Quick point cloud registration method based on curved surface fitting coefficient features

The invention discloses a quick point cloud registration method based on curved surface fitting coefficient features. Curvature mean differences of neighborhoods of different sizes are compared, points whose differences exceed a set threshold are selected to serve as key points, and key point candidate points are adaptively selected according to the differences. Multiple neighborhoods are selected at the key point for curved surface fitting, and a curved surface coefficient serves as a feature descriptor for the key point. Through comparing distances between key point feature descriptors, a key point pair with the smallest distance is selected to serve as an initial corresponding relation. A transformation matrix obtained through the initial corresponding relation is used for adjusting positions and orientations of the corresponding relation for basic coincidence, a distance threshold is set, and corresponding relations whose distances are larger than the threshold are removed. Then, a clustering method is used for enabling the corresponding relations to be uniformly distributed, a covariance matrix for the corresponding relations after optimization is calculated, and singular value decomposition is then carried out on the covariance matrix to obtain a final transformation matrix. The method of the invention has the advantages of quick registration, high precision and good anti-noise ability.