Coarse-fine point cloud registration based on local point-pair features and the iterative closest point algorithm

3D point cloud registration has a wide range of applications in object shape detection, robot navigation and 3D reconstruction. Aiming at the problems of the traditional ICP registration algorithm, such as slow convergence speed and high requirements for the initial point cloud position, this paper proposes a coarse-fine point cloud registration method based on a fast and robust local point-pair feature (LPPF) and the ICP algorithm. The LPPF feature descriptor is an improved descriptor for the local application of classic point-pair features and is a histogram descriptor formed by counting the feature information of the local point cloud neighborhood. This paper completes point cloud registration through keypoint extraction, LPPF feature description, feature matching, coarse registration and fine registration. To verify the effectiveness of this method, under the evaluation indices of L1, RMSE and MAE, we analyzed the experimental results from the three aspects of descriptors, coarse registration and coarse-fine registration. Under Gaussian noise conditions, LPPF compared to the second-ranked descriptor, the L1 scores of LPPF on the Stanford, Kinect and Princeton datasets increased by 12%, 12.4% and 9.1%, respectively. The coarse registration experiment is compared with 5 classic descriptors on 3 commonly used datasets. The LPPF feature descriptor has higher registration accuracy and less registration time. Finally, the coarse-fine registration experiment shows that our method can reduce the number of iterations of the ICP algorithm by 77% under optimal conditions.