A laser inertial SLAM system based on the fusion of lidar and inertial unit (IMU) is proposed to address the issue of inaccurate pose estimation using a single sensor laser odometer when unmanned vehicles are mapping in outdoor large scene environments, and the accuracy may decrease with accumulated drift. The front-end of the system is assisted by IMU information to remove distortion from the point cloud, and through point cloud registration, it forms a LiDAR odometer. The back-end optimization is realized by factor graph, which is jointly optimized by the front-end odometer factor, IMU pre Integrating factor and loopback detection factor. At the same time, this article proposes an improved fast loop detection method based on the global descriptor (Scan Context), which can effectively improve the accuracy and accuracy of loop detection while ensuring real-time performance. The results of publicly available datasets and unmanned vehicle experiments show that compared to the classic laser algorithms A-LOAM and LeGO-LOAM, the trajectory accuracy of the proposed method in this paper has been improved by about 40%, and the efficiency of loop detection has been improved by about 25%, effectively improving the performance of the SLAM system.