A Tightly Coupled Magnetic-Inertial SLAM System for Field Robots
A Tightly Coupled Magnetic-Inertial SLAM System for Field Robots
This research introduces MISLAM, a tightly coupled magnetic-inertial simultaneous localization and mapping system designed for field robots operating in GNSS-denied environments such as mines, tunnels, and disaster areas.
The paper presents MISLAM, a tightly coupled magnetic-inertial SLAM system evaluated offline on three indoor datasets, reporting endpoint closure error reductions of 45.7% to 78.0% relative to magnetic-inertial odometry and maintaining tracking under poor illumination with a horizontal closure error of 0.332 m, while noting vertical drift remains a limitation.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
horizontal closure error
0.332
View original evidence
on using an error-state Kalman filter. Offline evaluation on three indoor datasets collected with a handheld rig shows reductions in total endpoint closure error of 45.7% - 78.0% relative to magnetic-inertial odometry. Under poor illumination, MISLAM maintains tracking after visual-inertial odometry fails, achieving a horizontal closure error of 0.332 m, although vertical drift remains a limitation. These results sup
Open source S4
Dataset details reported by the source
- Tasks
- localization Open source S2
- Embodiments
- field robots Open source S2
- paper: not_reported
What remains unknown
Dataset licensing and commercial-use rights are not established here.
Source excerpts and review record
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ntact. This letter presents MISLAM, a tightly coupled magnetic-inertial simultaneous localization and mapping system using an IMU and a nine-magnetometer array, without relying on external odometry or platform-specific motion assumptions. The system combines the inertial navigation model with local polynomial magnetic-field models for short-term motion estimation and a global Gaussian process model for drift correcti
Open source S3
arXiv:2611.03106v1 Announce Type: new Abstract: Field robots operating in mines, tunnels, and disaster areas require reliable localization despite GNSS denial and degraded camera and LiDAR measurements caused by dust, smoke, or poor illumination. Missions involving both ground and aerial robots further motivate complementary sensing modalities that do not depend on visibility, preinstalled infrastructure, or wheel co
Open source S2
on using an error-state Kalman filter. Offline evaluation on three indoor datasets collected with a handheld rig shows reductions in total endpoint closure error of 45.7% - 78.0% relative to magnetic-inertial odometry. Under poor illumination, MISLAM maintains tracking after visual-inertial odometry fails, achieving a horizontal closure error of 0.332 m, although vertical drift remains a limitation. These results sup
Open source S4
port magnetic-inertial SLAM as a complementary localization modality for robotic sensor suites operating under degraded visual conditions.
Open source S5
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Source：arXiv Robotics — research abstracts · arxiv.org