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This research details a vision-based control framework enabling real-time localization, navigation, and 3D mapping for unmanned underwater vehicles in complex environments. By validating the system on synthetic data and live UUV experiments, the study demonstrates robust performance essential for autonomous marine inspection tasks without relying on GPS or acoustic positioning. The work advances robotic autonomy through computer vision techniques specifically adapted for dynamic, visually degraded underwater conditions rather than general robotics applications.
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A Vision-based Control Framework for Real-time Autonomous UUV Operations

arXiv1 min read

arXiv:2608.04723v1 Announce Type: new 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.

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.

Robotics (cs.RO)

arXiv:2608.04723 [cs.RO]

(or arXiv:2608.04723v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.04723 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 188 words

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