A Vision-based Control Framework for Real-time Autonomous UUV Operations

2026-08-05

AI Summary

This work addresses the challenge of achieving robust autonomous navigation for unmanned underwater vehicles (UUVs) in dynamic, low-visibility underwater environments by proposing an end-to-end vision-driven control framework. For the first time, this approach integrates visual perception, real-time 3D mapping, and dual-mode local–global localization within a unified architecture. By fusing vision-based SLAM, deep learning–based feature extraction, and multimodal pose estimation, the method significantly enhances system robustness under complex disturbances while maintaining real-time performance. Experimental results demonstrate that the proposed system efficiently constructs consistent 3D maps on both synthetic datasets and real-world UUV platforms, reliably enabling autonomous navigation and critical mission deployment.

Abstract

This paper presents a fully integrated vision-based framework for real-time and robust localization, autonomous navigation, and mapping for unmanned underwater vehicles (UUVs) in dynamic, visually challenging environments. The proposed pipeline enables both net-relative and global localization while generating continuous 3D maps of the surroundings in real-time. The framework was validated on synthetic datasets with ground truth and tested onboard an UUV during autonomous net-relative navigation experiments. Results demonstrate real-time performance and enhanced robustness, supporting vision-driven autonomous navigation and enabling the field deployment of marine robots for critical inspection and mapping tasks in complex underwater environments.

Problem

• autonomous UUV
• vision-based localization
• underwater navigation
• real-time mapping
• challenging underwater environments

Innovation

• vision-based control
• real-time localization
• autonomous UUV navigation
• 3D mapping
• robust underwater perception