Magnetic inertial system improves robot location where gps and cameras fail
A Tightly Coupled Magnetic-Inertial SLAM System for Field Robots
Robotics
Summary
Robots working underground or in smoky, dark places often get lost because GPS and cameras don't work well there. The paper introduces a new system called MISLAM that uses sensors detecting magnetic fields and motion to help robots figure out where they are. The authors tested their system indoors and showed it can track location more accurately than older methods, especially when cameras can't see well. However, they noted vertical positioning still needs improvement.
What this means in practice
- •For robotics engineers: Enable location tracking for robots operating underground or in smoke-filled environments where GPS and cameras are unreliable.
- •For search and rescue teams: Support better navigation of ground and aerial robots in disaster zones with poor visibility using magnetic-inertial sensing.
Authors
Chuan Huang, Tongxing Peng
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 contact. 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 correction 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 support magnetic-inertial SLAM as a complementary localization modality for robotic sensor suites operating under degraded visual conditions.