Aiming at the problem of low positioning accuracy of laser SLAM algorithm in indoor scenes with feature scarcity and narrow corners, a laser inertial SLAM method based on planar extension and constraint optimization is proposed. The IMU is fused in laser SLAM, and the laser point cloud is position compensated and key frames are judged according to the IMU state estimation results. The global planar map is constructed, the planar extraction of key frames is performed based on the RANSAC algorithm and combined with the pre-extraction method to track the planar features in order to reduce the time cost, and the fitting results are optimized by iPCA to remove the effect of noise on the RANSAC. Using the distance from the point to the surface to construct the plane constraint optimization equation, and integrate it with the edge point constraints and pre-integration constraints in a unified way to establish a nonlinear optimization model, and solve to get the optimized plane information and key frame bit position. Finally, to verify the effectiveness of the algorithm, experiments are carried out on the M2DGR public dataset and private dataset respectively, and the experimental results show that the present algorithm performs well on most of the public datasets, especially in the private dataset compared with the widely used fast-lio algorithm, the localization accuracy is improved by 61.9%, which demonstrates good robustness and real-time performance.