Aiming at the problem of large trajectory error and map drift of most slam algorithms in outdoor long-distance environment, a SLAM algorithm based on IEKF tightly coupled lidar and IMU is proposed, and a globally consistent laser 3D point cloud map is constructed. Firstly, the IMU state model is constructed and the state is estimated by forward propagation, and the back propagation is used to compensate the motion of the point cloud, and then the IMU data and radar data are fused by iterative extended Kalman filter to obtain the front-end laser odometer; the loop back detection module is introduced to construct the triangle descriptor in the point cloud and match the edges of the triangle descriptor to achieve closedloop detection; finally, in the back-end optimization part, GTSAM is used to build a factor map, which integrates IMU pre integration factor, odometer factor and loop detection factor to eliminate cumulative errors, improve positioning accuracy and reduce map drift. Experiments show that compared with FAST-LIO2 algorithm, the APE RMSE of the proposed algorithm in KITTI data set and self collected data set is reduced by 50.06% and 33.65%, respectively, and the drift on the z axis is reduced, which can build a closed dense point cloud map.