Feature line fused laser point cloud and analog image registration method

Abstract

The invention provides a feature line fused laser point cloud and analog image registration method, and aims to solve the problems that a point cloud distance intensity image or point feature-based image and point cloud registration method in the prior art has many defects of manual point selection errors, point cloud data interpolation errors, need of many observation values and the like, and theprecision is very limited. The invention provides a laser point cloud and analog image registration method based on two-dimensional and three-dimensional fusion characteristic lines. The method comprises the steps of: establishing a direct transformation model between the image feature line and the point cloud feature line. The requirement on the initial value of the exterior orientation elementof the image is not harsh, the method has high robustness to noise, realizes automatic matching of two-dimensional and three-dimensional fusion features, improves the degree of automation, achieves higher real-time performance, has the advantages of no model error, no need of exterior orientation element initial values, only need of a small number of registration elements and the like, is high incalculation speed, and meets the registration requirements of analog images and ground laser point cloud data.

Description

technical field [0001] The invention relates to a method for registering a laser point cloud and a simulated image, in particular to a method for registering a laser point cloud and a simulated image with feature line fusion, and belongs to the technical field of image registration methods. Background technique [0002] Photogrammetry mainly obtains the three-dimensional coordinates of the target through many images with a certain degree of overlap and a small number of ground control points. It is necessary to obtain the 3D information of the target through homonymous point matching and forward intersection, which is easily affected by factors such as the complexity of ground objects and occlusion between ground objects. Therefore, it is difficult to extract high-precision 3D information of the target only by using images. GPS / IMU-assisted photogrammetry technology still has significant problems such as low positioning accuracy, insufficient density of matching points with ...

AI Technical Summary

Problems solved by technology

However, due to the placement error of the optical camera and other reasons, there is a large error in the external orientation elements of the simulated image, which leads to the inability to fit well between the simulated image and the laser point cloud. Therefore, before the comprehensive application of the two data, they need to be included To a unified coordinate system, that is, the registration of the simulated image and the laser point cloud

[0005] For the registration of laser point cloud and simulated image, there are many limitations in the existing technology, which can be further summarized as follows: first, it is strongly dependent on point features and requires a large number of registration primitives, and the calculation complexity is high; second, generating point cloud The registration method of intensity images is based on a two-dimensional registration model, while the registration algorithm for image matching to generate point clouds is a three-dimensional registration model, which has the disadvantage of an imprecise registration model; the third is point cloud interpolation, projection, and image matching Errors will be introduced in the process; fourth, manual point selection is required, and the point features in the point cloud are relatively discrete, making it difficult to select accurately; fifth, manual matching of point features or straight line features is required, which not only has a low degree of automation, but also has manual errors, which affect Analytical accuracy; Sixth, it depends on the initial value of the external orientation elements of the image, and the number of iterations is large; Seventh, it is sensitive to noise and prone to local convergence

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