Method for obtaining Euclidean space coordinate conversion parameter based on minimum point set

Abstract

The invention relates to a method for obtaining a Euclidean space coordinate conversion parameter based on a minimum point set. The method comprises the following steps: 1, randomly extracting the minimum point set in point cloud; 2, calculating a coordinate conversion scaling proportionality coefficient lambda; 3, calculating a spatial similarity transformation model rotating matrix R; 4, calculating and drawing a translation vector t between a local coordinate system and world coordinate system; 5, counting correct points and gross errors; 6, executing judgment; and 7, outputting the Euclidean space coordinate conversion parameter. According to the method provided by the invention, the initial value of a spatial similarity transformation model is not needed, and the Euclidean space coordinate conversion parameter can be directly obtained according to the minimum point set, thereby avoiding the limitation that the traditional least square method requires the initial value. In combination with the RANSAC algorithm, the influence of the gross errors in control points to the calculation of the Euclidean space coordinate conversion parameter can be avoided, high-precision Euclidean space coordinate conversion parameter can be obtained, and the spatial similarity transformation between the coordinate systems can be calculated accurately.

Description

technical field [0001] The invention relates to the technical field of spatial similarity transformation model calculation, in particular to a method for obtaining parameters of Euclidean space coordinate transformation based on a minimum point set. Background technique [0002] The spatial similarity transformation is an optimal transformation that computes rotation, translation, and scaling between two coordinate systems, and is called an absolute orientation in photogrammetry. Spatial similarity transformation has a wide range of applications in geodesy, photogrammetry, computer vision, robot hand-eye calibration and other fields. With the development of surveying and mapping information science, digital earth and smart city have gradually entered people's daily life. Products such as map navigation, 3D street view, Internet of Things, and virtual reality are gradually changing people's lifestyles. Spatial similarity transformation is one of the core technologies of the...

AI Technical Summary

Problems solved by technology

If the initial value is not good, it is easy to cause the iteration to not converge, and the optimal spatial similarity transformation parameters cannot be obtained.

When the rigid transformation between two coordinate systems is too large, such as a large rotation angle or large-scale scaling, it is often difficult to obtain a good initial value, which further limits the promotion and application of the traditional least squares iterative method

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