Abstract: The operation areas such as coal mine roadway and mining working face have some typical semi⁃structured or unstructured environment characteristics， and GPS cannot be directly applied in underground coal mines. Therefore， there is an urgent need to build an autonomous positioning system for coal mine mobile robot to solve the problems of its precise positioning and state estimation in underground coal mine. To solve these problems， a LiDAR⁃ Inertial SLAM algorithm is proposed to achieve a real⁃time output of robust six degrees of freedom (6DOF) state esti⁃ mation and globally consistent simultaneous localization and mapping (SLAM) for robot in underground coal mines. It consists of two parts: front end iterative Kalman filtering and back end pose graph optimization. Firstly， on the frontend， an iterative Kalman filter is established to construct a tightly coupled based LiDAR⁃Inertial Odometry (LIO). The state propagation process for the a priori position and attitude of a robot， which uses predictions and observations， increases the accuracy of the attitude and enhances the system robustness. Secondly， on the back end， the key frame selection strategy is deployed to meet the real⁃time requirements for large⁃scale scenes. Moreover， loop detection and ground constraints are added to the optimization framework， thereby further improving the overall accuracy of the 6DOF state estimation. Finally， the performance of the algorithm is verified using a public dataset and the dataset col⁃ lected. The experimental results show that for the special environment of underground coal mine， compared with the existing LiDAR⁃SLAM algorithm， the proposed algorithm makes the robot have higher accuracy， real⁃time performance and robustness， effectively reducing the cumulative error of the system and ensuring the global consistency of the con⁃ structed maps.