The method identifies the correlation between color aerial images and underwater acoustic images to improve the localization of underwater vehicles that travel in partially structured environments such as harbors and marinas. The approach is validated on a real dataset acquired by an underwater vehicle in a marina. The results show an improvement in the localization when compared to the dead reckoning of the vehicle.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 2377-3766eISSN: 2377-3766DOI: 10.1109/LRA.2022.3154482Titel-ID: cdi_proquest_journals_2637443138
- Format
- –
- Schlagworte
- Dead reckoning, Deep learning for visual perception, Domains, Drone aircraft, Image processing, Image segmentation, Localization, Location awareness, Marinas, marine robotics, Neural networks, Optical imaging, Satellite imagery, Satellites, Underwater acoustics, Underwater vehicles