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.

本文提出了一种称为 SM/VIO 的模型驱动的可视惯性里程计（VIO）算法，该算法在海洋环境下具有较强的鲁棒性，并通过在 VIO 失败时结合机器人运动学模型和本体感知传感器来维持姿态估计