MIL-LC: 頑健な磁気・慣性・LiDAR融合マルチモーダル位置推定フレームワーク
MIL-LC: A Robust Magnetometer-Inertial-LiDAR Fusion Multimodal Localization Framework
GNSS拒否環境や幾何学的反復・テクスチャレス環境でも頑健な位置推定を実現するため、磁気センサ・慣性センサ・LiDARを融合したマルチモーダル位置推定フレームワークMIL-LCを提案した。
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著者: Qiyang Lyu, Zhenyu Wu, Wei Wang, Hongming Shen, Danwei Wang
分類: cs.RO
原文アブストラクト
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.
