Design of a High-Precision Map System for Mining Area Scenarios Based on LIO-SAM

With the rapid development of intelligent mining technology, autonomous mining trucks, as a crucial component of mine automation, are increasingly relying on high-precision mapping technology in their design and application. This paper proposes a design scheme for a high-precision mining area mapping system based on the LIO-SAM (Lidar Odometry and Mapping with Smoothing and Mapping) framework, systematically elaborating the core role of high-precision maps in autonomous mining trucks. This includes providing a foundation for precise localization, enabling environmental perception model construction, and supporting path planning. Addressing the unique challenges posed by complex mine terrains, extreme cold, and high altitudes, this research innovatively applies the LIO-SAM framework to the construction of point cloud maps within mining areas. Through key technical processes such as LiDAR point cloud data acquisition, pose estimation, loop closure detection, and point cloud map updating, the system achieves accurate determination of drivable area boundaries in both structured roads and unstructured operational zones within the mine. Experimental results demonstrate that the high-precision mine map built on the LIO-SAM framework outperforms traditional methods in terms of localization accuracy, map completeness, and real-time update capability, thereby providing more reliable navigation and decision support for autonomous mining trucks.