Abstract: Simultaneous Localization and Mapping (SLAM) is a core technology for achieving autonomous operation of intelligent mining equipment and enabling digital perception of underground environments. However, underground coal mines are typically unstructured, with narrow spaces, uneven illumination, and complex geometries. These characteristics pose significant challenges for traditional LiDAR SLAM systems that rely primarily on geometric feature matching. To address these limitations, this paper proposes a LiDAR SLAM method that integrates point cloud intensity information to enhance environmental constraints in degenerate scenarios. First, in addition to conventional geometric feature extraction, point cloud intensity texture features are introduced as supplementary constraints, significantly improving pose estimation stability in environments with weak geometric structure. Second, an intensity-based scan context descriptor (Intensity Scan Context Descriptor, ISCD) is developed for loop closure detection. By leveraging intensity distribution features, the proposed method achieves more robust scene matching and enhances the global consistency of pose graph optimization. Finally, experiments conducted in an indoor long corridor and a real underground coal mine validate the effectiveness of the proposed method. The results demonstrate that the method achieves superior accuracy and robustness compared with mainstream LiDAR SLAM approaches, providing valuable technical support for real-time localization and intelligent perception in coal mine robotics.