融合磁力计、惯性与激光雷达,实现无基础设施的高鲁棒定位
MIL-LC: A Robust Magnetometer-Inertial-LiDAR Fusion Multimodal Localization Framework

- 用磁力计+惯性+激光雷达多模态融合,不依赖纹理和地标
- 在激光雷达失效或磁场变化时仍能保持定位精度
- 适合地下停车场等复杂环境的机器人部署
在无GNSS信号、几何结构重复或缺乏纹理特征的场景(如办公室、酒店、地下车库)中,可靠定位仍是自主移动机器人(AMR)部署的难题。单一传感器方法受限于自身缺陷,现有融合方案多依赖几何或纹理特征,或需额外基础设施,增加成本且降低灵活性。基于环境磁场(AMF)的定位因无需依赖几何特征或额外设施,成为有前景的补充模态。然而,现有研究仅聚焦于智能手机搭载的行人场景,面向AMR系统的实用解决方案仍不明确。为此,本文提出定制化传感器套件的磁力计-惯性-激光雷达融合框架MIL-LC,可在激光雷达遭遇几何退化或磁图长期变化时仍实现稳定定位。仿真与真实环境实验表明,该框架具备鲁棒且精确的定位性能。
原文摘要 · Abstract (English)
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.
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