Automatic registration method for large scale three dimension scene multiple view point laser scanning data

Abstract

The present invention relates to automatic multiple view point laser scanning data registering method for large scale 3D scene. The method includes the following steps: obtaining the data of the measured scene by using laser range finder with Z-axis regulated to be perpendicular to ground and X-axis and Y-axis to be parallel to ground; scanning the measured scene to obtain the 3D data of each view point in the superposing rate of 10-20 %; extracting the structure and characteristic of the measured matter; calculating the virtual characteristic unit and constituting characteristic unit, coarsely registering, finely registering, overall registering and finally establishing registered model. The method of the present invention is widely applicable.

Description

technical field [0001] The invention belongs to a three-dimensional data registration method in the field of computer information processing, in particular to a large-scale three-dimensional scene multi-view point laser scanning data automatic registration method. Background technique [0002] 3D data registration is the key technology of digitization and reverse engineering. Especially with the emergence of medium and long-distance 3D laser scanning technology, the process of 3D data acquisition has been simplified, and the development of digital cities, digital navigation, virtual reality, urban planning, digital protection of cultural relics and other related fields has been promoted. Ground-based 3D laser scanning data Highly regarded. In order to obtain a complete geometric description of large-scale 3D scenes, it is necessary to collect laser 3D scanning data at dozens or even hundreds of viewpoints, so there is a registration problem of converting laser 3D scanning d...

AI Technical Summary

Problems solved by technology

[0003] The traditional research on multi-view 3D data registration mostly focuses on a single object, and the most representative one is the closest point iterative algorithm, that is, the ICP algorithm. This method finds corresponding points in the laser scanning point set of two viewpoints iteratively, and calculates the positions of the two viewpoints. The disadvantage of the relationship is that it is necessary to manually set the initial conversion estimation, which is easy to fall into the local minimum, and the iteration time is long, which is not suitable for large-scale registration. There are two commonly used registration methods for large-scale scenes: target control point method and external equipment such as GPS The target control point method requires that targets be placed in overlapping areas of adjacent viewpoints, and the target control points are automatically found by using the special reflection characteristics of the laser on the target. As long as there are more than three control points with the same name in the laser scanning data of adjacent viewpoints, the If there is a substance with the same reflection characteristics as the target in the scene, the target will be misidentified, and the registration will fail. The target control point method requires a lot of manual intervention, and some environments cannot place the target GPS and other external equipment methods. It is mainly to obtain the pose parameters of the laser scanner at each viewpoint with the help of external equipment. Not only is the equipment expensive, but the accuracy of the pose parameters is not high, and the registration results are different from the actual situation. Therefore, there is an urgent need for a method that does not use any external equipment. The automatic large-scale 3D scene multi-viewpoint 3D data registration method that directly seamlessly combines the 3D laser scanning data of each viewpoint into the same coordinate system has been found through literature search and analysis. The article "Automated Feature-Based Range Registration of Urban Scenes of Large Scale" proposed a method of using feature lines to realize automatic registration of 3D laser data from adjacent viewpoints, and said that line features reduce geometric complexity. It is suitable for large-scale registration. The disadvantage of this method is that only using linear features is not universal. For 3D scenes such as large cultural relics and ancient buildings with less linear features, the registration often fails. In addition, it cannot solve the influence of practical factors such as occlusion. Registration issues with lack of valid linear features in overlapping regions

[0004] The above method is intended to solve the two-viewpoint 3D data registration with overlapping areas. As for dozens or even hundreds of viewpoints, an effective registration strategy must be adopted to merge the 3D data into the same coordinate system. Strategies include sequential registration and synchronous registration. Sequence registration means that after a pair of viewpoints are registered, another pair of viewpoints starts to register, and must include one viewpoint in the previous pair of viewpoints, and the sequence is interlocked until all viewpoints are traversed. The advantage of the registration strategy is that only one pair of viewpoints participate in the registration at the same time, and it takes up less memory; the disadvantage is that there is a large cumulative error, especially in closed-loop scenarios, where there is a gap between the first and last viewpoints. The synchronous registration criterion is that all viewpoints are registered at the same time , there will be no cumulative error, high registration accuracy, but a large amount of calculation, high requirements for computing equipment, neither sequence registration nor simultaneous registration is suitable for large-scale scenarios

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