Abstract: SLAM (Simultaneous Localization and Mapping) of the underground robot is a hot research topic at present. But the research on improving the precision and robustness of laser SLAM in underground complicated conditions is still insufficient. The traditional laser SLAM method has the problems of rapidly increasing cumulative error, poor robustness of the rotation process and high error rate of feature correlation under complex underground environment. The existing laser inertial fusion location mapping tightly-coupled fusion mechanism still needs to further improve the adaptability to the complex environment in coal mines. In order to solve the above problems, a LiDAR (lidar)/IMU (inertial measurement unit) tightly-coupled SLAM (LI-SLAM) method for coal mine robot is proposed. Firstly, the IMU observation information is used to predict the point cloud motion state and make effective compensation to reduce the point cloud distortion caused by severe vibration, rapid rotation and other severe motion conditions. Secondly, the edge and plane features of the radar point cloud are extracted. The laser relative pose constraints are constructed based on point line and point surface scanning matching. In vector space and manifold space, the construction process of residual, Jacobian matrix and covariance matrix of constraints is derived analytically. Finally, the LiDAR/IMU tight coupling is completed based on the factor graph optimization method by constructing the radar relative pose constraint factor, IMU pre-integration constraint factor and loopback detection constraint factor. The localization and map construction of the mine mobile robot in the complex underground environment is realized. In order to verify the precision and robustness of the LI-SLAM method in the bumpy road and complex scenario, experiments are carried out in the field and underground garage environment based on the platform of wheeled mobile robot in the coal mine. The industrial experiments are carried out in Tashan Coal Mine of Jinneng Group. The results are compared with the current optimal LiDAR odometry and mapping (LOAM) method, lidar-inertial state estimator (LINS) method and lidar inertial odometry and mapping (LIO-mapping) method. The test results in field bumpy road show the following points. The map consistency of the LI-SLAM method and the LOAM method is the best, which is basically consistent with the real route. The LI-SLAM method has better adaptability to rotation, and the distance error is the minimum. The LIO-mapping method cannot run in real time. The method can obtain complete trajectory at 0.5 times. However, in the initial motion phase, there is a large degree of direction deviation, and the initialization process is easy to fail. Because LINS only uses the latest observation information, it drifts under complex terrain. The test results in underground garage environment show the following points. Compared with the LOAM method, LINS method and LIO-mapping method, the LI-SLAM method has higher modeling precision. The local refinement is higher, and the motion trajectory is smoother. The industrial test results in underground coal mines show the following points. The LI-SLAM method can operate stably and online in various terrain environments. The result meets the requirements of robustness and real-time. When the straight-line distance of the roadway where the coal mine mobile robot drives on is 273 m, 30 groups of distance results are analyzed, and the average error is less than 15 cm. It has high positioning and modeling precision. It basically meets the positioning and modeling precision requirements of coal mine mobile robots. It has better applicability for precise positioning and mapping of mobile robot in the complex environment of the coal mine.