To address the cumulative drift issue commonly encountered during long-term autonomous navigation of unmanned forklifts in warehouses, this paper presents a localization system based on the LeGO-LOAM framework, which incorporates ground-mounted Aruco markers. The system utilizes LiDAR to provide continuous odometry constraints, while the Aruco markers offer global pose anchors upon detection. These two sources of information are fused through back-end factor graph optimization. For practical engineering deployment, this paper details the marker arrangement strategy, sensor extrinsic calibration process and the method for setting graph optimization weights. Experiments conducted in a real indoor corridor environment demonstrate that the root mean square error (RMSE) of localization is reduced from 0.635 1 m to 0.272 8 m (a decrease of 57.0%). The system maintains stable performance even under partial marker occlusion. The results indicate that this solution offers significant engineering advantages, including low implementation cost, ease of deployment and strong maintainability, making it suitable for autonomous navigation tasks in warehouse logistics scenarios.