With the development of economic globalization, the speed of development of container terminals is also very rapid. Under the pressure brought by the surge in throughput, the unmanned and intelligent terminals will become the future development direction of terminals. As the cornerstone of the unmanned terminal, the positioning technology provides the most basic position information for system scheduling, path planning, real-time correction, and loading and unloading. Therefore, this paper is aimed to design a low-cost, high-precision, and easy-to-maintain unmanned dock positioning system in order to better solve the problem of unmanned dock positioning. The main research content of this paper is to design a positioning algorithm for unmanned terminal Automated Guided Vehicle (AGV) based on single-line lidar, including point cloud data acquisition, background filtering, point cloud clustering, vehicle position extraction, and result optimization. Among them, the clustering algorithm is based on the radially bounded nearest neighbor (RBNN) strategy, and the target vehicle point cloud is completely extracted from the collected point cloud. After obtaining the position of the vehicle, a Kalman filter is introduced to improve the accuracy of the result. In this paper, the related experiments are also designed. The real-time position of the vehicle is obtained by vehicle-mounted integrated navigation as the true value. With the true value, the error of the algorithm positioning detection result is measured, and the direction of further optimization and improvement is proposed.