Leveraging learned monocular depth prediction for pose estimation and mapping on unmanned underwater vehicles
This paper presents a general framework that integrates visual and acoustic sensor data to enhance localization and mapping in complex, highly dynamic underwater environments, with a particular focus on fish farming. The pipeline enables net-relative pose estimation for Unmanned Underwater Vehicles...
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| Autori principali: | , , , , , , |
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| Natura: | Articolo |
| Lingua: | inglese |
| Pubblicazione: |
Frontiers Media S.A.
2025-06-01
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| Serie: | Frontiers in Robotics and AI |
| Soggetti: | |
| Accesso online: | https://www.frontiersin.org/articles/10.3389/frobt.2025.1609765/full |
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| Riassunto: | This paper presents a general framework that integrates visual and acoustic sensor data to enhance localization and mapping in complex, highly dynamic underwater environments, with a particular focus on fish farming. The pipeline enables net-relative pose estimation for Unmanned Underwater Vehicles (UUVs) and depth prediction within net pens solely from visual data by combining deep learning-based monocular depth prediction with sparse depth priors derived from a classical Fast Fourier Transform (FFT)-based method. We further introduce a method to estimate a UUV’s global pose by fusing these net-relative estimates with acoustic measurements, and demonstrate how the predicted depth images can be integrated into the wavemap mapping framework to generate detailed 3D maps in real-time. Extensive evaluations on datasets collected in industrial-scale fish farms confirm that the presented framework can be used to accurately estimate a UUV’s net-relative and global position in real-time, and provide 3D maps suitable for autonomous navigation and inspection. |
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| ISSN: | 2296-9144 |