Most of the existing laser inertial navigation odometers adopt the filtering loose coupling fusion method, and there will be a certain motion estimation drift in large scene mapping, which will lead to the reduction of positioning and mapping accuracy. Aiming at this problem, a close-coupled odometer and mapping method of laser inertial navigation system based on graph optimization is proposed. At the front end, point cloud distortion compensation, point cloud clustering segmentation, ground and feature extraction are carried out in turn. At the back end, the map optimization method is used to integrate IMU pre-integration, laser odometer and loop detection information to complete the map construction. Finally, Kitti data set and self-collected data are used to compare and analyze LOAM, LeGO-LOAM and the method of this paper in odometer accuracy and loop detection effect. Experimental results show that compared with LOAM and LeGO-LOAM, the positioning and mapping accuracy of this method is improved by 45% and 35% respectively, and it has better robustness.