Search papers, labs, and topics across Lattice.
This paper introduces a comprehensive vision-based framework that facilitates real-time localization, navigation, and mapping for unmanned underwater vehicles (UUVs) operating in challenging environments. By enabling both net-relative and global localization while continuously generating 3D maps, the framework significantly enhances the operational capabilities of UUVs. Validation through synthetic datasets and onboard experiments shows robust performance, paving the way for effective marine robot deployment in critical inspection and mapping tasks.
Real-time vision-based navigation could revolutionize how UUVs operate in complex underwater environments, enabling unprecedented levels of autonomy and mapping accuracy.
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.
