A Registration Method of Terrestrial Laser Point Cloud

Abstract

The invention discloses a ground laser point cloud registration method, which automatically estimates the average distance and length of the point cloud through the original point cloud data, and obtains the size of the voxel grid for establishing the point cloud pyramid. At the top layer of the pyramid point cloud, the FPFH features of all points are established, and then the initial matching is obtained through bidirectional consistency detection, which can quickly and effectively realize the initial registration of the point cloud. On this basis, the coarse-to-fine ICP optimization is performed using the 3D points in the neighborhood of the initial matching results. While greatly reducing the number of participating ICP point clouds, the point cloud information in the neighborhood of the same-named points is more abundant, which is beneficial to the ICP algorithm. The iterations converge quickly.

Description

technical field [0001] The invention belongs to the technical field of three-dimensional reconstruction, and relates to registration between point cloud data of two stations with appropriate overlapping areas. Background technique [0002] In recent years, laser scanning technology has developed rapidly. Laser scanning technology has the characteristics of high precision and high efficiency, and can quickly and accurately obtain the distance of the target relative to the scanner, so as to obtain the precise three-dimensional point coordinates of the point indicated by the scanned target relative to the scanner. With the development of technology, it is easier and cheaper to obtain point clouds. Point cloud processing technology is widely used in reverse engineering, human body scanning, cultural relics protection, virtual reality, augmented reality, 3D urban reconstruction, automatic driving and robot vision. Due to the complexity of the object or scene, the limitation of ...

AI Technical Summary

Problems solved by technology

Whether it is persist analysis FPFH or ISS FPFH, it is necessary to calculate and analyze a large number of points and point neighborhoods. The calculation amount is large and the calculation efficiency is not high. Keypoint-based4 Points CongruentSets rely on feature point extraction, which requires high feature point extraction. It needs to be able to extract an appropriate number of feature points, and it also needs to set a priori point cloud overlap parameters like Super4points Congruent Sets, otherwise it will lead to registration errors or a lot of time for registration

These methods are deficient in terms of anti-noise performance, registration robustness, and registration efficiency.

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