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Method for improving precision of external reference calibration of multi-ocular camera based on multiple identification plate images

A multi-camera, precision calibration technology, which is applied in image analysis, image data processing, instruments, etc., can solve the problems of large modeling errors and inaccurate mutual positional relationships between multiple cameras, and achieve the effect of reducing errors

Active Publication Date: 2017-09-22
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of large modeling errors caused by the inaccurate mutual positional relationship between multi-cameras in the existing three-dimensional modeling, and propose a multi-camera extrinsic calibration accuracy based on multiple recognition calibration plate images Improve method

Method used

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  • Method for improving precision of external reference calibration of multi-ocular camera based on multiple identification plate images
  • Method for improving precision of external reference calibration of multi-ocular camera based on multiple identification plate images
  • Method for improving precision of external reference calibration of multi-ocular camera based on multiple identification plate images

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specific Embodiment approach 1

[0023] Specific implementation mode one: as figure 1 As shown, a method for improving the calibration accuracy of external parameters of a multi-eye camera based on multiple recognition calibration board images is implemented in the following steps:

[0024] Step 1: Set the black and white checkerboard or two-dimensional code as the calibration object, move the calibration object to different positions, and obtain the calibration information of the calibration object through simultaneous observation by N groups of cameras (the N groups of cameras are labeled A, B, C...) at each position , image and point cloud information, a set of calibration information is obtained for each position, a total of i set of calibration information is obtained, where N is greater than or equal to 2; the calibration information of the calibration object is the 2D image obtained by camera observation, and the camera coordinate system is obtained Transformation matrix to the coordinate system formed...

specific Embodiment approach 2

[0034] Specific embodiment two: the difference between this embodiment and specific embodiment one is: the specific process of obtaining the conversion matrix of the calibration coordinate system in the step two is:

[0035] Group 1 calibration information:

[0036]

[0037] where matrix Represents the transformation matrix of the coordinate system formed by the camera A coordinate system to the first group of calibration objects, represents the rotation matrix, represents the translation matrix;

[0038] Group i calibration information:

[0039]

[0040] in The matrix represents the conversion matrix from the coordinate system of camera A to the coordinate system formed by the i-th group of calibration objects.

[0041] Other steps and parameters are the same as those in Embodiment 1.

specific Embodiment approach 3

[0042] Specific embodiment 3: The difference between this embodiment and specific embodiment 1 or 2 is that in the step 3, the initial camera and the camera to be converted are selected, and the two sets of cameras required for calibration are obtained through the conversion matrix between the two groups of cameras and the calibration object. The specific process of coordinates is:

[0043] The transformation matrix from coordinate system 1 to coordinate system 2 is The coordinate system 1 is the coordinate system formed by the calibration object in the first group of calibration information, and the coordinate system 2 is the coordinate system formed by the calibration object in the second group of calibration information; Represents the transformation matrix from coordinate system 1 to coordinate system 2 observed by camera A, Represents the transformation matrix from coordinate system 1 to camera A coordinate system; Numerically yes inverse of.

[0044]In the coord...

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Abstract

The present invention relates to a method for improving precision of external reference calibration of a multi-ocular camera based on multiple identification plate images, in order to solve the problem that the modeling error is large caused by that the positional relationship between the multi-phase cameras in the existing three-dimensional modeling is not accurate. The method for using the high precision calibration to calibrate the low precision provided by the present invention, the direction error of the axial direction is calibrated through the single point displacement error so as to reduce the overall calibration error; according to the method provided by the present invention, more spatial calibration information is constantly collected, and the spatial calibration information is taken to calibrate coordinate origin information and compensate the direction axis error, so that the effect that the characteristic with the large error degree is compensated by the information with small error degree; by virtue of the method provided by the present invention, the calibration error of the two-phase camera is reduced from that the original error level of the direction axis can be determined by multi-point measurement to the error level that the origin position can be determined by single-point measurement; and the method of the present invention is applied to the field of camera calibration.

Description

technical field [0001] The invention relates to a method for improving the calibration accuracy of external parameters of a multi-purpose camera based on multiple recognition of calibration plate images. Background technique [0002] With the development of color depth cameras, it is more and more convenient to obtain high-quality 3D point clouds, which also promotes the development and application of stereo vision, such as 3D measurement, 3D modeling, etc. However, since the field of view of a single camera cannot cover the model we need, we usually use three to four or even more cameras to obtain data when obtaining model information. Therefore, how to accurately fuse the data obtained by each camera has become a crucial part of this kind of project. [0003] At present, the dual-target calibration method is widely used. At the same time, you can also set your own calibration method, use computer vision to identify calibration objects such as black and white chessboards, ...

Claims

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Application Information

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IPC IPC(8): G06T7/80
CPCG06T7/85
Inventor 高会军薛盛日李湛林伟阳
Owner HARBIN INST OF TECH
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