In order to solve the problem of continuous and accurate localization in complex environments such as slope, feature degradation and GNSS signal loss, a multi-sensor fusion scheme based on ground constraints is proposed in this paper to improve the overall performance of SLAM algorithm. Firstly, the key frame selection strategy under different system states is proposed. By increasing the number of key frames in the starting position, the positioning jump caused by factor map optimization is avoided, and continuous and accurate pose output is obtained. At the same time, in order to prevent the loopback detection failure caused by error accumulation, this keyframe strategy is used to effectively increase the subkeyframe set of the current frame, and improve the robustness of the loopback detection algorithm. Secondly, to solve the problem that IMU drifts too much in the height direction after long-term operation, this paper constructs the ground constraint according to the extracted ground points and introduces it into the factor graph for optimization. Finally, the mobile robot experiment platform is used to complete the data collection of different scenes on campus, and the effectiveness of the proposed algorithm is verified. The comparison test between KITTI data set and LIO-SAM algorithm is carried out, and the error analysis shows that the proposed algorithm has better positioning accuracy.