Simultaneous localization and mapping (SLAM) algorithm in the process of mapping using LiDAR, due to the changes in the environment and the fixed alignment parameters, it will produce a large cumulative error in the process of point cloud alignment, and the poor suppression of elevation error, which in turn will affect the global alignment accuracy and mapping positioning effect. Aiming at the above problems, a laser inertial SLAM algorithm based on iterative error state Kalman filter (IESKF) and factor map optimization is proposed, with an adaptive parameter adjustment module, a point cloud preprocessing module, a front-end odometry module, and a back-end factor map optimization module. According to the size of point cloud, different key frame distance parameters, alignment parameters, voxel downsampling parameters and ground constraints are decided; K-nearest neighbor (KNN) is used to select key frames to compose local maps, which makes full use of the spatial information in frame-map matching; the point cloud residuals are fused with IMU through the IESKF, which is an adaptive algorithm. The IESKF fuses the point cloud residuals with the a priori position of IMU to obtain the front-end odometry of the filter fusion method; the ground constraints are added in the back-end optimization and combined with the loopback constraints to form the factor map optimization, which improves the global consistency of the map construction. Multi-algorithm comparison experiments are carried out on the M2DGR public dataset and real scenarios, and the experimental results show that, in real scenarios, the proposed algorithm improves the global mapping accuracy by 38%, and reduces the elevation error by 52% compared with the LIO-SAM algorithm that only uses factor graph optimization, and improves the global mapping accuracy by 64%, and reduces the elevation error by 62% compared with the FAST-LIO2 algorithm that only uses IESKF. The results demonstrate that the proposed algorithm has better performance in terms of environmental adaptability and elevation error suppression.