MIL-LC: A Robust Magnetometer-Inertial-LiDAR Fusion Multimodal Localization Framework
It addresses the problem of reliable localization for autonomous mobile robots in challenging indoor environments where single-modality methods fail, offering a practical solution without additional infrastructure.
The paper proposes MIL-LC, a multimodal localization framework fusing magnetometer, inertial, and LiDAR data for autonomous mobile robots in GNSS-denied, repetitive, or textureless environments. It achieves robust and accurate localization even under LiDAR degeneration or magnetic map changes, validated through simulation and real-world experiments.
Localization in challenging environments, such as GNSS-denied, geometrically repetitive, or textureless scenes commonly found in offices, hotels, and underground parking facilities, remains an open problem for reliable autonomous mobile robot (AMR) deployment. Single-modality localization methods are inherently limited by the constraints of individual sensors. Although multimodal fusion frameworks have shown improved robustness, most existing approaches still rely heavily on geometric or texture features, or on infrastructure-based beacons, which increase installation and maintenance costs while reducing deployment flexibility. Recently, ambient magnetic field (AMF)-based localization has attracted growing attention because it does not depend on geometric or texture features, nor does it require additional infrastructure, making it a promising complementary modality for AMR localization. However, existing studies have only explored such fusion in pedestrian scenarios using smartphone-mounted sensor suites, and practical solutions for AMR systems remain largely unexplored. To address this gap, this article proposes a magnetometer-inertial-LiDAR fused multimodal localization framework with a custom-designed sensor suite, termed MIL-LC, which provides reliable localization even when LiDAR suffers from geometric degeneration or when the magnetic map changes during long-term deployment. Extensive experiments in both simulation and real-world environments demonstrate that the proposed MIL-LC framework achieves robust and accurate localization performance.