Underwater robots improve location tracking using combined sensors
SVIn+: Multi-Modal Framework for Robust Underwater State Estimation using 3D Sonar, Visual-Inertial, Water-Pressure, and DVL
Robotics
Summary
Tracking the position of underwater robots is very hard because of poor visibility and challenging conditions. The authors created SVIn+, a system that combines many sensors like cameras, sonar, water pressure sensors, and velocity logs to estimate a robot’s location more reliably. They improved an existing method by integrating all these data sources together in a smart way to reduce errors. They tested their system at real underwater sites like shipwrecks and coral reefs, showing better accuracy and stability in tracking.
What this means in practice
- •For underwater robotics engineers: Build underwater vehicles that can navigate reliably by combining visual, inertial, sonar, pressure, and velocity data to reduce navigation errors.
- •For marine ecologists: Use improved vehicle tracking to conduct long-duration underwater surveys at coral reefs or shipwrecks with better positional accuracy.
Authors
Chinmay Burgul, Xiaoming Zhao, Yewei Huang, Michalis Chatzispyrou, Marios Xanthidis, Ioannis Rekleitis, Alberto Quattrini Li
Abstract
As expansion and scalability in underwater oper- ations becomes more important and challenging, autonomous underwater vehicles (AUVs) become more relevant and the necessity for reliable autonomy more critical. A fundamental challenge in any autonomous underwater operation, from control to safe navigation and mapping, is state estimation; a notorious hard problem to address, especially in low visibility conditions. In this paper, we tackle this problem, by presenting SVIn+, a multi-sensor framework for robust state estimation of AUVs equipped with an IMU, cameras, a water-pressure sensor, a Doppler Velocity Log (DVL), and a compact 3D sonar. This framework extends an established open-source state-of-the-art visual-inertial SLAM, SVIn, by integrating water-pressure and DVL measurements into a tightly-coupled nonlinear optimiza- tion formulation resulting in improved estimates. To leverage the structural information of recently available 3D acoustic sonars while maintaining computational efficiency, we introduce a hierarchical architecture that incorporates estimates from a decoupled front-end via KISS-ICP, treated as relative pose factors within the factor graph. The proposed framework is validated through field deployments in a shipwreck and coral reef, demonstrating drift mitigation and global consistency. To support further research in underwater multi-modal fusion, the code and the datasets can be found on project website https://cmburgul.github.io/projects/svin+/ https://dartmouthrobotics.github.io/svin-plus