Inertial Magnetic SLAM Systems Using Low-Cost Sensors

2025-12-10

AI Summary

Traditional magnetic SLAM systems suffer from error accumulation in unmapped areas due to reliance on high-precision visual or wheel odometry. To address this, we propose the first fully 3D, vision-free inertial–magnetic SLAM system, operating solely with low-cost IMUs, a magnetometer array, and a barometer—enabling robust simultaneous localization and mapping under GPS-denied, low-light, dusty, or smoky conditions. Methodologically, we introduce a novel loosely–tightly coupled dual-filter architecture that integrates IMU preintegration, calibrated magnetometer-array gradient estimation, multi-scale magnetic field modeling (capturing both local disturbances and global background fields), and barometric altitude constraints. Experimental results show the tightly coupled variant achieves ~1 m/100 m positioning error—significantly outperforming its loosely coupled counterpart. Validation in underground mine and fire-simulation environments demonstrates bounded-error localization and scalable multi-scale mapping, providing a deployable, low-power solution for emergency navigation.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Low Level & Physics-based Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Spatially inhomogeneous magnetic fields offer a valuable, non-visual information source for positioning. Among systems leveraging this, magnetic field-based simultaneous localization and mapping (SLAM) systems are particularly attractive because they can provide positioning information and build a magnetic field map on the fly. Moreover, they have bounded error within mapped regions. However, state-of-the-art methods typically require low-drift odometry data provided by visual odometry or a wheel encoder, etc. This is because these systems need to minimize/reduce positioning errors while exploring, which happens when they are in unmapped regions. To address these limitations, this work proposes a loosely coupled and a tightly coupled inertial magnetic SLAM (IM-SLAM) system. The proposed systems use commonly available low-cost sensors: an inertial measurement unit (IMU), a magnetometer array, and a barometer. The use of non-visual data provides a significant advantage over visual-based systems, making it robust to low-visibility conditions. Both systems employ state-space representations, and magnetic field models on different scales. The difference lies in how they use a local and global magnetic field model. The loosely coupled system uses these models separately in two state-space models, while the tightly coupled system integrates them into one state-space model. Experiment results show that the tightly coupled IM-SLAM system achieves lower positioning errors than the loosely coupled system in most scenarios, with typical errors on the order of meters per 100 meters traveled. These results demonstrate the feasiblity of developing a full 3D IM-SLAM systems using low-cost sensors and the potential of applying these systems in emergency response scenarios such as mine/fire rescue.

Problem

• Develops inertial magnetic SLAM systems using low-cost sensors.
• Addresses need for odometry by fusing IMU, magnetometer, and barometer data.
• Enables robust positioning in low-visibility conditions like rescue operations.

Innovation

• Low-cost IMU, magnetometer, barometer sensors used
• Loosely and tightly coupled magnetic field models employed
• State-space representation integrates local and global magnetic data