Abstract: Point cloud registration is the key technology of 3D reconstruction.Aiming at the problems of slow convergence speed,low registration efficiency and long registration time in iterative closest point(ICP) algorithm,a fast point cloud registration method based on feature transformation combined with kdtree is proposed to improve ICP.First of all,the three-dimensional SIFT key points are obtained on the differential Gaussian model by down-sampling with voxel mesh method.Secondly,fast point feature histogram(FPFH) is established.Then sample consensus initial alignment(SAC-IA) algorithm is used to realize rough registration.Finally,according to the obtained initial transformation matrix and improved ICP algorithm based on KD tree,accurate registration is realized.Experimental results of Stanford data registration show that compared with ICP algorithm,the proposed algorithm has higher registration accuracy and time utilization,and can select a better initial pose for accurate registration.To some extent,this study avoids the local optimal phenomenon existing in point cloud collocation,and provides an efficient me-thod for subsequent target recognition and matching and 3D reconstruction.

Key words: Feature transform, Consistency of sampling, Fast point feature histograms, Iterative closest point