Marco Job, David Botta, Victor Reijgwart, Luca Ebner, Andrej Studer, Roland Siegwart, Eleni Kelasidi
{"title":"Leveraging learned monocular depth prediction for pose estimation and mapping on unmanned underwater vehicles.","authors":"Marco Job, David Botta, Victor Reijgwart, Luca Ebner, Andrej Studer, Roland Siegwart, Eleni Kelasidi","doi":"10.3389/frobt.2025.1609765","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1609765"},"PeriodicalIF":3.0000,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12240768/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Robotics and AI","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/frobt.2025.1609765","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Frontiers in Robotics and AI publishes rigorously peer-reviewed research covering all theory and applications of robotics, technology, and artificial intelligence, from biomedical to space robotics.