This study proposes a 3D global localization method that implements mobile LiDAR mapping and point cloud registration to recognize the locations of objects in an underground mine. An initial global point cloud map was built for an entire underground mine area using mobile LiDAR; a local LiDAR scan (local point cloud) was generated at the point where underground positioning was required. We calculated fast point feature histogram (FPFH) descriptors for the global and local point clouds to extract point features. The match areas between the global and the local point clouds were searched and aligned using random sample consensus (RANSAC) and iterative closest point (ICP) registration. The object’s location on the global coordinate system was measured using the LiDAR sensor trajectory. Field experiments were performed at the Gwan-in underground mine using three mobile LiDAR systems. The local point cloud dataset formed for the six areas of the underground mine precisely matched the global point cloud, with a low average error of approximately 0.13 m, regardless of the type of mobile LiDAR system used. In addition, the LiDAR senor trajectory was aligned on the global coordinate system to confirm the change in the dynamic object’s position over time.

서 론 인류의 미래를 위해서는 지구 온난화를 늦추고 기후 위 기에 대응하기 위한 탄소중립을 실천함으로써 지속 가능하 게 지구를 이용하는 것이 그 어느 때보다 중요하다. 이에 발 맞춰 각국 정부는 탄소중립을 실현하기 위한 실천 방안을 제시하고 있으며 전기차와 배터리 등으로 대표되는 전기회 로의 친환경 에너지 전환은 피할 수 없는 현실이 되고 있다. 이 과정에서 리튬, 니켈, 코발트,