期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 7, 期 2, 页码 4969-4974出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2022.3154482
关键词
Deep learning for visual perception; marine robotics; localization
类别
资金
- CNPq
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior -Brasil (CAPES) [001]
- INCT-Mar COI - CNPq [610012/2011-8]
This study proposes a cross-domain and cross-view localization framework that improves the localization of underwater vehicles in partially structured environments by identifying the correlation between color aerial images and underwater acoustic images.
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.
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