This paper presents a new algorithm for real-time positioning and orientation of unmanned surface vessels (USVs) based on a single LiDAR. To address the perception challenges in environments where GNSS works not, this algorithm achieves online and efficient identification of point cloud clusters through dynamic area division, and prioritizes the selection of the point cloud clusters that are most valuable for continuous tracking of USV navigation. By adaptively determining the calculation area based on the real-time position information of the USV, the high-precision reference positions are obtained through point cloud cluster identification, and the position and heading angle of the USV are synchronously solved using geometric constraint. Both the performance and real-time capabilities of proposed algorithm are demonstrated through experiment results, providing an effective solution for the state measurement of USVs in GNSS-constrained environments.