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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Robotics and AI |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2025.1609765/full |
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| _version_ | 1850157067086594048 |
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| author | Marco Job Marco Job David Botta Victor Reijgwart Luca Ebner Andrej Studer Roland Siegwart Eleni Kelasidi Eleni Kelasidi Eleni Kelasidi |
| author_facet | Marco Job Marco Job David Botta Victor Reijgwart Luca Ebner Andrej Studer Roland Siegwart Eleni Kelasidi Eleni Kelasidi Eleni Kelasidi |
| author_sort | Marco Job |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d9fd88c966dc41bc80da9e243b3ddbdf |
| institution | OA Journals |
| issn | 2296-9144 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Robotics and AI |
| spelling | doaj-art-d9fd88c966dc41bc80da9e243b3ddbdf2025-08-20T02:24:17ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442025-06-011210.3389/frobt.2025.16097651609765Leveraging learned monocular depth prediction for pose estimation and mapping on unmanned underwater vehiclesMarco Job0Marco Job1David Botta2Victor Reijgwart3Luca Ebner4Andrej Studer5Roland Siegwart6Eleni Kelasidi7Eleni Kelasidi8Eleni Kelasidi9Autonomous Systems Lab, Institute of Robotics and Intelligent Systems, Department of Mechanical and Process Engineering, ETH, Zurich, Zurich, SwitzerlandDepartment of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, NorwayAutonomous Systems Lab, Institute of Robotics and Intelligent Systems, Department of Mechanical and Process Engineering, ETH, Zurich, Zurich, SwitzerlandAutonomous Systems Lab, Institute of Robotics and Intelligent Systems, Department of Mechanical and Process Engineering, ETH, Zurich, Zurich, SwitzerlandTethys Robotics, Zurich, SwitzerlandTethys Robotics, Zurich, SwitzerlandAutonomous Systems Lab, Institute of Robotics and Intelligent Systems, Department of Mechanical and Process Engineering, ETH, Zurich, Zurich, SwitzerlandAutonomous Systems Lab, Institute of Robotics and Intelligent Systems, Department of Mechanical and Process Engineering, ETH, Zurich, Zurich, SwitzerlandDepartment of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, NorwayAquaculture Robotics and Automation Group, SINTEF Ocean, Trondheim, NorwayThis 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.https://www.frontiersin.org/articles/10.3389/frobt.2025.1609765/fulllocalizationmappingUUVsdepth predictionaquaculture |
| spellingShingle | Marco Job Marco Job David Botta Victor Reijgwart Luca Ebner Andrej Studer Roland Siegwart Eleni Kelasidi Eleni Kelasidi Eleni Kelasidi Leveraging learned monocular depth prediction for pose estimation and mapping on unmanned underwater vehicles Frontiers in Robotics and AI localization mapping UUVs depth prediction aquaculture |
| title | Leveraging learned monocular depth prediction for pose estimation and mapping on unmanned underwater vehicles |
| title_full | Leveraging learned monocular depth prediction for pose estimation and mapping on unmanned underwater vehicles |
| title_fullStr | Leveraging learned monocular depth prediction for pose estimation and mapping on unmanned underwater vehicles |
| title_full_unstemmed | Leveraging learned monocular depth prediction for pose estimation and mapping on unmanned underwater vehicles |
| title_short | Leveraging learned monocular depth prediction for pose estimation and mapping on unmanned underwater vehicles |
| title_sort | leveraging learned monocular depth prediction for pose estimation and mapping on unmanned underwater vehicles |
| topic | localization mapping UUVs depth prediction aquaculture |
| url | https://www.frontiersin.org/articles/10.3389/frobt.2025.1609765/full |
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