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

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

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...

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Bibliographic Details
Main Authors: Marco Job, David Botta, Victor Reijgwart, Luca Ebner, Andrej Studer, Roland Siegwart, Eleni Kelasidi
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-06-01
Series:Frontiers in Robotics and AI
Subjects:
localization
mapping
UUVs
depth prediction
aquaculture
Online Access:https://www.frontiersin.org/articles/10.3389/frobt.2025.1609765/full
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