Fast Point Cloud Splicing Algorithm Based on Weighted Neighborhood Information of Points

Aiming at the problem that the traditional four-points congruent sets (4PCS) splicing algorithm is not efficient when the data volume is large, this paper proposed a point cloud fast splicing algorithm based on weighted neighborhood information of points. Firstly, a weighted principal component analysis algorithm is designed to calculate the normal vector of the point more accurately. Secondly, the distance from the point to the center of gravity of its neighborhood is used to extract the feature points. The corresponding point pairs are obtained by using neighborhood feature description based on weighted curvature estimation. The double constraint algorithm is adopted to filter the false correspondences. Finally, the extracted correspond- ing point pairs are used as the initial data of 4PCS splicing algorithm. The experimental results show that the weighted estimation of normal and curvature, feature points extraction and filtering methods of correspon- dences are stable and reliable. The splicing accuracy and efficiency of proposed point cloud splicing method are improved compared with traditional 4PCS splicing algorithm.