The accurate and rapid calculation of aircraft pose using three-dimensional point clouds scanned by LiDAR is the key to achieving automatic parking guidance for aircraft. Therefore, a fast target pose estimation algorithm based on precise registration of skeleton point clouds is proposed. In the point cloud on the aircraft surface, the main body structures such as wings, engines, and nose are selected from the perspective of LiDAR to construct a simplified aircraft skeleton point cloud, avoiding erroneous registration of other complex structures and effectively reducing computational complexity. When parking the aircraft, establish a point cloud bounding box based on the aircraft axis to obtain the initial pose of the aircraft and use it as a constraint for registration. Then, a random sampling consistent coarse registration algorithm based on fast point feature description is used to correct the aircraft pose, and a point surface fine registration algorithm based on bidirectional KD-Tree is designed to improve the accuracy of aircraft pose estimation. Finally, the performance of the algorithm was validated through simulation experiments on aircraft pose estimation throughout the entire parking process. Compared with typical algorithms such as Super-4PCS, MSKM-NDT, and AA-ICP, this paper’s algorithm reduces registration error by 32.5% and improves processing speed by 34%. The maximum angle error for pose estimation is 2.0 degrees, the maximum distance error is 0.125 meters, and the single frame processing speed is 0.37 seconds. The actual aircraft pose estimation experiment also verified the effectiveness of the algorithm.