SURGE improves underwater vehicle location and 3D mapping together
SURGE: Sonar-fUsed Reconstruction and localization via image-gated Graph Estimation
Robotics
Summary
Underwater vehicles like small robots explore places where GPS doesn't work, so they must figure out where they are and what surrounds them using sensors. Cameras show detailed pictures but can get confused about distances, while sonar provides distance measurements but less detail. The authors created SURGE, which combines camera and sonar data to track the vehicle's path and build accurate 3D maps at the same time. This method helps the vehicle understand its location better and creates clearer, metric 3D models underwater.
What this means in practice
- •For underwater vehicle operators: Guide remotely operated vehicles for inspection missions by improving their path tracking and 3D mapping accuracy simultaneously underwater.
- •For marine infrastructure inspectors: Produce precise metric 3D maps of underwater structures to support maintenance and safety checks without external positioning systems.
Authors
Mohammed Ibrahim M, Vallabh Deogaonkar, Trung Dong, Jane Shin, Abhilash Somayajula, Xiaomin Lin
Abstract
Remotely operated vehicles (ROVs) are widely used to explore and inspect underwater environments such as caves, shipwrecks, and submerged infrastructure. These missions require accurate 3D understanding of the surrounding environment, which depends on both reliable vehicle localization and metric scene reconstruction. However, external positioning is often unavailable underwater, requiring small ROVs to rely primarily on onboard perception. Optic vision provides rich visual and geometric information but suffers from scale ambi- guity and trajectory drift, whereas 2D imaging sonar provides metric range but incomplete 3D geometry. Existing underwater reconstruction approaches typically address these limitations separately or assume known sensor poses, leaving localization and reconstruction disconnected. We present SURGE, a camera sonar framework that jointly estimates the ROV trajectory and target location by integrating visual and acoustic observations within a factor graph, then uses the recovered metric poses for sonar Gaussian splatting. Experiments on real underwater RGB sonar observations show that SURGE substantially improves localization consistency over conventional vision based pose estimation and produces a more compact, natively metric reconstruction than RGB Gaussian splatting baselines.