Leveraging learned monocular depth prediction for pose estimation and mapping on unmanned underwater vehicles.

IF 3 Q2 ROBOTICS
Frontiers in Robotics and AI Pub Date : 2025-06-26 eCollection Date: 2025-01-01 DOI:10.3389/frobt.2025.1609765
Marco Job, David Botta, Victor Reijgwart, Luca Ebner, Andrej Studer, Roland Siegwart, Eleni Kelasidi
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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.

利用学习到的单目深度预测在无人水下航行器上进行姿态估计和映射。
本文提出了一个集成视觉和声学传感器数据的总体框架,以增强在复杂,高度动态的水下环境中的定位和绘图,特别关注鱼类养殖。该管道通过将基于深度学习的单目深度预测与基于经典快速傅里叶变换(FFT)方法的稀疏深度先验相结合,实现了无人水下航行器(uuv)的净相对姿态估计和网圈内深度预测。我们进一步介绍了一种方法,通过融合这些净相对估计与声学测量来估计UUV的全球姿态,并演示了如何将预测的深度图像集成到波浪图制图框架中,以实时生成详细的3D地图。对工业规模养鱼场收集的数据集进行了广泛的评估,证实了所提出的框架可以用来准确地实时估计UUV的净相对位置和全球位置,并提供适合自主导航和检查的3D地图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.50
自引率
5.90%
发文量
355
审稿时长
14 weeks
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.
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