Multibeam LiDAR can directly capture the distance to target points and perceive three-dimensional information. However, its low vertical resolution results in sparse single-frame point clouds. Hence, an external rotating axis was designed to densify the point cloud obtained by the multibeam LiDAR. This paper proposes a method using a multilevel perforated circular calibration board to establish the relationship between the LiDAR and external rotating axis. First, the external rotating axis scans the calibration board at different angles to obtain multiple single-frame point cloud data and solve the initial external parameters. Second, an optimization model is established using two sets of calibration board point-cloud data scanned by the external rotating axis within 0°?180° and 180°?360° intervals. Ultimately, the initial values are input into the optimization model to refine the initial pose such that precise pose parameters can be obtained. Experimental results show that the proposed method offers high accuracy and dense distributions in point-cloud reconstruction. When measuring a standard sphere, its absolute error is 0.9 cm, which satisfies the requirement for practical applications.