CN109242908B - Calibration method for underwater binocular vision measurement system - Google Patents
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Abstract
The invention belongs to the technical field of underwater detection and measurement, and aims to solve the problem that the existing underwater binocular vision measurement system is not general and cannot be applied to occasions with higher precision, so that all underwater detection and measurement requirements cannot be met. Therefore, the invention provides a calibration method for an underwater binocular vision measurement system, which comprises the steps of calibrating a binocular camera in the air to obtain an internal parameter matrix and distortion parameters; shooting an image of the checkerboard by using a binocular camera under water, carrying out distortion correction, detecting to obtain checkerboard angular points, and measuring angular point position coordinates through an underwater refraction relation; and designing an optimization target according to the relative position relation between the angular points of the checkerboards, and completing calibration of the underwater binocular vision measuring system by using multi-objective optimization. The invention can improve the measurement precision of the underwater binocular vision measurement system, so that the measurement precision can meet the requirements of occasions with higher precision, the generality of the underwater binocular vision measurement system is improved, and all underwater detection and measurement requirements can be met.
Description
Technical Field
The invention belongs to the technical field of underwater detection and measurement, and particularly provides a calibration method for an underwater binocular vision measurement system.
Background
As an important branch of ocean exploration technology, underwater robot-based underwater exploration is being widely used. In underwater detection and measurement by using a robot vision technology, because a vision system is mostly protected by a waterproof bin, light rays are refracted twice when entering a camera, and a camera calibration technology in the air cannot be directly applied to calibration of the underwater camera.
Aiming at the influence of refraction on the imaging process of a camera, the solutions to the problem in the prior art can be roughly divided into five types, the first type is a physical method, the refraction phenomenon is counteracted by designing a special optical component, the method has high process requirements on the optical component, and the production and the manufacture are inconvenient; the second type is a method using an auxiliary plane, i.e. a direction vector when light is incident is determined by an auxiliary calibration plate, but the method is relatively complex in operation and is not suitable for popularization and application; the third method regards refraction as focal length change, but when the incident angle of light is large, the error is also large, so that the measurement deviation is large; the fourth type is that the error of underwater refraction is regarded as lens distortion, and the image is corrected, the method continues to use the linear pinhole imaging model, and the error is still large; and the fifth type is to establish a refraction model of the underwater camera to calibrate the underwater camera, and the method has higher accuracy.
In China, a plurality of companies and university researchers have carried out extensive research on underwater camera refraction models, and methods such as an underwater camera calibration method based on multilayer plane refraction geometry and an underwater measurement system calibration based on particle swarm are proposed, for example, in a patent with the patent application number of CN201511019268.0, an underwater camera calibration method is proposed, although the influence of underwater refraction is considered, the method is that a camera imaging plane normal vector is assumed to be parallel to a refraction plane normal vector, and the wall thickness of a waterproof bin is ignored, so that the method has no generality, namely cannot be applied to application occasions with high precision, and cannot meet the requirements of all underwater detection and measurement.
Therefore, there is a need in the art for a new calibration method for an underwater binocular vision measuring system to solve the above problems.
Disclosure of Invention
The underwater binocular vision measuring system aims to solve the problems that the existing underwater binocular vision measuring system is not general and cannot be applied to occasions with high precision, and therefore all underwater detection and measurement requirements cannot be met. The invention provides a calibration method for an underwater binocular vision measurement system, wherein the underwater binocular vision measurement system comprises a first camera positioned on the left side and a second camera positioned on the right side, and the calibration method comprises the following steps:
calibrating the first camera and the second camera in the air to obtain an internal parameter matrix and a distortion coefficient of the first camera and the second camera and a pose transformation matrix between the first camera and the second camera;
shooting the checkerboard image through a first camera and a second camera under water;
distortion correction is carried out on the image of the first camera through the intrinsic parameter matrix of the first camera and the distortion coefficient of the first camera, and distortion correction is carried out on the image of the second camera through the intrinsic parameter matrix of the second camera and the distortion coefficient of the second camera;
obtaining coordinates of the checkerboard angular points on the image of the first camera and coordinates of the checkerboard angular points on the image of the second camera by adopting an angular point detection method;
acquiring a measurement value of a corner position coordinate;
and designing an optimization target in the calibration process by using the relative position relation of the checkerboard corner points of the calibration plate, and calibrating the underwater binocular vision measurement system through multi-objective optimization.
In a preferred technical solution of the above calibration method, before the step of calibrating the first camera and the second camera in the air to obtain an internal parameter matrix, a distortion coefficient, and a pose transformation matrix between the first camera and the second camera, the calibration method further includes:
and setting the estimated parameter values of the first camera and the second camera under water.
In a preferred technical solution of the above calibration method, the step of "obtaining a measurement value of a corner position coordinate" specifically includes:
substituting the estimated parameter values into the following formula, namely:
P＝f(Pl,Pr,R,dl,dr,h,nx,ny,nz)，
a measure of the coordinates of the location of the corner points is obtained, wherein,
p denotes the position of the underwater target point, PlAnd PrRespectively representing pixel points of a target point in the image of the first camera and pixel points in the image of the second camera, R is a pose transformation matrix between the first camera and the second camera, dlAnd drRespectively representing the perpendicular distance from the optical center of the first camera to the refraction plane of the first layer and the second cameraH is the thickness of a waterproof chamber of the underwater binocular vision measuring system, nx，nyAnd nzIs the coordinate component of the normal vector of the refraction plane with the x-axis, the y-axis and the z-axis of the first camera coordinate system as the reference.
The predicted parameter value refers to the pair dl,dr,h,nx,ny,nzAnd (4) estimating.
In the preferred technical scheme of the calibration method, the steps of designing an optimization target of the calibration process by using the relative position relationship of the checkerboard corner points of the calibration plate and calibrating the underwater binocular vision measurement system by multi-objective optimization specifically comprise:
setting a first optimization goal, namely:
min∑(|Cm i,j-Cm i+1,j|+|Cm i,j-Cm i,j+1|-2w)，
wherein the measured value of the angular point position coordinate is Cm i,jI is the horizontal direction serial number of the checkerboard angular points, j is the vertical direction serial number of the checkerboard angular points, and w is the side length of the checkerboard grids;
setting a second optimization goal, namely:
setting a third optimization target, namely:
and optimizing based on the first optimization target, the second optimization target and the third optimization target so as to calibrate the underwater binocular vision measuring system.
In a preferred technical solution of the above calibration method, the internal parameter matrix of the first camera is:
wherein, fxlAnd fylIs the focal length of the first camera, xolAnd yolAre principal point coordinates relative to the imaging plane.
In a preferred technical solution of the above calibration method, the internal parameter matrix of the second camera is:
wherein, fxrAnd fyrIs the focal length of the second camera, xorAnd yorAre principal point coordinates relative to the imaging plane.
It can be understood by those skilled in the art that in a preferred embodiment of the present invention, the first camera and the second camera may be calibrated in the air to obtain the intrinsic parameter matrix and distortion coefficient of the first camera and the second camera, the first camera and the second camera may capture the image of the checkerboard under water, the image of the checkerboard captured by the first camera may be corrected by the intrinsic parameter matrix and distortion coefficient of the first camera, the image of the checkerboard captured by the second camera may be corrected by the intrinsic parameter matrix and distortion coefficient of the second camera, the coordinates of the corner points of the checkerboard on the image of the first camera and the coordinates of the corner points of the checkerboard on the image of the second camera may be obtained by using a corner point detection method, and the underwater binocular vision measuring system may be calibrated by combining the measured values of the coordinates of the positions, in such a way, the underwater binocular vision measuring system has the advantages that the measuring precision of the underwater binocular vision measuring system can be improved, the requirement of occasions with high precision can be met, the generality of the underwater binocular vision measuring system is improved, all underwater detection and measuring requirements can be met, and the underwater binocular vision measuring system can be widely applied to the fields of underwater vision accurate measurement, underwater detection, underwater robot operation and the like.
Furthermore, by setting the first optimization target, the second optimization target and the third optimization target, namely designing the optimization target in the calibration process by using the relative position relation of the checkerboard corner points of the calibration plate, the calibration problem can be converted into a multi-objective optimization problem in such a way, underwater camera parameters are continuously updated in the optimization process, and finally the calibration of the underwater binocular vision measurement system is realized, so that the calibration result of the underwater camera parameters is accurately obtained.
Drawings
FIG. 1 is a schematic view of a refraction model of an underwater camera according to the present invention;
FIG. 2 is a schematic diagram of triangularization-like determination of the position of a target point by the underwater vision measurement system of the present invention;
FIG. 3 is a schematic diagram of a design optimization target for the relative position relationship of checkerboard corner points of the calibration plate of the present invention;
FIG. 4 is a diagram illustrating a calibration effect test result of the underwater vision measurement system of the present invention.
Detailed Description
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that the terms "first" and "second" in the description of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The existing underwater binocular vision measurement system pointed out based on the background technology has no generality and cannot be applied to occasions with high precision, so that the problems of all underwater detection and measurement requirements cannot be met. The invention provides a calibration method for an underwater binocular vision measurement system, and aims to improve the measurement precision of the underwater binocular vision measurement system, so that the underwater binocular vision measurement system can meet the requirements of occasions with higher precision, improve the generality of the underwater binocular vision measurement system, and meet all underwater detection and measurement requirements.
In the invention, because the underwater camera is protected by the waterproof bin, the light from the optical center of the camera to the target point needs to be refracted twice, namely on the connecting surface of the air and the waterproof bin (hereinafter referred to as a first layer refraction plane) and the connecting surface of the waterproof bin and the water (hereinafter referred to as a second layer refraction plane). Therefore, it is necessary to first establish a refraction model of the underwater camera, where the refraction planes are assumed to be parallel to each other.
As shown in fig. 1, O is the optical center of the camera, the bottom surface of the triangle is the camera imaging plane, the camera coordinate system is established with O as the origin, the Z axis is the camera optical axis direction, and the X axis direction is perpendicular to the Z axis direction as shown in fig. 1; the light path from the light center to the target point P is divided into three sections by two refractions, and the direction vectors from the first section to the third section are respectively represented by r0，r1And r2Represents; x0The intersection point of the optical path where the target point is located and the imaging plane is represented, namely the coordinates of the image point in a camera coordinate system can be obtained through the focal length relation of the camera by the image pixel coordinate point; x1And X2Respectively representing the coordinates of the intersection point of the optical path of the target point and the first layer refraction plane and the coordinates of the intersection point of the optical path of the target point and the second layer refraction plane; n isπThe normal vector of the refraction plane is expressed, and is not parallel to the direction of the optical axis in analysis in order to keep generality; d and h respectively represent the vertical distance from the optical center to the first layer refraction plane and the thickness of the waterproof bin, and the unit is millimeter; mu.s0，μ1And mu2Representing the refractive indices of air, water-resistant chamber material and water, respectively.
With continued reference to fig. 1, the direction vector of the first segment of the optical path is obtained according to the following equation (1):
the coordinates of the intersection point of the optical path where the target point is located and the first layer refraction plane are expressed by the following formula (2), namely:
according to the law of refraction, r0，r1And nπSatisfies the following relationship, i.e., formula (3):
r1＝α0r0+β0nπ
wherein alpha is0＝μ0/μ1，
In the same way, X2And r2Can be found. Therefore, the starting point and the direction vector of the third section of the optical path where the target point is located can be known, and the linear equation of the third section of the optical path of the target point can be obtained.
Through one camera, a linear equation of the third section of the optical path where the target point is located can be obtained, but the position of the target point cannot be obtained. As shown in fig. 2, for the same target point, two optical paths will be obtained in the left and right cameras, and the linear equation of the third section of the two optical paths can be obtained by the methods of the above equations (1), (2) and (3). Theoretically, after the two straight lines are converted into the same coordinate system through the left and right camera pose transformation matrixes R, the intersection point coordinates of the two straight lines are the positions of the target points. However, due to the existence of processing errors, the two straight lines may not intersect, so the method of taking the midpoint of the common perpendicular line of the two straight lines as the position of the target point is a triangle-like method.
So far, by establishing a refraction model of the underwater camera, it can be known that the position of the underwater target point satisfies the following nonlinear relationship, namely the following formula (4):
P＝f(Pl,Pr,R,dl,dr,h,nx,ny,nz)
in the above non-linear relationship, P represents the position of the underwater target point, PlAnd PrRespectively representing pixel points of a target point in left and right images, R is a pose transformation matrix between the left and right cameras, dlAnd drRespectively showing the vertical distance from the optical centers of the left camera and the right camera to the first layer of refraction plane, h is the wall thickness of the waterproof bin, and nx，nyAnd nzIs the x, y, z axis coordinate component of the normal vector of the refraction plane referenced to the left camera coordinate system.
To clarify the calculation method of the mathematical relationship of formula (4) in detail, with reference to fig. 2, the following is explained in detail:
the intersection points of the light paths of the left camera and the right camera where the target point P is positioned and the second layer refraction plane are respectively PWG，lAnd PWG，rThe direction vectors of the third section of the optical path of the left and right cameras where the target point P is respectively rlAnd, rr(ii) a At PlAnd PrOn the known premise, the intersection point P can be easily obtained according to the above formulas (1), (2) and (3)WG，lAnd PWG，rAnd a direction vector rlAnd rr(ii) a Taking the left camera optical path as an example, the derivation process is as follows: ,
the coordinates P of the pixel points of the left image are calculated according to the focal length relation of the cameralAnd (3) converting into the coordinates of the left camera coordinate system:
Plc＝focal(Pl)
wherein focal () represents a function of focal length transformation, the specific method being conventional prior art and not described in detail herein; next, the intersection point P of the left camera optical path where the target point P is located and the first layer refraction planeAG，lThe following equation (1) and (2) can be used to obtain:
wherein
According to the formula (3), the direction vector r of the second segment of the optical path1,lCan be obtained by the following formula:
r1,l＝α0,lr0,l+β0,lnπ
wherein alpha is0,l＝μ0/μ1，
Similar to the formulas (1) and (2), the intersection point P of the optical path of the left camera where the target point P is located and the second layer refraction planeWG,lCan be found by the following formula, namely:
wherein n isπ＝(nx,ny,nz)
According to the formula (3), the direction vector r of the third segment of the light pathlCan be obtained by the following formula:
rl＝α1,lr1,l+β1,lnπ
wherein alpha is0,l＝μ1/μ2，
In a similar way, the intersection point of the right camera light path where the target point P is located and the second layer refraction plane and the direction vector of the third section of light path can be obtained;
at this time, the obtained intersection point PWG，lAnd PWG，rAnd a direction vector rlAnd rrAll in their respective camera coordinate systems, should be transformed into the left camera coordinate system by the coordinate transformation matrix of:
so far, knowing a point of a straight line in the space and a direction vector thereof, the equation of the straight line of the third section of the left and right camera optical paths where the target point P is located can be obtained, so that the midpoint of the perpendicular bisector of the two straight lines can also be obtained, and the coordinate of the target point P can be determined; the calculation of the line equation and the perpendicular bisector is conventional prior art and will not be described in detail here.
Therefore, in order to obtain the precise position of the target point, it is necessary to obtain the following accurate parameter sets:
{R,dl,dr,h,nx,ny,nz}
specifically, the underwater binocular vision measuring system of the present invention includes a waterproof bin, and a first camera and a second camera disposed in the waterproof bin, wherein the first camera is located on the left side (i.e., the aforementioned left camera), and the second camera is located on the right side (i.e., the aforementioned right camera), and the calibration method of the present invention includes:
the method comprises the following steps: calibrating the first camera and the second camera in the air to obtain an internal parameter matrix and a distortion coefficient of the first camera and the second camera and a pose transformation matrix between the first camera and the second camera;
the intrinsic parameter matrix of the first camera is:
wherein, fxlAnd fylIs the focal length of the first camera, xolAnd yolAre principal point coordinates relative to the imaging plane.
The intrinsic parameter matrix of the second camera is:
wherein, fxrAnd fyrIs the focal length of the second camera, xorAnd yorAre principal point coordinates relative to the imaging plane.
Step two: shooting the checkerboard image through a first camera and a second camera under water;
step three: distortion correction is carried out on the image of the first camera through the intrinsic parameter matrix of the first camera and the distortion coefficient of the first camera, and distortion correction is carried out on the image of the second camera through the intrinsic parameter matrix of the second camera and the distortion coefficient of the second camera;
step four: obtaining coordinates of the checkerboard angular points on the image of the first camera and coordinates of the checkerboard angular points on the image of the second camera by adopting an angular point detection method;
step five: acquiring a measurement value of a corner position coordinate;
step six: and determining according to the relative relation of the checkerboard angular points, designing three optimization targets, and converting the calibration process into a multi-target optimization problem so as to calibrate the underwater binocular measurement system.
In the invention, before the step one, the { d ] in the underwater camera calibration parameters can be calibrated according to experience and practical conditionsl,dr,h,nx,ny,nzPreliminarily determining the value range of the camera, and giving a reasonable initialization estimated value, namely setting the estimated parameter values of the first camera and the second camera under water. In the estimation process, the vertical distance between the two cameras and the first layer of refraction plane, the thickness of the camera waterproof bin and the direction angle of the normal vector of the refraction plane can be measured through the ruler, and the value range of the underwater camera calibration parameters is preliminarily determined. It should be noted that the estimated parameter value may be an initial estimated value obtained by a person skilled in the art through multiple experiments or according to past experience, and the person skilled in the art may flexibly set the selected mode of the estimated parameter value in practical application.
In the fifth step, the underwater camera parameter estimated value and the camera parameter calibrated in the air are substituted into the formula (4) to obtain the measured value C of the angular point position coordinatesm i,jIn order to calibrate the parameters of the underwater camera, the invention designs an optimization target by using the relative position relationship of the checkerboard angular points. As shown in fig. 3, the relative positional relationship between the corner points of the calibration board checkerboard is known. The distance between two adjacent points is known and is the side length of the checkerboard grid; angular points on the same horizontal line or the same vertical line are combined into vectors which are parallel to each other; the vector formed by combining any two angular points on the same horizontal line is parallel to the vector formed by any two angular points on the same vertical line. Measured value C of angular pointm i,j(i is the horizontal direction serial number of the checkerboard corner points, such as the X direction in fig. 3, and j is the vertical direction serial number of the checkerboard corner points, such as the Y direction in fig. 3), the above three relative position relationships should be satisfied, and accordingly the following three optimization targets are obtained:
the first optimization objective is:
min∑(|Cm i,j-Cm i+1,j|+|Cm i,j-Cm i,j+1|-2w)
wherein w is the side length of the checkerboard grid;
the second optimization objective is:
third optimization objective:
the technical solution of the present invention is further illustrated below with reference to a specific embodiment, a 6 × 9 checkerboard is used as a calibration board, the side length of the checkerboard is 30 mm, and the calibration experiment is performed according to the following steps.
The first step is as follows: according to experience and actual conditions, the value range of the underwater camera parameters is preliminarily determined, and a reasonable initialization estimation value is given.
The second step is that: in the air, calibrating the binocular camera by using a checkerboard to obtain an internal parameter matrix I of the left camera and the right cameraleftAnd IrightDistortion coefficient vector kleftAnd krightAnd a pose transformation matrix R between the left camera and the right camera, wherein the data is as follows:
kleft＝[-0.1504 -0.0114]，kright＝[-0.1573 0.0096]；
in the first three rows of the matrix R, the first three columns are the rotation variation relationship between the first camera and the second camera, and the last column represents the displacement variation relationship between the first camera and the second camera.
The third step: under water, the binocular camera takes the checkerboard image and performs distortion correction.
The fourth step: obtaining coordinates of the checkerboard corner points in the image by using a corner point detection method, wherein the coordinates of the left image corner points are Pl iThe coordinate of the right image corner is Pr i,j(i is the horizontal direction serial number of the checkerboard angular points, and j is the vertical direction serial number of the checkerboard angular points), and the left image and the right image correspond to each other.
The fifth step: substituting the estimated value of the underwater camera parameter into the formula (4) to obtain the measured value C of the angular point position coordinatem i,jI is the horizontal direction serial number of the checkerboard angular points, and j is the vertical direction serial number of the checkerboard angular points.
And a sixth step: and designing an optimization target of the calibration process by using the relative position relation of the checkerboard corner points of the calibration board. The calibration process of the underwater camera parameters is converted into a multi-objective optimization problem, the underwater camera parameters are continuously updated through a multi-objective optimization algorithm, and finally calibration of an underwater binocular measurement system is realized to obtain a calibration result of the underwater camera parameters:
dl＝14.98,dr＝14.56,h＝6.53,nx＝0.0011,ny＝0.0998,nz＝0.9950。
it should be noted that the multi-objective optimization is a mature mathematical calculation process, which is easy to implement on the premise that the optimization objective and the parameter to be optimized are known. Different optimization algorithms can be selected to calibrate the underwater camera parameters. In order to clarify the relation between underwater camera parameter calibration and multi-objective optimization, the calibration process is as follows:
wherein, F1，F2And F3Respectively represent the first optimization goal, the second optimization goal and the third optimization goal. In the multi-objective optimization process, parameters of the underwater camera are continuously optimized, and the relative position between the measured value coordinates of the checkerboard angular points obtained by the formula (4) is gradually close to the relative position relationship between the real value coordinates of the checkerboard angular points, so that effective underwater camera calibration can be realized.
In addition, the invention also carries out a checkerboard position point calculation experiment on the calibrated underwater vision measurement system so as to verify that the calibration method has good measurement performance. The test checkerboard size selected was 8 x 11 with the grid having sides of 25 mm. As shown in fig. 4, the rightmost lattice a is the true value of the checkerboard position, the lattice B in the diagram is the measured value of the underwater measurement system calibrated by the method, the lattice C in the diagram is the measured value of the underwater measurement system calibrated by the conventional calibration method one, and the lattice D in the diagram is the measured value calibrated by the conventional calibration method two, wherein the error of the underwater refraction is regarded as lens distortion by the conventional method one, and the image is corrected; the second conventional method is a binocular vision method in which refraction is not considered and air is directly used. It can be seen that the method of the invention is closer to the true value, the average position error of the corresponding point is only 17.89 mm, and the accuracy is higher.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
Claims (5)
1. A calibration method for an underwater binocular vision measuring system, the underwater binocular vision measuring system comprising a first camera on a left side and a second camera on a right side, the calibration method comprising:
calibrating the first camera and the second camera in the air to obtain an internal parameter matrix and a distortion coefficient of the first camera and the second camera and a pose transformation matrix between the first camera and the second camera;
shooting the checkerboard image through a first camera and a second camera under water;
distortion correction is carried out on the image of the first camera through the intrinsic parameter matrix of the first camera and the distortion coefficient of the first camera, and distortion correction is carried out on the image of the second camera through the intrinsic parameter matrix of the second camera and the distortion coefficient of the second camera;
obtaining coordinates of the checkerboard angular points on the image of the first camera and coordinates of the checkerboard angular points on the image of the second camera by adopting an angular point detection method;
acquiring a measurement value of a corner position coordinate;
the method comprises the following steps of designing an optimization target in a calibration process by using the relative position relation of checkerboard corner points of a calibration plate, and calibrating the underwater binocular vision measurement system through multi-objective optimization, wherein the method comprises the following steps:
setting a first optimization goal:
wherein the measured value of the angular point position coordinate is Cm i,jI is the horizontal direction serial number of the checkerboard angular points, j is the vertical direction serial number of the checkerboard angular points, and w is the side length of the checkerboard grids;
setting a second optimization target:
setting a third optimization target:
and optimizing based on the first optimization target, the second optimization target and the third optimization target so as to calibrate the underwater binocular vision measuring system.
2. The calibration method according to claim 1, wherein before the step of calibrating the first camera and the second camera in the air to obtain the intrinsic parameter matrix, the distortion coefficient and the pose transformation matrix between the first camera and the second camera, the calibration method further comprises:
and setting the estimated parameter values of the first camera and the second camera under water.
3. The calibration method according to claim 2, wherein the step of obtaining the measured value of the coordinates of the corner positions comprises:
substituting the estimated parameter values into equation (1), namely:
P＝f(Pl,Pr,R,dl,dr,h,nx,ny,nz) (1)
a measure of the coordinates of the location of the corner points is obtained, wherein,
p denotes the position of the underwater target point, PlAnd PrRespectively representing pixel points of a target point in the image of the first camera and pixel points in the image of the second camera, R is a pose transformation matrix between the first camera and the second camera, dlAnd drRespectively representing the vertical distance from the optical center of the first camera to the first layer of refraction plane and the vertical distance from the optical center of the second camera to the first layer of refraction plane, h is the thickness of a waterproof cabin of the underwater binocular vision measuring system, nx，nyAnd nzIs the coordinate component of the normal vector of the refraction plane with the x-axis, the y-axis and the z-axis of the first camera coordinate system as the reference.
4. The calibration method according to claim 1, wherein the internal parameter matrix of the first camera is:
wherein, fxlAnd fylIs the focal length of the first camera, xolAnd yolAre principal point coordinates relative to the imaging plane.
5. The calibration method according to claim 1, wherein the internal parameter matrix of the second camera is:
wherein, fxrAnd fyrIs the focal length of the second camera, xorAnd yorAre principal point coordinates relative to the imaging plane.
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| CN111721259B (en) * | 2020-06-24 | 2022-05-03 | 江苏科技大学 | Underwater robot recovery positioning method based on binocular vision |
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| CN113436272B (en) * | 2021-05-21 | 2021-12-21 | 中国科学院自动化研究所 | Spherical refraction correction method for underwater stereoscopic vision system and electronic equipment |
| CN114136544B (en) * | 2021-11-05 | 2023-04-28 | 同济大学 | Underwater vibration simulation test system and method based on high-speed video measurement |
| CN114429431B (en) * | 2022-04-01 | 2022-06-21 | 西南科技大学 | Identification method and system for converting image from underwater to air |
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