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

• URL: http://arxiv.org/abs/2304.01988v1
• Date: Tue, 4 Apr 2023 17:46:20 GMT
• Title: SM/VIO: Robust Underwater State Estimation Switching Between Model-based and Visual Inertial Odometry
• Authors: Bharat Joshi, Hunter Damron, Sharmin Rahman, Ioannis Rekleitis
• Abstract summary: This paper addresses the robustness problem of visual-inertial state estimation for underwater operations. The proposed approach utilizes a model of the robot's kinematics together with proprioceptive sensors to maintain the pose estimate. Health-monitoring tracks the VIO process ensuring timely switches between the two estimators.
• Score: 1.9785872350085876
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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