In order to solve the problem of poor robustness and low registration accuracy of the iterative closest point algorithm under noise interference and data loss, a point cloud registration method based on neighborhood characteristic point extraction and matching is proposed. Firstly, a neighborhood characteristic parameter is defined, which is composed of three parts: the k-neighborhood curvature of the point, the normal vector inner product' mean value of the point and the neighborhood points, and the distance variance between the neighborhood points and the neighborhood fitted plane. Neighborhood characteristic parameters and curvature characteristic parameters constructed on moving least square surface are used to extract feature points twice. Secondly, three matching conditions are defined according to the histogram features, and the correct matching point pairs are obtained by double constraints. Finally, in the registration stage, the iterative closest point algorithm of bi-directional k-dimension tree is used to achieve accurate registration. The experimental results show that the registration accuracy of the proposed algorithm is more than 90% higher than that of the iterative closest point algorithm, and it can effectively complete the registration of missing point clouds in noisy environment, which has obvious advantages in robustness and precise registration.