CN102622747B - Camera parameter optimization method for vision measurement - Google Patents
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Abstract
The invention belongs to the technical field of measurement and provides a camera parameter optimization method for the vision measurement. The camera parameter optimization method for the vision measurement comprises the following steps of: shooting at least three calibrated images comprising plane targets and extracting image coordinates of feature points in the calibrated images; solving internal parameters and external parameters of a camera, carrying out primary optimization on the parameters and using an optimization result as an initial value of secondary optimization calculation; carrying out distortion correction on the image coordinates of the feature points and calculating an equation of projection rays for connecting a projection center with orthoscopic image feature points; calculating an equation of each target plane in a camera coordinate system and calculating crossing points of the projection rays and the corresponding target planes; and calculating three-dimensional coordinates of each feature point in the camera coordinate system, comparing the three-dimensional coordinates with calculated coordinates of the crossing points and carrying out optimization search on the parameters of the camera by using distances between the three-dimensional coordinates and the calculated coordinates of the crossing points as target functions. In the camera parameter optimization method provided by the invention, the errors of the feature points in the three-dimensional space are used as the target functions; compared with the conventional parameter optimization process, the camera parameter optimization method is closer to the measurement process; and the camera parameter optimization method has higher calibration accuracy and measurement accuracy.
Description
Technical field
The invention belongs to field of measuring technique, relate to a kind of camera parameters optimization method for vision measurement.
Background technology
Camera calibration is important and the key link in the vision measurement, so-called camera calibration just refers to according to camera model, utilize extraction image coordinate and the known world coordinate solving model parameter of unique point, thereby set up the mapping relations of object space and the plane of delineation.The camera model parameter is divided into inner parameter and external parameter usually, and wherein inner parameter is the intrinsic parameter of video camera, can not change because camera position changes, and what external parameter then reflected is the position relationship of camera coordinate system and world coordinate system.
In order to obtain higher stated accuracy, generally need to adopt optimization method to estimate intrinsic parameters of the camera and external parameter.Traditional camera parameters optimization method is according to camera model, with known substance space characteristics spot projection to the plane of delineation, obtain the model image coordinate of unique point, model image coordinate and video camera actual detection to image coordinate have deviation, process by nonlinear optimization is so that this deviation reaches minimum, thereby obtain deviation parameter hour, i.e. the optimum solution of camera parameters.For example: Weng Juyang, the paper of et al. " Camera calibration with distortion models and accuracy evaluation.IEEE Transactions on Pattern Analysis and Machine Intelligence[J] .1992; 14 (10): 965 – 980 " and the paper of Zhang Zhengyou " A flexible new technique for camera calibration.IEEE Transactions on Pattern Analysis and Machine Intelligence[J] .2000; 22 (11): 1330 – 1334 ", above-mentioned two pieces of the thesis be different camera marking methods, but the optimization of camera parameters all is to discuss in two dimensional image plane: according to the initial estimate of camera model and parameter with the coordinate re-projection of unique point in measurement space to the plane of delineation, obtain a series of model pixel coordinates, model pixel coordinate and the real image coordinate that adopts image processing method to obtain are compared one by one, the deviation sum of two kinds of coordinate corresponding point is as the optimization aim function, utilize the model parameter of nonlinear optimization algorithm (for example Levenberg-Marquardt algorithm or Newton-Raphson algorithm) when finding the solution the minimization of object function, i.e. the optimum solution of camera parameters.
Traditional camera parameters optimization method is to be based upon on the two dimensional image plane, and as objective function, search optimum solution getparms iterates with minimizing image pixel distance error.Yet, actual vision measurement flow process is carried out in the three-dimensional body space, for example the most basic distance and measurement of angle all need to be carried out under world coordinate system or camera coordinate system, traditional parameter optimisation procedure and actual measurement flow process coordinate system separately are also inconsistent, can bring degradation problem under the measuring accuracy.
Summary of the invention
Technology of the present invention is dealt with problems and is: overcome the deficiencies in the prior art, a kind of camera parameters optimization method for vision measurement is provided.
Technical solution of the present invention is: a kind of camera parameters optimization method for vision measurement is characterized in that the method includes the steps of:
1, in the field range of video camera, move freely at least 3 positions of target, piece image is taken in position of every movement, is called uncalibrated image, and unique points all on the target should be included in the uncalibrated image; Extract the image coordinate of the unique point in the uncalibrated image, and corresponding with the world coordinates of unique point;
2, utilize the image coordinate of all unique points that step 1 extracts and corresponding world coordinates calibrating camera inner parameter and external parameter, intrinsic parameters of the camera comprises effective focal length, principal point and the distortion factor of video camera; Adopt traditional optimization that camera parameters is carried out first nonlinear optimization, optimum results is as the initial value of the nonlinear optimization calculating second time; Wherein, traditional optimization refers to according to camera parameters the world coordinates re-projection of unique point to the plane of delineation, obtain the model pixel coordinate, as objective function, camera parameters is carried out nonlinear optimization with the error minimum of model pixel coordinate and image coordinate;
3, the distortion of camera parameter of utilizing step 2 to demarcate is carried out distortion correction to the image coordinate of unique point, obtains the orthoscopic image coordinate of unique point;
4, take projection centre as end points, the ray that connects projection centre and orthoscopic image unique point is called projection ray, under camera coordinate system, calculates the equation of projection ray;
5, under camera coordinate system, the target plane equation of calculation procedure 1 described different camera sites, and calculate the intersection point on projection ray and corresponding target plane, intersection point is called the calculated characteristics point;
6, calculate the coordinate of each known features point under camera coordinate system, the coordinate of the calculated characteristics point under the camera coordinate system that itself and step 5 are obtained relatively again carries out nonlinear optimization as objective function to camera parameters with the two distance and minimum.
The described target of step 1 is the plane target drone of glass material, and the target unique point is 10 * 10 lattice points on the plane, and the minor increment between the lattice point is 9mm, and its range accuracy is 0.001mm～0.01mm.
The described nonlinear optimization of step 2 and step 6 all adopts the minimization problem of Levenberg-Marquardt algorithm process objective function.
The present invention's advantage compared with prior art is:
Employing is based on the camera calibration parameter optimization method of three-dimensional measurement coordinate system, camera parameters to first suboptimization carries out double optimization, the measuring error criterion of the more approaching reality of double optimization criterion of parameter has been avoided degradation problem under the inconsistent measuring accuracy of bringing of parameter optimization coordinate system and vision measurement coordinate system.
Description of drawings
Fig. 1 is the math-model of vidicon schematic diagram;
Fig. 2 is the camera parameters optimization method schematic flow sheet that the present invention relates to;
Fig. 3 is the plane target drone schematic diagram that is used for camera calibration that the present invention relates to;
Fig. 4 is the piece image that is used for camera calibration in the embodiment of the invention.
Embodiment
The below is described in further details the present invention.The present invention adopts the parameter optimization method based on the three-dimensional measurement coordinate system that camera parameters is carried out double optimization first, has realized high-precision camera calibration.
Figure 1 shows that the math-model of vidicon schematic diagram.o
c-x
cy
cz
cBe camera coordinate system, o
w-x
wy
wz
wFor being based upon the world coordinate system on the target plane, o
u-x
uy
uBe orthoscopic image coordinate system, o
n-x
ny
nBe the normalized image coordinate system.π
nBe normalized image plane, π
uBe orthoscopic image plane, o
cBe projection centre, o
pStraight line o
cz
cWith π
uIntersection point, be called the principal point of video camera.Definition o
cz
c⊥ π
u) π
n// π
u, o
cx
c//o
ux
u//o
nx
nAnd o
cy
c//o
uy
u//o
ny
nObject point M is at π arbitrarily
uOn be projected as picture point m.The world coordinates of M of setting up an office is M
w=x
w, y
w, z
w)
T, camera coordinates is M
c=(x
c, y
c, z
c)
T, o then
w-x
wy
wz
wTo o
c-x
cy
cz
cBe transformed to
M
c=RM
w+t [1]
R is 3 * 3 quadrature rotation matrix in the formula, and t is 3 * 1 translation vector.
If m
n=(x
n, y
n)
TBe the normalized image coordinate of a m, m
uBe corresponding orthoscopic image pixel coordinate, the effective focal length of video camera on x, y direction is f
xAnd f
y, the principal point coordinate of video camera is u
0, v
0)
T, then have
m
n＝(x
c/z
c，y
c/z
c)
T [2]
m
u=(f
xx
n+u
0，f
yy
n+v
0)
T [3]
If consider camera lens once with the secondary radial distortion, the fault image coordinate of the m that sets up an office is
Then have
K wherein
1, k
2Be coefficient of radial distortion, r is orthoscopic image point m
uTo principal point (u
0, v
0)
TDistance.
Formula [1]～[4] have represented perspective projection model and the distortion model of spatial point to the plane of delineation.Camera parameters comprises two parts: 1. inner parameter: effective focal length f
x, f
y, principal point coordinate (u
O, v
0)
TAnd coefficient of radial distortion k
1And k
2; 2. external parameter: rotation matrix R and translation vector t.According to camera model, any point can be determined unique image projection point in the space.Otherwise, if known intrinsic parameters of the camera, according to formula [4], to direct-detection to picture point at first carry out distortion correction and obtain the ideal diagram picture point, can obtain spatial point according to formula [1]～[3] again and project to coordinate on the normalization plane, thereby can obtain subpoint at camera coordinate system o
c-x
cy
cz
cUnder coordinate.
Figure 2 shows that the camera parameters optimization method schematic flow sheet that the present invention relates to.
Fig. 3 is the plane target drone schematic diagram that is used for camera calibration that the present invention relates to.
Set forth a kind of concrete steps of the camera parameters optimization method for vision measurement below in conjunction with Fig. 1, Fig. 2, Fig. 3:
1, adjusts focal length and the aperture of camera lens, guarantee that video camera can photograph clearly image in measurement range, move freely at least 3 positions of target, position of every movement, take piece image, be called uncalibrated image, all unique points should be included in the photographic images in the target, obtain altogether k width of cloth uncalibrated image (k 〉=3), and extract the coordinate of each unique point in the image.As shown in Figure 3, the target that adopts is the plane target drone of glass material, and the target unique point is 10 * 10 lattice points on the plane, and the minor increment between the lattice point is 9mm.For the ease of equation expression, if every width of cloth image contains n unique point (n 〉=4), unique point image coordinate extracting method is referring to " feature extraction in the pattern-recognition and computer vision invariant " (grandson i.e. auspicious, Wang Xiaohua, kind mountain work, National Defense Industry Press, calendar year 2001).
2, utilize the image coordinate of all unique points to find the solution world coordinate system o in each visual angle with corresponding world coordinates
w-x
wy
wz
wO with image coordinate system
u-x
uy
uBetween homography matrix, homography matrix is expressed as H.Utilize the orthogonality of rotation matrix can decomposing H, solve the inner parameter f of video camera
x, f
y, u
0, v
0, k
1, k
2With external parameter R, t.The specific algorithm of homography matrix and inside and outside parameter is referring to the paper of Zhang Zhengyou " A flexible new technique for camera calibration.IEEE Transactions on Pattern Analysis and Machine Intelligence[J] .2000,22 (11): 1330 – 1334 ".
3, the inside and outside parameter value that obtains according to math-model of vidicon and step 2, with the coordinate re-projection of unique point in the target world coordinate system to the plane of delineation, obtain the width of cloth (i=1 ..., k) of image (j=1 ... n) the model pixel coordinate of unique point
With model pixel coordinate and the real image coordinate that adopts image processing method to obtain
Compare one by one, set up take the unique point re-projection error as minimum objective function:
The initial estimate of inside and outside parameter is provided by step 2, and the distortion factor value is very little, can be with k
1, k
2Initial estimate all be set to zero.Use the Levenberg-Marquardt algorithm that formula [5] is carried out nonlinear optimization, as objective function, search optimum solution getparms iterates with minimizing image pixel distance error.The Levenberg-Marquardt algorithm is referring to " Optimum Theory and method " (Yuan Yaxiang, Sun Wenyu work, Science Press, 1999).
4, the result of just suboptimization of step 3 will optimize the initial value that calculates for the second time as step 4～8.To n * k the fault image unique point that amount to that detects in the width of cloth target image
Carry out distortion correction, obtain the orthoscopic image coordinate m of unique point
u
5, at camera coordinate system o
c-x
cy
cz
cCan obtain the normalization coordinate of projection centre and orthoscopic image unique point: o down,
c, m
cThereby, find the solution take projection centre as end points and connect projection centre and the projection ray equation om of orthoscopic image unique point
c
6, utilize the external parameter R that obtains, t is with the expression π of each target plane equation under world coordinate system
wBe converted to the expression π under the camera coordinate system
c, calculate projection ray equation om
cWith corresponding target plane π
cIntersection point
7, the physical size of plane target drone of the present invention is known quantity, therefore can obtain the world coordinates M of unique point correspondence on different targets planimetric position
wThe external parameter R that utilization is obtained, t is with the unique point coordinate M under the different world coordinate systems
wUnified expression M under camera coordinate system
c
8, the coordinate M that step 7 is calculated
cWith the described intersecting point coordinate of step 6
Relatively, the unique point of establishing width of cloth image is expressed as respectively M
C, i, jWith
At unified measurement coordinate system, namely under the camera coordinate system, M
C, i, jAs supposition actual value, M
C, i, jWith
Mean square deviation as objective function:
Utilize the Levenberg-Marquardt algorithm that formula [6] is carried out the nonlinear optimization second time, to minimize the three dimensions distance error as objective function, the optimum solution of Optimal Recursive search parameter.
Embodiment
Adopt IMPERX-IGV1610M camera and 16mm camera lens to form video camera to be calibrated, image resolution ratio is 1624pixels * 1236pixels.The target that adopts among the embodiment is the plane target drone of glass material, and the target unique point is 10 * 10 lattice points on the plane, and the minor increment between the lattice point is 9mm, and its range accuracy is 0.001mm～0.01mm.
Move freely target to 20 a different position, position of every movement, take piece image, unique points all on the target should be included in the photographic images, obtain altogether 20 width of cloth images (Figure 4 shows that the piece image that is used for demarcation of actual photographed), adopt 15 width of cloth images for the demarcation of camera parameters, obtain first optimum results (classic method) and the double optimization result (the inventive method) (seeing Table) of camera parameters according to the step described in the specific implementation method.Wherein, the evaluation method of stated accuracy is: the minor increment 9mm between the lattice point as gauged distance, is utilized the parameter result demarcated, calculate three-dimensional distance between the adjacent feature point of 15 width of cloth uncalibrated images and the root-mean-square error of gauged distance.The evaluation method of measuring accuracy is: adopt and have neither part nor lot in 5 width of cloth images of demarcation as test pattern, use the demarcation numerical value of parameter, calculate the target plane pose parameter of every width of cloth test pattern, again with the anti-target planar shaped commercial base that projects of the image characteristic point of every width of cloth test pattern, calculate the root-mean-square error (seeing Table two) of the distance between itself and all space characteristics points.
Table one
Table two
Can find out from the data of table one and table two, adopt traditional parameter optimization method to finish camera calibration, the stated accuracy that reaches and measuring accuracy are respectively 0.0400mm and 0.0503mm, and the camera parameters that adopts optimization method of the present invention to obtain, its stated accuracy and measuring accuracy are respectively 0.0385mm and 0.0454mm, than classic method in various degree raising are arranged all.
The above is preferred embodiment of the present invention only, is not for limiting protection scope of the present invention.
Claims (3)
1. camera parameters optimization method that is used for vision measurement is characterized in that the concrete steps of the method are as follows:
1.1, in the field range of video camera, move freely at least 3 positions of target, piece image is taken in position of every movement, is called uncalibrated image, unique points all on the target should be included in the uncalibrated image; Extract the image coordinate of the unique point in the uncalibrated image, and corresponding with the world coordinates of unique point;
1.2, utilize the image coordinate of all unique points that step 1.1 extracts and corresponding world coordinates calibrating camera inner parameter and external parameter, intrinsic parameters of the camera comprises effective focal length, principal point and the distortion factor of video camera; Adopt traditional optimization that camera parameters is carried out first nonlinear optimization, optimum results is as the initial value of the nonlinear optimization calculating second time; Wherein, traditional optimization refers to according to camera parameters the world coordinates re-projection of unique point to the plane of delineation, obtain the model pixel coordinate, as objective function, camera parameters is carried out nonlinear optimization with the error minimum of model pixel coordinate and image coordinate;
1.3, the distortion of camera coefficient that utilizes step 1.2 to demarcate carries out distortion correction to the image coordinate of unique point, obtains the orthoscopic image coordinate of unique point;
1.4, take projection centre as end points, the ray that connects projection centre and orthoscopic image unique point is called projection ray, under camera coordinate system, the equation of calculating projection ray;
1.5, under camera coordinate system, the target plane equation of calculation procedure 1.1 described different camera sites, and calculate the intersection point on projection ray and corresponding target plane is called the calculated characteristics point with intersection point;
1.6, calculate the coordinate of each known features point under camera coordinate system, the coordinate of the calculated characteristics point under the camera coordinate system that itself and step 1.5 are obtained relatively again carries out nonlinear optimization as objective function to camera parameters with the two distance and minimum.
2. a kind of camera parameters optimization method for vision measurement according to claim 1, it is characterized in that: the described target of step 1.1: be the plane target drone of glass material, the target unique point is 10 * 10 lattice points on the plane, minor increment between the lattice point is 9mm, and its range accuracy is 0.001mm～0.01mm.
3. a kind of camera parameters optimization method for vision measurement according to claim 1 is characterized in that: step 1.2 and 1.6 described nonlinear optimizations: the minimization problem that adopts Levenberg-Marquardt optimized algorithm processing target function.
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| EP3200148B1 (en) * | 2014-10-31 | 2019-08-28 | Huawei Technologies Co., Ltd. | Image processing method and device |
| CN105046704A (en) * | 2015-07-13 | 2015-11-11 | 河海大学 | Vision measurement calibration method based on circular target array and observation equation |
| WO2018119771A1 (en) * | 2016-12-28 | 2018-07-05 | 深圳大学 | Efficient phase-three-dimensional mapping method and system based on fringe projection profilometry |
| CN106767533B (en) * | 2016-12-28 | 2019-07-05 | 深圳大学 | Efficient phase-three-dimensional mapping method and system based on fringe projection technology of profiling |
| CN108269289B (en) * | 2018-01-16 | 2021-08-10 | 上海汇像信息技术有限公司 | Two-step optimization method for calibrating camera parameters |
| CN109544642B (en) * | 2018-11-16 | 2023-05-09 | 北京理工大学 | N-type target-based TDI-CCD camera parameter calibration method |
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| CN110956667B (en) * | 2019-11-28 | 2023-02-17 | 李安澜 | Camera self-calibration method and system based on approximate plane target |
| CN111640181A (en) * | 2020-05-14 | 2020-09-08 | 佳都新太科技股份有限公司 | Interactive video projection method, device, equipment and storage medium |
| CN112001880B (en) * | 2020-06-29 | 2024-01-05 | 浙江大学 | Method and device for detecting characteristic parameters of planar member |
| CN112634373B (en) * | 2020-12-01 | 2023-08-11 | 北京航天计量测试技术研究所 | Visual measurement system dynamic correction method based on zero-expansion ceramic calibration plate |
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