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An X-corner Detection Method Applied to Vision Positioning and Calibration

A corner detection and visual positioning technology, which is applied in the field of X corner detection, can solve the problems such as being unsuitable for parallel batch processing, and the algorithm calculation amount is large, so as to achieve the effect of enhancing robustness and improving detection speed.

Active Publication Date: 2021-09-21
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The proposed method is mainly to judge the strength of the X corner point through various feature calculations. The algorithm has a large amount of calculation and is not suitable for parallel batch processing.

Method used

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  • An X-corner Detection Method Applied to Vision Positioning and Calibration
  • An X-corner Detection Method Applied to Vision Positioning and Calibration

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] An X corner detection method applied to visual positioning and calibration, such as figure 2 shown, including:

[0047] S1: Acquire an image, using a trapezoidal window (such as figure 1 Sampling the image; set the side length of the trapezoidal window sampling as 2r pixels, and the trapezoidal window is a square, then the samples taken by the trapezoidal window contain a total of 8r-4 pixels, and r is less than Half of the side length of the smallest X corner point in the image; all pixels of the trapezoidal window are counted into a circular data queue, all pixels of the trapezoidal window are the sample data P, and the i-th pixel is recorded as P i , P i The grayscale value of f is i , i=1,2...(8r-4);

[0048] S2: Preliminarily determine whether the sample data P contains the X corner point according to the image characteristics of the X corner point, if the judgment condition is satisfied, then calculate the sub-pixel level position of the X corner point, other...

Embodiment 2

[0053] The X corner detection method applied to visual positioning and calibration according to Embodiment 1, the difference is that the step S2 includes:

[0054] S21: grayscale the sample data in turn; the threshold can be adaptively selected.

[0055] S22: Binarize the gray value of the sample data twice, the binarization threshold is mean±Δ, mean is the mean value of the gray value of the sample data, Δ is the threshold adjustment value, and the value range of Δ is 20-160 pixel. The value of Δ is related to the brightness of the whole image. Adding Δ as the threshold adjustment value can avoid the wrong judgment caused by the influence of image noise and enhance the robustness of the algorithm. Calculate the number of steps N of the sample data processed in step S21 s , if N s =4, then execute step S23, otherwise, execute step S5;

[0056] S23: Binarize the gray value of the sample data with the average value of the gray value of the sample data as the threshold; set t...

Embodiment 3

[0059] The X corner detection method applied to visual positioning and calibration according to Embodiment 1, the difference is that the step S3 includes:

[0060] S31: Judging the X corner point repeated detection flag, if the pixel value L of the X corner point obtained in step S23 is located in the inactive area, then it is determined that the X corner point has been detected, then jump out of this cycle, and execute step S5; otherwise, Execute step S32;

[0061] S32: Obtain the pixel value L of the X corner point and the gray value of the neighboring pixels, and the neighborhood refers to the range with the pixel value L of the X corner point as the center and the r pixel as the radius; The mean of , as the threshold, binarizes the neighborhood, and calculates the number of steps of the gray value ΔV C , if ΔV C >min_V, continue to perform step S4, otherwise, perform step S5; min_V=4.

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Abstract

The invention relates to an X corner point detection method applied to visual positioning and calibration, including: S1: collecting images, sampling the images by using a ring-shaped square window; S2: initially judging whether the sample data is Include the X corner point; S3: further judge whether the sample data contains the X corner point, and exclude the repeated judgment of the X corner point; S4: take the X corner point as the window center, reacquire the sample data, and judge whether the data satisfies the X corner point symmetry If the condition is satisfied, the sub-pixel position of the X corner point is calculated by the curve fitting method, and the repeated detection flag of the X corner point is set; S5: Steps S2 to S4 are repeated to detect all the X corner points. When the present invention samples the image, half of the side length of the sampling window is sampled each time, which improves the detection speed and does not miss the X corner point. The invention judges whether the sampling window contains the X corner point based on the image feature of the X corner point, thereby enhancing the robustness of the algorithm.

Description

technical field [0001] The invention relates to an X corner detection method applied to visual positioning and calibration, and belongs to the technical field of computer vision applications. Background technique [0002] Visual localization and calibration is an important part of 3D computer vision. One of the basic tasks of computer vision is to calculate the geometric information of objects in three-dimensional space from the image information obtained by the camera, and then reconstruct and identify the object. The interrelationship is determined by the geometric model of the camera imaging, and these geometric model parameters are the camera parameters. Under most conditions, these parameters must be obtained experimentally and computationally, a process known as visual calibration. The calibration process is to determine the geometric and optical parameters of the camera, the orientation of the camera relative to the world coordinate system; the visual positioning pr...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/80G06T7/60
CPCG06T7/60G06T2207/10016G06T2207/30208G06T7/80
Inventor 赵子健王芳
Owner SHANDONG UNIV