Iterative camera calibration method and device based on elliptic dual quadratic curve

Abstract

The invention relates to an iterative camera calibration method based on an ellipse dual quadratic curve. The method comprises the following steps: extracting ellipse circle center coordinates on a calibration plate image; calibrating the camera according to the two-dimensional pixel coordinates and the three-dimensional space coordinates corresponding to the ellipse circle center coordinates, and solving to obtain camera parameters and a first projection error; correcting perspective projection and lens distortion in the calibration plate image, and converting the calibration plate image to a forward view; extracting circle center coordinates of a perfect circle on the corrected calibration plate image; re-projecting the perfect circle center coordinate back to the camera coordinate system, re-calibrating the camera according to the coordinate of the perfect circle center coordinate in the camera coordinate system, and solving to obtain a second re-projection error; and carrying out convergence judgment according to the first projection error and the second projection error. According to the iterative camera calibration method based on the elliptic dual quadratic curve, the re-projection error can be reduced by 92.4%, and the calibration precision of the camera is greatly improved.

Description

technical field [0001] The invention relates to the technical field of machine vision, in particular to an iterative camera calibration method and device based on an ellipse dual quadratic curve. Background technique [0002] In the process of image measurement and machine vision applications, in order to determine the relationship between the three-dimensional geometric position of a point on the surface of a space object and its corresponding point in the image, it is necessary to establish a geometric model of camera imaging. These geometric model parameters are camera parameters, Under most conditions, these parameters must be obtained through experiments and calculations. This process of solving parameters is called camera calibration. Therefore, in machine vision applications, the calibration of camera parameters is a very critical link. However, in machine vision, Since the corners of the checkerboard are sensitive to noise and image quality, the detection accuracy of...

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Problems solved by technology

[0002] In the process of image measurement and machine vision applications, in order to determine the relationship between the three-dimensional geometric position of a point on the surface of a space object and its corresponding point in the image, it is necessary to establish a geometric model of camera imaging. These geometric model parameters are camera parameters. Under most conditions, these parameters must be obtained through experiments and calculations. This process of solving parameters is called camera calibration. Therefore, in machine vision applications, the calibration of camera parameters is a very critical link. However, in machine vision, Because the corners of the checkerboard are sensitive to noise and image quality, the detection accuracy of the corner coordinates is low, and the circular features have a strong suppression of noise and high detection accuracy. widely used in

[0003] However, when the camera is calibrated in the circle mode, the planar calibration plate mainly relies on a set of contour points extracted in advance. Since it cannot perform multiple calibrations, it is very sensitive to non-uniform illumination such as light sources and reflections. The projected ellipse The center of the circle is not necessarily the projection of the center of the perfect circle. At the same time, the ring is also affected by distortion, which in turn produces eccentricity errors and affects the camera calibration accuracy.

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