UUVのロバストな自己位置推定と網生け簀マッピングのためのフレームワーク
Framework for Robust Localization of UUVs and Mapping of Net Pens
視覚と音響センサを融合し、魚養殖場の網生け簀に対するUUVの相対・絶対位置推定と3Dマッピングをリアルタイムで行うフレームワークを提案した。
著者: David Botta, Luca Ebner, Andrej Studer, Victor Reijgwart, Roland Siegwart, Eleni Kelasidi
分類: cs.RO
原文アブストラクト
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.
