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This content will become publicly available on September 25, 2024

Title: Hybrid Visual Inertial Odometry for Robust Underwater Estimation
Vision-based state estimation is challenging in underwater environments due to color attenuation, low visibility and floating particulates. All visual-inertial estimators are prone to failure due to degradation in image quality. However, underwater robots are required to keep track of their pose during field deployments. We propose robust estimator fusing the robot's dynamic and kinematic model with proprioceptive sensors to propagate the pose whenever visual-inertial odometry (VIO) fails. To detect the VIO failures, health tracking is used, which enables switching between pose estimates from VIO and a kinematic estimator. Loop closure implemented on weighted posegraph for global trajectory optimization. Experimental results from an Aqua2 Autonomous Underwater Vehicle field deployments demonstrates the robustness of our approach over different underwater environments such as over shipwrecks and coral reefs. The proposed hybrid approach is robust to VIO failures producing consistent trajectories even in harsh conditions.  more » « less
Award ID(s):
1943205 2024741
NSF-PAR ID:
10496709
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
OCEANS 2023 - MTS/IEEE U.S. Gulf Coast
ISSN:
0197-7385
Page Range / eLocation ID:
1 to 7
Format(s):
Medium: X
Location:
Biloxi, MS, USA
Sponsoring Org:
National Science Foundation
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