Aiming at the problems of low mapping accuracy and map ghosting and drift in outdoor large scene environment, a simultaneous localization and mapping system integrating filtering and graph optimization theory is proposed. The system consists of three parts: point cloud data preprocessing, filtering based tight coupling inertial odometer and back-end pose map optimization. Firstly, the point cloud data preprocessing uses the random sampling consistency algorithm to segment the ground, extracts the ground model parameters, and constructs the ground constraint factors in the back-end optimization. Then, the front-end tightly coupled inertial odometer adopts iterative error state kalman filter, takes the laser odometer as the observed value and the result of IMU pre-integration as the predicted value, and constructs a joint function to filter and fuse to obtain a more accurate laser inertial odometer. Finally, combined with the graph optimization theory, the closed-loop factor, ground constraint factor and odometer factor matched between frame and graph are introduced as constraints to construct the factor graph and optimize the map pose. The closed-loop factor adopts the improved closed-loop detection algorithm of scanned text for position recognition, which can reduce the environmental false recognition rate. The algorithm proposed in this paper completes scene mapping in multiple scenes such as outdoor plant buildings, parking lots and indoor workshops. The cumulative deviation in the three directions of distance, level and elevation is controlled by about 10 cm, which can effectively solve the problem of map ghosting and drift, and has high robustness and high precision.