Abstract

With the development of industrial automation and intelligent logistics, the application of unmanned forklifts in warehousing and production environments has become increasingly widespread. However, the decline in positioning accuracy and the problem of drift seriously affect their stability and safety. Although existing LiDAR and inertial navigation systems have improved, they still face challenges related to cumulative errors during operation. This paper proposes a new algorithm called Aruco-LOAM, which significantly improves the positioning accuracy of unmanned forklifts by combining Aruco markers with LEGO-LOAM's LiDAR point cloud data and utilizing visual constraints during the graph optimization process. The research results indicate that this method suppresses cumulative errors and enhances the robustness of unmanned forklifts in complex environments, providing a more reliable navigation solution for warehousing systems.