High-precision point cloud registration method for three-dimensional laser scanning

Abstract

The invention provides a high-precision point cloud registration method for three-dimensional laser scanning. The method comprises steps of providing strategy of two steps from preposed coarse registration to iterative fine registration; carrying out curvature-based feature extraction on scattered point clouds which are obtained through three-dimensional laser scanning and have different visual angles to obtain feature points; and calculating coordinate transformation parameters of the matched feature point pairs to obtain a preposed coarse registration result, eliminating wrong homonymous points by using a distance constraint condition for the condition that the two-point cloud data is not in a subset relationship, and then performing iterative fine registration to obtain a final registration result. Feature point extraction is rapid and accurate, feature point matching accuracy is high, speed is high, coarse registration provides a good initial position for fine registration, after iterative fine registration is iterated to a certain number of times, an accurate result can reach very high precision, registration precision can reach the millimeter level, the method can be used forcomplex point cloud data, and engineering application requirements are met. The point cloud registration speed and precision are greatly improved, and the registration method is wide in application range.

Description

technical field [0001] The invention relates to a high-precision point cloud registration method, in particular to a high-precision point cloud registration method for three-dimensional laser scanning, and belongs to the technical field of three-dimensional laser point cloud. Background technique [0002] The 3D laser scanning technology can quickly obtain the 3D coordinates of the surface of the object without direct contact with the object. Scanning technology constructs 3D models. The main process is divided into two aspects: field data acquisition and office data processing. With the rapid development of 3D laser scanning hardware, it is no longer technically difficult to obtain accurate 3D coordinates for field data acquisition. Industrial data processing has become the key to technological development. [0003] Due to the limitation of the viewing angle, the existing technology cannot scan the target object to obtain complete data information through only one measurin...

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Problems solved by technology

[0006] No matter what kind of point cloud registration method is used in the existing technology, there are obvious deficiencies. They are suitable for some environments but cannot meet all needs. Some registration methods do not have sufficient theoretical and experimental support, and the registration accuracy is very low. , which seriously reduces the quality of follow-up work; in some cases, the computer automatically recognizes overlapping areas based on certain algorithms, and automatically selects point pairs in the overlapping areas for registration parameter calculation and registration. Due to the complexity of point cloud data, the quality is not good or bad. Different, the applicable conditions cannot meet various complex environments, massive point cloud data not only increases the difficulty of registration, but also increases the time of automatic registration, the registration speed is slow, and the registration effect is poor; some need to build targets , the application area is very narrow; some require manual participation in the whole process, such as manual visual search for feature points, the manual workload is huge, the efficiency is very low, the registration accuracy is not high, and it cannot be used for complex point cloud data, and cannot meet engineering applications. Require

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