Point cloud registration algorithm based on neighborhood features of multi-scale normal vectors

To solve the registration of 3D laser scanning point cloud data, a new method of registration algorithm based on neighborhood features of multi-scale vectors is proposed. Firstly, because there is error between normal vectors of a point calculated by different neighborhood radii, setting constraint condition can be used to select the key points. Thus, the point cloud data is streamlined. Secondly, a method for extracting point feature information is designed based on neighborhood eigenvectors and feature descriptor of all key points can be gotten by using this method. Then, by using the minimum and second distance ratio thresholds to obtain rough corresponding relation and twice optimization methods (random sample consensus algorithm and clustering sorting method), the exact correspondence between source point and target point cloud can be gotten. Finally, covariance matrix is built and decomposed to get the rigid body transformation matrix. The experimental results show that the selection of key points, extraction of point feature information and determination of correspondence of the new method have simple theory, stable performance, high calculation speed and low computational complexity, and it has practical significance to the realization of point cloud registration. ©, 2015, Board of Optronics Lasers. All right reserved.