SM/VIO: Robust Underwater State Estimation Switching Between Model-based and Visual Inertial Odometry

TL;DR

This paper presents a robust underwater state estimation framework that switches between visual-inertial odometry and model-based kinematic estimation, ensuring continuous pose tracking in challenging environments.

Contribution

It introduces a hybrid estimator that combines visual-inertial odometry with model-based kinematics and health monitoring for reliable underwater localization.

Findings

Demonstrated robustness over coral reefs and shipwrecks.

Achieved continuous pose estimation despite visual failures.

Effective switching mechanism between estimators.

Abstract

This paper addresses the robustness problem of visual-inertial state estimation for underwater operations. Underwater robots operating in a challenging environment are required to know their pose at all times. All vision-based localization schemes are prone to failure due to poor visibility conditions, color loss, and lack of features. The proposed approach utilizes a model of the robot's kinematics together with proprioceptive sensors to maintain the pose estimate during visual-inertial odometry (VIO) failures. Furthermore, the trajectories from successful VIO and the ones from the model-driven odometry are integrated in a coherent set that maintains a consistent pose at all times. Health-monitoring tracks the VIO process ensuring timely switches between the two estimators. Finally, loop closure is implemented on the overall trajectory. The resulting framework is a robust estimator switching between model-based and visual-inertial odometry (SM/VIO). Experimental results from numerous deployments of the Aqua2 vehicle demonstrate the robustness of our approach over coral reefs and a shipwreck.