A Review of Lidar-Based SLAM Techniques for Open-Pit Coal Mine Environments

Abstract

With the rapid advancement of mobile robotics and intelligent mining, Simultaneous Localization and Mapping (SLAM) has become a critical technology for autonomous navigation of unmanned mine trucks and inspection robots. Open-pit coal mine environments are characterized by complex terrain, dust, variable lighting, and numerous dynamic obstacles, which present significant challenges to SLAM accuracy and robustness. Among available sensors, LiDAR has been widely adopted for mapping and positioning due to its high-precision ranging and robustness to lighting variations. This paper systematically reviews LiDAR-based SLAM techniques in open-pit coal mines, focusing on their algorithmic categories, including feature-based, direct, and graph-optimization approaches. Comparative analyses of algorithm performance under typical mining conditions are presented, highlighting strengths, limitations, and adaptability to harsh environments. Finally, the paper discusses emerging trends, such as multi-sensor fusion, intelligent decision-making, and large-scale collaborative applications, providing new perspectives for the development of robust and scalable LiDAR SLAM systems in mining operations.