SM/VIO: Robust Underwater State Estimation Switching Between Model-based and Visual Inertial Odometry
It addresses the critical need for reliable pose estimation in underwater robots operating in poor visibility conditions, though it is incremental as it combines existing methods with switching mechanisms.
This paper tackles the robustness problem of visual-inertial state estimation for underwater robots by proposing SM/VIO, which switches between model-based and visual-inertial odometry to maintain pose estimates during failures. Experimental results from deployments on the Aqua2 vehicle demonstrate its robustness in challenging environments like coral reefs and shipwrecks.
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.