Cross-view and Cross-domain Underwater Localization based on Optical Aerial and Acoustic Underwater Images
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866914990099791872 |
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| author | Santos, Matheus M. Dos De Giacomo, Giovanni G. Drews-Jr, Paulo L. J. Botelho, Silvia S. C. |
| author_facet | Santos, Matheus M. Dos De Giacomo, Giovanni G. Drews-Jr, Paulo L. J. Botelho, Silvia S. C. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2202_07817 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Cross-view and Cross-domain Underwater Localization based on Optical Aerial and Acoustic Underwater Images Santos, Matheus M. Dos De Giacomo, Giovanni G. Drews-Jr, Paulo L. J. Botelho, Silvia S. C. Computer Vision and Pattern Recognition Robotics 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. |
| title | Cross-view and Cross-domain Underwater Localization based on Optical Aerial and Acoustic Underwater Images |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2202.07817 |