An efficient registration algorithm based on spin image for LiDAR 3D point cloud models ?
Spin image is a good point feature descriptor of the 3D surface and has been used in model registration for many applications from medical image processing to cooperation of multiple robots. However, researches show that current Spin-Image based Registration (SIR) algorithms present disadvantages in computational efficiency and robustness. Thus in this paper, aiming at 3D model acquired from LiDAR sensor, a new SIR algorithm is proposed to solve these problems. The new algorithm is on the basis of a new-constructed three-dimensional feature space, which, composed of the curvature, the Tsallis entropy of spin image, and the reflection intensity of laser sensor, is combined with the concept of KD-tree to firstly realize the primary key point matching, i.e., to find the Corresponding Point Candidate Set (CPCS). After that, spin-image based corresponding point searching is conducted with respect to each CPCS to precisely obtain the final corresponding points. The most absorbing advantages of the proposed method are as the following two aspects: on one hand, due to the introduction of the extra features, the fault corresponding relation introduced by spin image based method can be effectively reduced and thus the registration precision and robustness can be improved greatly; on the other hand, the CPCS obtained using low-dimensional feature space and KD-tree reduces extraordinarily the computational burden due to spin-image based correspondence searching. This greatly improves the computational efficiency of the proposed algorithm. Finally, in order to verify the feasibility and validity of the proposed algorithm, experiments are conducted and the results are analyzed.
spin image 3d model registration kd tree