Framework for Robust Localization of UUVs and Mapping of Net Pens
<|reference_start|>Framework for Robust Localization of UUVs and Mapping of Net Pens: 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.<|reference_end|>
@article{botta2024framework,
    title={Framework for Robust Localization of UUVs and Mapping of Net Pens},
    author={David Botta, Luca Ebner, Andrej Studer, Victor Reijgwart, Roland
  Siegwart, Eleni Kelasidi},
    journal={arXiv preprint arXiv:2409.15475},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15475},
    primaryClass={cs.RO}
}