A Vision-based Control Framework for Real-time Autonomous UUV Operations
Erik Tjærand Frøland, Marco Job, Md Ether Deowan, Eleni Kelasidi
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.
Create a lesson
Related papers
rMuscle: Robotic Muscle Memory for Efficient Vision-Language-Action Model Inference
Kaijun Zhou, Zhiyang Li, Le Chen et al.
ElastiQP: An Always-Feasible QP Solver for Constrained Robot Control
Daniel Morton, Jon Arrizabalaga, Zachary Manchester et al.
"What's going to happen after I'm gone?": Parent Perspectives on Technology in Supporting Independent Living for Adults with Intellectual Disabilities
Alexander Tyshka, Andrea Macklem-Zabel, Absalat Getachew et al.
Learning Holistic Whole-Body Loco-Manipulation with a Bipedal Mobile Manipulator
Zhongyu Chen, Yuxuan Nai, Qian Chen et al.
CaSCo: Cascade-Aware Soft-Collision Motion Planning
Shivaram Kumar, Gaoyuan Liu, Yoonchang Sung
Examining the Difference in Human Behavior Between Virtual and Real-World Human-Robot Teaming
Sean Dallas, Absalat Getachew, Motaz AbuHijleh et al.