Framework for Robust Localization of UUVs and Mapping of Net Pens
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916408522178560 |
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| author | Botta, David Ebner, Luca Studer, Andrej Reijgwart, Victor Siegwart, Roland Kelasidi, Eleni |
| author_facet | Botta, David Ebner, Luca Studer, Andrej Reijgwart, Victor Siegwart, Roland Kelasidi, Eleni |
| contents | This paper presents a general framework integrating vision and acoustic sensor data to enhance localization and mapping in highly dynamic and complex underwater environments, with a particular focus on fish farming. The proposed pipeline is suited to obtain both the net-relative pose estimates of an Unmanned Underwater Vehicle (UUV) and the depth map of the net pen purely based on vision data. Furthermore, this paper presents a method to estimate the global pose of an UUV fusing the net-relative pose estimates with acoustic data. The pipeline proposed in this paper showcases results on datasets obtained from industrial-scale fish farms and successfully demonstrates that the vision-based TRU-Depth model, when provided with sparse depth priors from the FFT method and combined with the Wavemap method, can estimate both net-relative and global position of the UUV in real time and generate detailed 3D maps suitable for autonomous navigation and inspection purposes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15475 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Framework for Robust Localization of UUVs and Mapping of Net Pens Botta, David Ebner, Luca Studer, Andrej Reijgwart, Victor Siegwart, Roland Kelasidi, Eleni Robotics This paper presents a general framework integrating vision and acoustic sensor data to enhance localization and mapping in highly dynamic and complex underwater environments, with a particular focus on fish farming. The proposed pipeline is suited to obtain both the net-relative pose estimates of an Unmanned Underwater Vehicle (UUV) and the depth map of the net pen purely based on vision data. Furthermore, this paper presents a method to estimate the global pose of an UUV fusing the net-relative pose estimates with acoustic data. The pipeline proposed in this paper showcases results on datasets obtained from industrial-scale fish farms and successfully demonstrates that the vision-based TRU-Depth model, when provided with sparse depth priors from the FFT method and combined with the Wavemap method, can estimate both net-relative and global position of the UUV in real time and generate detailed 3D maps suitable for autonomous navigation and inspection purposes. |
| title | Framework for Robust Localization of UUVs and Mapping of Net Pens |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.15475 |