Abstract: Aiming at the problems of easy loss of GNSS positioning signals and poor robustness of traditional SLAM algorithms in forest and orchard environments, the problems of easy loss of GNSS positioning signals and poor robustness of traditional SLAM algorithms in forest and orchard environments was addressed. The proposed method was based on the factor graph for multi-source constrained IMU odometry construction, real-time output of high-frequency position information. The IMU odometry factors and pre-integration factors were used to optimize LiDAR odometry and provide a priori constraints on IMU bias. The LiDAR odometry was optimized by the odometry factor and pre-integration factor, which provided a priori constraints on the IMU bias of the position. The local point cloud map was introduced to participate in feature point cloud coarse matching and non-feature point cloud progressive matching to further densify the source point cloud and improve the performance of the LiDAR odometer. The map construction by fusing GPS signals with LiDAR/IMU tightly coupled framework can obtain accurate and high-frequency continuous position information and improve the reuse rate of point cloud maps. The experimental results showed that the positioning accuracy was maintained at around 0.05 m and the root mean square error was 0.016 2 m compared with algorithms such as LIO-SAM. The algorithm presented enabled the robot to achieve higher accuracy, real-time performance and robustness, effectively reducing the cumulative error of the system and ensuring the global consistency of the constructed maps.