Enhancing Visual Inertial SLAM with Magnetic Measurements
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 41 references that are in the map. First posted on arXiv 16 Sep 2024.
Builds on
10 of 41 references.
References · 10
- 2020ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAMcited by 878
- 2017VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimatorcited by 423
- 2015On-Manifold Preintegration for Real-Time Visual-Inertial Odometrycited by 95
- 2017Quaternion kinematics for the error-state Kalman filtercited by 30
- 2015Modeling and interpolation of the ambient magnetic field by Gaussian processescited by 28
- 2018SVIn2: An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensorcited by 16
- 2019Experimental Comparison of Open Source Visual-Inertial-Based State Estimation Algorithms in the Underwater Domaincited by 16
- 2022High Definition, Inexpensive, Underwater Mappingcited by 9
- 2016Magnetometer calibration using inertial sensorscited by 4
- 2015On Calibration of Three-axis Magnetometercited by 2
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