3D Point Cloud Registration With Low Overlap Rate And Development Of Point Cloud Data Integrated Processing System

With the rapid development of 3D point cloud,point cloud registration technology is widely used in 3D reconstruction,3D map construction,automatic driving of vehicles,UAV inspection and other fields.3D point clouds can be collected by 3D lidar.Due to the limitation of the scanning Angle of lidar,the acquisition of complete 3D point clouds of the measured object or scene needs to scan multiple partially overlapping 3D point clouds from multiple angles by lidar,and then register the partially overlapping 3D point clouds.In the case of small overlapping area,there are still many problems in point cloud registration technology,such as difficult to extract features and low registration accuracy.How to achieve fast and accurate point cloud registration with low overlap rate has important research value.The main research contents of this paper include:(1)Aiming at the problems of small overlap area of point cloud to be registered,difficulty in feature extraction and low registration accuracy,an improved RANSAC-ICP point cloud registration algorithm is proposed in this paper.Firstly,the random sample consensus(RANSAC)algorithm is improved by using multiple geometric constraints to realize the initial registration of point cloud under low overlap rate;In the improved RANSAC algorithm,the sampling points with large difference in normal vectors are eliminated by the included Angle normal threshold of normal,and then the sampling points are described in local space through the fast point feature histogram.Finally,according to the invariable euclidean distance of point cloud in space,a different value is set to delete the outliers and the corresponding relationship of mismatching.Secondly,the improved RANSAC algorithm is used for point cloud coarse registration,which provides a good initial pose for iterative closest point(ICP)algorithm registration;The point cloud fine registration of ICP algorithm optimizes the initial registration results and improves the overall registration accuracy.The experimental results show that the improved RANSAC-ICP algorithm realizes the "local-global-global" registration of point cloud,effectively improves the accuracy of point cloud registration,and is still robust in low overlap point cloud registration.(2)Using 3D lidar equipment to collect the point cloud data in the real scene,the point cloud registration experiment is carried out on the point cloud data in the real scene and point cloud model,and compared with the mainstream point cloud registration algorithm.The experimental results show that the proposed algorithm can quickly and accurately obtain the optimal transformation of point cloud registration,whether in the point cloud data containing noise in the real scene or in the standard point cloud model.(3)According to the relevant algorithms involved in point cloud registration,a 3D point cloud data integrated processing system is developed based on PCL library.The system can read and save a variety of point cloud data formats,realize file operations such as point cloud merging and point cloud format conversion,cover a variety of point cloud processing algorithms,and has the functions of point cloud filtering,feature extraction,registration and visualization,which improves the integrated level of point cloud data processing.The system can adjust the key parameters of the algorithm to improve the efficiency of point cloud data processing.