融合视觉与声学数据，实现水下无人艇在渔网环境中的实时定位与三维建图。
Framework for Robust Localization of UUVs and Mapping of Net Pens
- 用视觉数据结合声学信息，估计无人艇相对于渔网的位置和全局姿态。
- 在真实渔场数据上验证，可实时生成高精度3D渔网地图。
- 适合水下机器人自主巡检、智能养殖场景，尤其适用于动态复杂水域。
本文提出一种通用框架，融合视觉与声学传感器数据，提升在高度动态且复杂的水下环境中对无人潜水器（UUV）的定位与建图能力，特别针对鱼类养殖场景。所提流程可仅基于视觉数据获取UUV相对于渔网的相对位姿及渔网深度图。此外，本文还提出一种方法，通过融合相对位姿估计与声学数据来估算UUV的全局位姿。该流程在工业级渔场数据集上进行了验证，结果表明：当使用快速傅里叶变换（FFT）提供的稀疏深度先验，并结合Wavemap方法时，基于视觉的TRU-Depth模型能实时估计出UUV的相对与全局位置，并生成适用于自主导航与巡检任务的详细3D地图。
原文摘要 · Abstract (English)
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.
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