When the iterative closest point (ICP) is used for model registration of the aircraft skin surface, problems such as dislocation and local minimum value will appear since the surface is flat and smooth and has few features. For this reason, a registration method of skin point clouds based on contour constraints was proposed. Firstly, a new description method of Cκ feature points for a three-dimensional contour was defined, and the initial Cκ feature points were clustered and filtered based on the distance constraint, realizing the accurate feature description of skin point clouds. Secondly, on the basis of the similarity constraint of fast point feature histogram based on distance (FPFH-d) features, the corresponding point pairs of point clouds and model feature points were found to achieve the initial registration of the skin contour. Finally, according to the ICP algorithm, the contour constraints of Cκ feature description were fused for the precise registration of the skin. Furthermore, the speed and accuracy of the new algorithm were tested by the point clouds from the Stanford public database. In comparison with the fast point feature histograms-sample consensus initial alignment (FPFH-SAC-IA), the initial registration accuracy of the proposed algorithm is improved by 48.47% and 77.29%, respectively, and the speed is increased by 70.35% and 97.08%, respectively, which proves the universality and effectiveness of the extraction algorithm of Cκ feature points. In addition, based on the proposed algorithm, we conduct experiments to verify the measurement data of aircraft skin and the results demonstrate that the registration accuracy reaches 100% and the global error is better than 3.5 mm in the range of 12 m ³. In conclusion, the method proposed in this paper can effectively solve the problems of dislocation and local minimum value during skin registration.