Stacking unmanned forklifts are responsible for the stacking and picking of goods in warehousing and logistics management. In industrial environments, it is necessary to develop fast and accurate state estimation and environment perception algorithms in order to achieve autonomous navigation movement of unmanned forklift trucks. However, when using LiDAR odometer for state estimation, problems such as inaccurate mapping and pose drift are often encountered. Therefore, a method of simultaneous localization and mapping of pileup unmanned forklift based on improved DLO algorithm is proposed. Firstly, the motion model provided by the Inertial measurement Unit and the point cloud data of the multi-line LiDAR are combined to perform a prior estimation of the initial pose of the forklift. Then, through the front end of DLO SLAM, the generalized least square method is used to scan and match, and the pose of the forklift is estimated in real time and the map is constructed. Finally, the back-end pose optimization and loop detection of HDL-Graph-SLAM are used to further improve the accuracy of map reconstruction. Experimental results show that the proposed scheme can effectively suppress map drift and error accumulation in dynamic environment. Compared with DLO SLAM, the localization accuracy is improved by 60.9 % and compared with Cartographer algorithm by 56.9 %. At the same time, the stability is also significantly improved to meet the requirements of stacking unmanned forklifts for accurate and efficient simultaneous localization and mapping.