SL(C)AMma: Simultaneous Localisation, (Calibration) and Mapping With a Magnetometer Array

2026-04-21

AI Summary

This work addresses the challenges of indoor localization, where GNSS signals are unavailable and ego-motion sensors suffer from unbounded drift due to integration errors, while single-magnetometer SLAM exhibits limitations in unknown environment exploration and loop closure. The paper introduces, for the first time, a magnetometer array into a SLAM framework, proposing a magnetic-field-based simultaneous localization and mapping method that jointly estimates magnetometer calibration parameters online. This approach significantly enhances the consistency of magnetic field measurements and the robustness of trajectory estimation. Experimental results across ten datasets demonstrate that, compared to pure inertial integration, the proposed method reduces trajectory drift by over 80%, effectively resolving localization failures encountered with single-magnetometer configurations.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Indoor localisation techniques suffer from attenuated Global Navigation Satellite System (GNSS) signals and from the accumulation of unbounded drift by integration of proprioceptive sensors. Magnetic field-based Simultaneous Localisation and Mapping (SLAM) reduces drift through loop closures by revisiting previously seen locations, but extended exploration of unseen areas remains challenging. Recently, magnetometer arrays have demonstrated significant benefits over single magnetometers, as they can directly estimate the odometry. However, inconsistencies between magnetometer measurements negatively affect odometry estimates and complicate loop closure detection. We propose two filtering algorithms: The first focuses on magnetic field-based SLAM using a magnetometer array (SLAMma). The second extends this to jointly estimate the magnetometer calibration parameters (SLCAMma). We demonstrate, using Monte Carlo simulations, that the calibration parameters can be accurately estimated when there is sufficient orientation excitation, and that magnetometers achieve inter-sensor measurement consistency regardless of the type of motion. Experimental validation on ten datasets confirms these results, and we demonstrate that in cases where single magnetometer SLAM fails, SLAMma and SLCAMma provide good trajectory estimates with, more than 80% drift reduction compared to integration of proprioceptive sensors.

Problem

• indoor localisation
• magnetic field-based SLAM
• magnetometer array
• sensor inconsistency
• drift reduction

Innovation

• magnetometer array
• SLAM
• sensor calibration
• odometry estimation
• loop closure