Enhanced LIO-Based Localization System with Online Map Update for Robust Mining Tunnel Operations

Coal mines, crucial for energy in the face of rapid economic growth, grapple with challenges like unsafe manual mining in narrow tunnels, which significantly impact efficiency and safety. In response, recent research has intensified the development of unmanned mining technologies, with robust localization emerging as a foundational requirement for autonomous navigation. However, localization in underground mines remains difficult due to sparse and degraded geometric features, compounded by dynamic environmental changes such as route adjustments and structural modifications during mining operations. To address these challenges, we propose an integrated localization system that emphasizes robust state estimation, environment reconstruction, and long-term adaptability. The system combines multi-LiDAR sensing, with complementary field-of-view and resolution characteristics, and incorporates contextual infrastructure features commonly encountered in underground environments to enhance localization stability. Furthermore, we introduce a map inconsistency detection and correction module, which enables the system to adapt to long-term environmental changes and maintain map relevance over time. The efficacy of our proposed system is rigorously evaluated across various mine tunnel environments over an extended duration, affirming its reliability and performance.