Abstract: With the rapid development of artificial intelligence and unmanned and other related disciplines, the intelligence and unmanned of coal mining equipment has become a new trend. The application of intelligent equipment will greatly improve the productivity of coal mine operations as well as personnel safety. In this environment, the existing LIDAR-based Simultaneous localization and mapping (SLAM) solution is prone to positioning drift and large mapping errors. To address these problems, a tightly coupled SLAM algorithm based on LiDAR (Light Detection and Ranging) and IMU (Inertial Measurement Unit) is proposed, which uses both LiDAR and IMU sensors as data inputs.The front-end uses an iterative extended Kalman filter to fuse the pre-processed LiDAR feature points with the IMU data and uses backward propagation to correct the radar motion distortion, the back-end uses the LiDAR relative positional factor to use the LiDAR inter-frame alignment results as a constraint factor together with the loopback factor to complete the global factor map optimization. The robustness and accuracy of the algorithm are verified using open source dataset and open pit coal mine field dataset. The experimental results show that the accuracy of the proposed algorithm is consistent with the current LiDAR SLAM algorithm in the urban structured environment, while the proposed algorithm improves the localization accuracy by 46.00% and 23.15% with higher robustness than the FAST-LIO2 and LIO-SAM tightly coupled algorithms for the open pit coal mine field environment of more than2000meters long, respectively.