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.
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This content will become publicly available on May 13, 2025
Enhancing Visual Inertial SLAM with Magnetic Measurements
This paper presents an extension to visual inertial odometry (VIO) by introducing tightly-coupled fusion of
magnetometer measurements. A sliding window of keyframes
is optimized by minimizing re-projection errors, relative inertial errors, and relative magnetometer orientation errors.
The results of IMU orientation propagation are used to efficiently transform magnetometer measurements between frames
producing relative orientation constraints between consecutive
frames. The soft and hard iron effects are calibrated using an
ellipsoid fitting algorithm. The introduction of magnetometer
data results in significant reductions in the orientation error
and also in recovery of the true yaw orientation with respect
to the magnetic north. The proposed framework operates in
all environments with slow-varying magnetic fields, mainly
outdoors and underwater. We have focused our work on the
underwater domain, especially in underwater caves, as the
narrow passage and turbulent flow make it difficult to perform
loop closures and reset the localization drift. The underwater
caves present challenges to VIO due to the absence of ambient
light and the confined nature of the environment, while also
being a crucial source of fresh water and providing valuable
historical records. Experimental results from underwater caves
demonstrate the improvements in accuracy and robustness
introduced by the proposed VIO extension.
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- PAR ID:
- 10547542
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-8457-4
- Page Range / eLocation ID:
- 10012 to 10019
- Format(s):
- Medium: X
- Location:
- Yokohama, Japan
- Sponsoring Org:
- National Science Foundation
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