CN112634373A - Zero-expansion ceramic calibration plate-based dynamic correction method for vision measurement system - Google Patents
Zero-expansion ceramic calibration plate-based dynamic correction method for vision measurement system Download PDFInfo
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Abstract
本发明提出一种基于零膨胀陶瓷标定板的视觉测量系统校正方法，能够克服测试过程中高低温变化及传输路径气流密度变化对测量准确度的影响。在视觉测量系统的被测视场空间内放置零膨胀陶瓷标定板，零膨胀陶瓷标定板上设置有靶点；测量时，视觉测量系统中的各相机进行实时图像采集，提取得到零膨胀陶瓷标定板上各靶点的图像坐标，并计算重投影误差e，当e大于预设阈值s时，进行参数校正；参数校正过程为：首先建立零膨胀陶瓷标定板上各靶点的真实值与预测值之间的关系，进而得到畸变校正模型；然后将视觉测量系统静态参数标定条件下直接计算出的测点空间相对坐标输入到畸变校正模型中进行计算，其输出值即为畸变校正后的测点空间三维坐标。
The invention proposes a calibration method for a vision measurement system based on a zero-expansion ceramic calibration plate, which can overcome the influence of high and low temperature changes and changes in the airflow density of the transmission path on the measurement accuracy during the test process. A zero-expansion ceramic calibration plate is placed in the measured field of view space of the vision measurement system, and a target point is set on the zero-expansion ceramic calibration plate; during measurement, each camera in the vision measurement system collects real-time images, and extracts the zero-expansion ceramic calibration. The image coordinates of each target point on the board are calculated, and the reprojection error e is calculated. When e is greater than the preset threshold s, parameter correction is performed; the parameter correction process is as follows: first, establish the true value and prediction of each target point on the zero-expansion ceramic calibration board Then, the spatial relative coordinates of the measuring points calculated directly under the static parameter calibration condition of the vision measurement system are input into the distortion correction model for calculation, and the output value is the distortion corrected measurement. 3D coordinates of point space.
Description
Technical Field
The invention relates to a correction method, in particular to a dynamic correction method of a vision measurement system based on a zero-expansion ceramic calibration plate.
Background
The stereo vision measurement technology is realized based on the parallax principle, a plurality of cameras (one of which is a reference camera and the spatial position and attitude relations of other cameras relative to the reference camera are known) with known spatial position and attitude relations are utilized to simultaneously acquire images of measured features in different directions, and homonymy image point pairs corresponding to the measured features are acquired through image processing, homonymy point matching and other technologies; and then, establishing an imaging light equation by using a camera imaging model, establishing light triangle intersection constraint, establishing a multi-view vision measurement mathematical model, and further calculating the three-dimensional coordinates of the measured feature space.
In the vision measurement system, the spatial transformation relationship (i.e. the camera external parameters) between the multi-camera coordinate systems and each camera distortion parameter (i.e. the camera internal parameters) are the prior conditions which must be known in the stereoscopic vision measurement model resolving process, and need to be calibrated and oriented in advanceTechnique ofAnd acquiring internal and external parameters of the camera.
The camera calibration intrinsic parameters comprise the intrinsic orientation parameters of the camera and various lens distortions. The internal orientation parameters of the camera include the principal point coordinates and principal distance (i.e., focal length) of the camera; the lens distortion refers to a point location error of an image point deviating from an ideal position caused by design, manufacture and equipment of a camera objective system, and comprises a radial distortion coefficient, an eccentric distortion coefficient and an area array deformation coefficient.
Outside cameraThe parameters include three-dimensional translation parameters (T)x，Ty，Tz) And three-directional rotation parameter (theta)x，θy，θz)。
However, in some special occasions, such as wind tunnel tests, the environment is dynamically changed in the measurement process, the temperature change of wind tunnel airflow is large, the temperature gradient of a gas medium can influence the transmission path of light, and the airflow density can refract and distort the optical path along with the change of temperature and flow speed; the traditional method for statically calibrating and dynamically measuring the vision measuring system is not applicable any more, and the parameters of the vision measuring system calibrated under a static condition only comprise internal and external parameters of a camera and distortion parameters of an optical system; the change of the environmental airflow in the test can introduce new distortion, and dynamic correction is needed; but the distortion caused by the environment does not conform to the traditional lens distortion model.
Disclosure of Invention
In view of the above, the invention provides a dynamic correction method for a vision measurement system based on a zero-expansion ceramic calibration plate, which is used for dynamically correcting distortion of the vision measurement system applied in special environments such as a wind tunnel test and the like, and can overcome the influence of high and low temperature changes and changes in the airflow density of a transmission path on measurement accuracy in a test process.
The dynamic correction method of the vision measurement system based on the zero-expansion ceramic calibration plate comprises the following steps:
placing a zero-expansion ceramic calibration plate in a measured view field space of the vision measurement system, wherein more than four circular target points are arranged on the zero-expansion ceramic calibration plate;
firstly, carrying out static parameter calibration on the vision measurement system, including calibration of internal parameters and external parameters, thereby determining an internal parameter matrix of each camera, a distortion model of each camera and an external parameter matrix of each camera relative to a reference camera in the vision measurement system;
during measurement, each camera in the vision measurement system acquires images in real time, extracts and obtains image coordinates of each target point on the zero expansion ceramic calibration plate through the images of the zero expansion ceramic calibration plate in the measured view field space acquired by each camera, and then calculates the reprojection error e of each target point in the images acquired by each non-reference camera relative to the reference camera; if e is greater than s, the parameter correction is required, and if e is less than or equal to s, the parameter correction is not required; wherein s is a preset reprojection error threshold;
the parameter correction process comprises the following steps:
the space relative coordinate of each target point on the zero-expansion ceramic calibration plate is a known standard value, and the space relative coordinate is a true value; the spatial relative coordinates of each target point measured by the vision measurement system under the calibration and adjustment of the static parameters are taken as predicted values, and the relationship between the real values and the predicted values of each target point on the zero-expansion ceramic calibration plate is established, so that a distortion correction model is obtained;
and inputting the relative coordinates of the measuring point space directly calculated under the static parameter calibration condition of the vision measurement system into the distortion correction model for calculating aiming at the image pair of which the reprojection error exceeds a preset reprojection error threshold value and needs parameter correction, wherein the output value of the image pair is the three-dimensional coordinates of the measuring point space after distortion correction.
As a preferred embodiment of the present invention: and establishing a distortion correction model through a neural network, and optimizing and training the network to obtain the distortion correction model by taking the spatial relative coordinates measured under the condition of calibrating the static parameters of each target point as input data samples and the known standard value of each spatial relative coordinate as output data samples when the neural network is established.
As a preferred embodiment of the present invention: the reprojection error e is calculated by the following equation:
wherein: e.g. of the typexRe-projection error of line coordinate in camera image coordinate system; e.g. of the typeyRe-projection errors of the row coordinates in the camera image coordinate system; x is the line coordinate of the target point in the non-reference camera image; y is the column coordinate of the target point in the non-reference camera image; f is the focal length of the non-reference camera; x, Y, Z calculating target point of vision measuring system in space coordinate systemThree-dimensional coordinates; xS、YS、ZSThe real value of the three-dimensional coordinate of the target point under a space coordinate system; x is the number of0And y0Is the principal point coordinate of a reference camera in the vision measuring system; r is1To r9The parameters of the rotation matrix in the external parameters are respectively.
As a preferred embodiment of the present invention: the flatness of the zero-expansion ceramic calibration plate is better than 10-2Delta, the position degree and the roundness of the target point on the zero-expansion ceramic calibration plate are better than 10-2δ, where δ is the maximum value of the measurement error of the vision measurement system.
As a preferred embodiment of the present invention: more than two zero-expansion ceramic calibration plates are placed in the space of the measured visual field, and the zero-expansion ceramic calibration plates are arranged around the measured object.
As a preferred embodiment of the present invention: the target points on the zero-expansion ceramic calibration plate are arranged in a square matrix.
As a preferred embodiment of the present invention: the formation mode of the target point on the zero-expansion ceramic calibration plate is as follows: processing a blind hole on the reference surface of the zero-expansion ceramic calibration plate; then placing a filling cylinder in each blind hole, wherein the filling cylinder is in interference fit with the blind hole; the color of the base body of the zero-expansion ceramic calibration plate is white, and the color of the filling cylinder is dark color or black, so that a target point is formed.
Has the advantages that:
(1) the invention adopts the zero-expansion calibration plate to dynamically correct distortion of the vision measurement system in the wind tunnel measurement test, provides a stable and reliable physical standard through the zero-expansion calibration plate, and can overcome the influence of environmental factors such as wind tunnel field high and low temperature change, airflow disturbance and the like on the vision measurement system.
(2) The method adopts a neural network mode to construct the distortion correction model, reduces the influence of the parameterized model on the measurement result, realizes the distortion correction of the wind tunnel vision measurement system, and improves the measurement accuracy of the vision system in severe environments such as high and low temperature change, airflow turbulence and the like.
Drawings
FIG. 1 is a flow chart of the calibration method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The embodiment provides a zero-expansion ceramic calibration plate-based dynamic correction method for a vision measurement system, which is applied to parameter optimization of the vision measurement system in a special environment of a wind tunnel test.
Visual measurement system installs outside the wind-tunnel observation window, measures the testee in the wind-tunnel through the observation window, and visual measurement system includes: two cameras, a lens, an image acquisition computer and a cable; the range shown by the dotted line in the figure is a measurable area of the vision measuring system, and the measured object is positioned at the air outlet of the wind tunnel.
The method firstly needs to prepare a zero-expansion calibration plate: the zero expansion ceramic calibration plate is made of near-zero expansion ceramic matrix composite material, the material is prepared by compounding negative expansion material and positive expansion material according to proper proportion and process, the thermal expansion coefficient of the prepared near-zero expansion material is controllable, due to the low thermal expansion coefficient, when the material is subjected to large temperature gradient change, the whole size change is very small, and the thermal expansion coefficient of the material is about 1 multiplied by 10 within the range from normal temperature to 1000 DEG C-6/K。
The zero-expansion ceramic calibration plate is of a planar structure, and the flatness is better than 10-2Delta, setting round target points on the calibration plate, arranging the target points in a square matrix, wherein the number of the target points is 25 or 49, and the position degree and the roundness of the target points are better than 10-2δ, where δ is the measurement error requirement (i.e., the maximum value of the measurement error) of the vision measurement system. For example, if the measurement error of the vision measurement system is required to be less than 1mm, i.e. the measurement error is required to be 1mm, the flatness of the calibration plate should be better than 0.01mm, and the position and roundness of the target point should be better than 0.01 mm. The target point can be installed by processing a blind hole array on the datum plane of the calibration plate and then processing a filling cylinder, wherein the diameter tolerance of the filling cylinderThe diameter of the filling cylinder is larger than that of the blind hole, so that the filling cylinder is in interference fit with the blind hole; the color of the base body of the calibration plate is white, and the color of the filling cylinder is dark color or black which is convenient to distinguish from the base body, so that the target point is formed.
Then erecting a vision measuring system and a prepared calibration plate on a wind tunnel test site: and placing the zero-expansion ceramic calibration plate in a space of a measured visual field in a wind tunnel, and placing 3 to 4 zero-expansion ceramic calibration plates under the condition of not shielding a measured object. In this example, the calibration plate is in the form of a 7 x 7 target array with three zero expansion ceramic calibration plates placed around the object to be measured.
As shown in fig. 1, the method for calibrating a vision measuring system based on a zero-expansion ceramic calibration plate comprises the following specific steps:
the method comprises the following steps: static parameter calibration of a vision measurement system:
before a vision measuring system is erected on a wind tunnel test site, static parameter calibration is carried out on the vision measuring system, namely, internal parameters and external parameters of the vision measuring system are calibrated under a normal-temperature static condition, so that an internal parameter matrix of each camera, a distortion model of each camera and an external parameter matrix among multiple cameras in the vision measuring system are determined (a reference camera is arranged, and the external parameter matrix among the multiple cameras refers to the external parameter matrix among the cameras (non-reference cameras) except the reference camera and the reference camera in the vision measuring system);
taking a stereoscopic vision measuring system with two cameras as an example, the two cameras are respectively a left camera and a right camera, and the internal parameter matrix M of the left camera is determined by calibrating static parameters of the stereoscopic vision measuring systemLInner parameter matrix M of right cameraRTwo cameras' external parameter matrix MLRAnd a distortion model;
the left camera intrinsic parameter matrix, the right camera intrinsic parameter matrix and the two camera extrinsic parameter matrices can have various parameter forms, and the following are taken as examples:
wherein: f. ofLIs the focal length of the left camera, dxLDifferentiating the left camera line coordinates, dyLTo differentiate the left camera column coordinates, x0LIs the line coordinate of the principal point of the left camera, y0LIs the coordinates of the main point row of the left camera;
fRis the focal length of the right camera, dxRTo differentiate the right camera line coordinates, dyRTo differentiate the right camera column coordinates, x0RIs the line coordinate of the principal point of the right camera, y0RIs the right camera principal point row coordinate;
r1to r9Respectively, the parameters of the rotation matrix in the camera extrinsic parameters, txThe parameters of the translation matrix in the camera external parameters are shown.
Distortion parameters caused by the camera optical system include: coefficient of radial distortion (k)1，k2) Coefficient of eccentric distortion (p)1，p2) And area array distortion coefficient(s)1，s2) (ii) a The distortion model of the camera is as follows:
wherein: deltaxRefers to the distortion in the x-direction, deltayRefers to distortion in the y direction, x refers to the row coordinates in the camera image coordinates, and y refers to the column coordinates in the camera image coordinates;
step two: erecting a calibration plate on a wind tunnel test site: and placing the zero-expansion ceramic calibration plate in a space of a measured visual field in a wind tunnel, and placing 3 to 4 zero-expansion ceramic calibration plates under the condition of not shielding a measured object.
Step three: judging a correction threshold value:
after the wind tunnel test is started, each camera in the vision measurement system carries out real-time image acquisition, image coordinates of a target point on a calibration plate are extracted and obtained through calibration plate images acquired by each camera, and a reprojection error e of each non-reference camera relative to a reference camera is calculated according to the following formula:
wherein: e.g. of the typexRe-projection error of line coordinate in camera image coordinate system; e.g. of the typeyRe-projection errors of the row coordinates in the camera image coordinate system; x is the line coordinate of the target point in the non-reference camera image; y is the column coordinate of the target point in the non-reference camera image; f is the focal length of the non-reference camera; x, Y, Z is the three-dimensional coordinate of the target point calculated by the vision measuring system in the space coordinate system; xS、YS、ZSThe real value of the three-dimensional coordinate of the target point under a space coordinate system; x is the number of0And y0Is the principal point coordinate of a reference camera in the vision measuring system;
and (4) setting a preset threshold s as 0.2 pixel, and if e is greater than s, performing a parameter correction process (namely step four and step five), and if e is less than or equal to s, not needing to perform parameter correction.
Step four: establishing a distortion correction model through a neural network
Establishing a relation between the spatial relative coordinates of the target points on the calibration plate and the spatial relative coordinates of each target point predicted by the static model through a neural network, and further obtaining a distortion correction model, specifically:
the space relative coordinate of each target point on the calibration plate is a known standard value (namely a true value); the image coordinates of each target point can be calculated by the image acquired by the camera in the vision measuring system, and the output value predicted by the static model can be obtained according to the system parameters calibrated under the static condition, namely the space relative coordinates of each target point under the calibration and adjustment of the static parameters;
let the standard value of the space relative coordinate of the ith target point be Pbi(xbi,ybi,zbi) And the space relative coordinate measured under the condition of static parameter calibration is Pji(xji,yji,zji) (ii) a The difference between the true value and the predicted value of the static model is:
(δxi,δyi,δzi)＝(xbi,ybi,zbi)-(xji,yji,zji)
wherein deltaxi、δyi、δziAre the difference values of the three directions of the space three-dimensional coordinate system respectively.
Based on the method, a neural network model is constructed, the space relative coordinates measured under the condition of calibrating the static parameters of each target point are used as input data samples, the standard values of the space relative coordinates are used as output data samples, and the distortion correction model is obtained through optimization and training.
Step five: distortion correction:
for each frame of image collected by the vision measurement system, the coordinates of the measuring points directly calculated under the static parameter calibration condition of the vision measurement system are input into a distortion correction model for calculation aiming at the image pair which exceeds the threshold s and needs distortion correction, and the output value is the three-dimensional coordinates of the measuring point space after distortion correction.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (7)
1. The dynamic correction method of the vision measurement system based on the zero-expansion ceramic calibration plate is characterized by comprising the following steps of:
placing a zero-expansion ceramic calibration plate in a measured view field space of the vision measurement system, wherein more than four circular target points are arranged on the zero-expansion ceramic calibration plate;
firstly, carrying out static parameter calibration on the vision measurement system, including calibration of internal parameters and external parameters, thereby determining an internal parameter matrix of each camera, a distortion model of each camera and an external parameter matrix of each camera relative to a reference camera in the vision measurement system;
during measurement, each camera in the vision measurement system acquires images in real time, extracts and obtains image coordinates of each target point on the zero expansion ceramic calibration plate through the images of the zero expansion ceramic calibration plate in the measured view field space acquired by each camera, and then calculates the reprojection error e of each target point in the images acquired by each non-reference camera relative to the reference camera; if e is greater than s, the parameter correction is required, and if e is less than or equal to s, the parameter correction is not required; wherein s is a preset reprojection error threshold;
the parameter correction process comprises the following steps:
the space relative coordinate of each target point on the zero-expansion ceramic calibration plate is a known standard value, and the space relative coordinate is a true value; the spatial relative coordinates of each target point measured by the vision measurement system under the calibration and adjustment of the static parameters are taken as predicted values, and the relationship between the real values and the predicted values of each target point on the zero-expansion ceramic calibration plate is established, so that a distortion correction model is obtained;
and inputting the relative coordinates of the measuring point space directly calculated under the static parameter calibration condition of the vision measurement system into the distortion correction model for calculating aiming at the image pair of which the reprojection error exceeds a preset reprojection error threshold value and needs parameter correction, wherein the output value of the image pair is the three-dimensional coordinates of the measuring point space after distortion correction.
2. The zero expansion ceramic calibration plate based vision measurement system dynamic correction method of claim 1, wherein: and establishing a distortion correction model through a neural network, and optimizing and training the network to obtain the distortion correction model by taking the spatial relative coordinates measured under the condition of calibrating the static parameters of each target point as input data samples and the known standard value of each spatial relative coordinate as output data samples when the neural network is established.
3. The zero expansion ceramic calibration plate based vision measurement system dynamic correction method of claim 1 or 2, characterized in that: the reprojection error e is calculated by the following equation:
wherein: e.g. of the typexIs a phase ofReprojection error of line coordinate in machine image coordinate system; e.g. of the typeyRe-projection errors of the row coordinates in the camera image coordinate system; x is the line coordinate of the target point in the non-reference camera image; y is the column coordinate of the target point in the non-reference camera image; f is the focal length of the non-reference camera; x, Y, Z is the three-dimensional coordinate of the target point calculated by the vision measuring system in the space coordinate system; xS、YS、ZSThe real value of the three-dimensional coordinate of the target point under a space coordinate system; x is the number of0And y0Is the principal point coordinate of a reference camera in the vision measuring system; r is1To r9The parameters of the rotation matrix in the external parameters are respectively.
4. The zero expansion ceramic calibration plate based vision measurement system dynamic correction method of claim 1 or 2, characterized in that: the flatness of the zero-expansion ceramic calibration plate is better than 10-2Delta, the position degree and the roundness of the target point on the zero-expansion ceramic calibration plate are better than 10-2δ, where δ is the maximum value of the measurement error of the vision measurement system.
5. The zero expansion ceramic calibration plate based vision measurement system dynamic correction method of claim 1 or 2, characterized in that: more than two zero-expansion ceramic calibration plates are placed in the space of the measured visual field, and the zero-expansion ceramic calibration plates are arranged around the measured object.
6. The zero expansion ceramic calibration plate based vision measurement system dynamic correction method of claim 1 or 2, characterized in that: the target points on the zero-expansion ceramic calibration plate are arranged in a square matrix.
7. The zero expansion ceramic calibration plate based vision measurement system dynamic correction method of claim 1 or 2, characterized in that: the formation mode of the target point on the zero-expansion ceramic calibration plate is as follows: processing a blind hole on the reference surface of the zero-expansion ceramic calibration plate; then placing a filling cylinder in each blind hole, wherein the filling cylinder is in interference fit with the blind hole; the color of the base body of the zero-expansion ceramic calibration plate is white, and the color of the filling cylinder is dark color or black, so that a target point is formed.
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| CN116448662A (en) * | 2022-12-16 | 2023-07-18 | 中国科学院长春光学精密机械与物理研究所 | Zero crossing point correction method based on neural network |
| CN116935077A (en) * | 2023-07-26 | 2023-10-24 | 湖南视比特机器人有限公司 | Template matching optimization method and system based on encoding and decoding |
| WO2024021654A1 (en) * | 2022-07-28 | 2024-02-01 | 江苏集萃智能光电系统研究所有限公司 | Error correction method used for line structured light 3d camera, and apparatus |
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| CN114001682B (en) * | 2021-11-19 | 2023-08-01 | 天津博迈科海洋工程有限公司 | Flatness judging method for mounting surface of heavy door frame of electric room module |
| WO2024021654A1 (en) * | 2022-07-28 | 2024-02-01 | 江苏集萃智能光电系统研究所有限公司 | Error correction method used for line structured light 3d camera, and apparatus |
| CN116448662A (en) * | 2022-12-16 | 2023-07-18 | 中国科学院长春光学精密机械与物理研究所 | Zero crossing point correction method based on neural network |
| CN116935077A (en) * | 2023-07-26 | 2023-10-24 | 湖南视比特机器人有限公司 | Template matching optimization method and system based on encoding and decoding |
| CN116935077B (en) * | 2023-07-26 | 2024-03-26 | 湖南视比特机器人有限公司 | Template matching optimization method and system based on encoding and decoding |
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