Building point cloud registration algorithm based on dimension reduction

Abstract

The invention discloses a building point cloud registration algorithm based on dimension reduction, comprising the following steps: respectively acquiring building point cloud data at two sampling points at different angles of view, and numbering all the points in the point clouds; selecting the overlapping areas of the building point clouds at the two angles of view, and carrying out building point cloud plane fitting on the overlapping areas by means of the least square method to obtain a projection surface of the building point clouds; projecting the building point clouds on the building projection surface, and determining the coordinates of the projection points of all the points in the point clouds after the points in the point clouds are vertically projected on the building projection surface; re-sampling the projection points, and determining a two-dimensional image of the building point clouds after dimension reduction; searching same-name points in the two-dimensional image through template matching based on difference of squares; indexing the searched same-name points of the two-dimensional image back to a three-dimensional point cloud according to the mark number; and getting rotation and translation variables by means of the unit quaternion method according to the points in the three-dimensional point cloud indexed based on the same-name points, and applying the rotation and translation variables to the overall building point cloud. By using the method, the efficiency of registration is improved.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a building point cloud registration algorithm based on dimensionality reduction. Background technique [0002] Digital city construction is the focus of attention in the field of geographic information systems and urban informatization, and has broad application prospects in urban planning, public safety, and public geographic services. The key technology of a digital city is ground objects, especially the construction of 3D models of buildings. For example, 3D city model products represented by Google Earth, Baidu Maps, and Gaode Maps are on the rise. The 3D reconstruction of buildings has always been the core of digital cities. research content. [0003] Three-dimensional laser scanning technology (3DLaserScanningTechnology) can continuously, automatically, non-contact, and quickly collect a large number of three-dimensional point data on the surface of the target object, that ...

AI Technical Summary

Problems solved by technology

For building point clouds, when using PFH and FPFH of key points for feature extraction, due to the similarity of building structures, the local point feature vectors converge, which in turn leads to a serious decline in the reliability of point cloud feature matching.

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