SM/VIO: Robust Underwater State Estimation Switching Between Model-based and Visual Inertial Odometry
Bharat Joshi, Hunter Damron, Sharmin Rahman, Ioannis Rekleitis
In IEEE International Conference on Robotics and Automation (ICRA) 2023.
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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BibTeX
@inproceedings{JoshiICRA2023,
author = {Bharat Joshi and Hunter Damron and Sharmin Rahman and
Ioannis Rekleitis},
title = {{SM/VIO: Robust Underwater State Estimation Switching
Between Model-based and Visual Inertial Odometry}},
booktitle = {IEEE International Conference on Robotics and Automation
(ICRA)},
year = {2023},
pages = {5192-5199},
address = {London, UK},
doi = {10.1109/ICRA48891.2023.10161407}
}
Thu Apr 18 06:21:02 EDT 2024