To address the issues of height inaccuracy and ghost map caused by the vertical drift overlooked in existing LiDAR SLAM methods,a tightly coupled LiDAR-inertial SLAM method based on vertical constraints is proposed. Proposed method extracts precise ground points by combining the installation height of the LiDAR sensor and the distance from points to the LiDAR.Based on the extracted ground points,a LiDAR odometry considering vertical residuals is designed.Proposed method uses a two-step Levenberg-Marquardt(L-M)method to solve for pose transformation.These residuals contribute to converging to the optimal solution in the vertical direction.A native but effective Euclidean distance-based loop closure detection method is used to avoid ghost map.To verify the superiority of the proposed algorithm,relevant experiments were conducted on the KITTI dataset and in real-world environments.On the KITTI dataset,the root mean square error(RMSE)of the trajectories obtained by the proposed algorithm were reduced by 47.62%,33.14%,and 73.79%compared to LeGO-LOAM,LIO-SAM,and Point-LIO,respectively.In real-world campus environments,the RMSE of the trajectories obtained by the proposed algorithm were reduced by 83.56%,13.55%,and 82.04%compared to LeGO-LOAM,LIO-SAM,and Point-LIO,respectively.These results demonstrate the higher localization accuracy of the proposed method.