CN111415391B - External azimuth parameter calibration method for multi-camera by adopting mutual shooting method - Google Patents
External azimuth parameter calibration method for multi-camera by adopting mutual shooting method Download PDFInfo
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Abstract
A method for calibrating external azimuth parameters of a multi-camera by adopting a mutual shooting method. The method comprises the steps of establishing a calibration system of the multi-camera space measurement system; calibrating parameters in a monocular camera; the auxiliary camera is combined with the measurement camera to calibrate external parameters of the binocular vision system; calculating the pose of the target under an auxiliary camera coordinate system; calculating the pose of the target under the self-measuring camera coordinate system; calibrating external parameters of mutual shooting; and rotating the measuring camera to the working posture and measuring. The external azimuth parameter calibration method of the multi-camera by adopting the mutual shooting method can quickly and accurately calibrate the external parameters between each measuring camera in the multi-camera measuring system, and can flexibly adapt to various working environments, so that the measuring work can be efficiently carried out. In addition, the external parameters between cameras can be calibrated rapidly, accurate parameters are provided for subsequent measurement work, the working efficiency and the precision can be improved, and the measurement cost is reduced.
Description
Technical Field
The invention belongs to the technical field of machine vision three-dimensional space coordinate measurement, and particularly relates to a method for calibrating external azimuth parameters of a multi-camera by adopting a mutual shooting method.
Background
With the continuous development of fields such as machine manufacturing, aerospace, robots and the like in China, particularly the development of the production technology level of domestic large aircrafts, the requirement on the space measurement precision in the manufacturing and assembling process is also continuously improved. The accuracy of the measurement technique directly determines the accuracy of industrial manufacturing and assembly. The traditional three-coordinate measuring machine has overlarge volume and lower calculation efficiency, and the traditional measuring method often needs to contact with the measured object, so that the measured object is easily damaged, and the precision and efficiency requirements of the modern industry on a measuring system can not be met.
Along with the improvement of hardware level, the calculation speed of the computer is also improved rapidly, so that the computer vision and photogrammetry technology can be widely applied to various industries, the working efficiency and precision of the measurement system are greatly improved, and the labor cost and the time cost are reduced. In the field of three-coordinate measurement of computer vision space, the multi-camera vision measurement system is widely applied by virtue of the advantages of high measurement precision, strong adaptability, high measurement efficiency, low cost and the like. Compared with the traditional three-coordinate measuring method, the method has the advantages of high precision, zero loss on the measured object and the like. Therefore, it is of great practical importance to study multi-camera vision measurement systems.
In the multi-camera measurement system, the mainly adopted measurement method is a triangulation algorithm, in which the calibration accuracy of the internal and external parameters of the camera can directly influence the measurement accuracy of the space coordinates, and the internal parameters of the camera can be accurately calibrated by using mature calibration tools such as a Zhang's calibration method. However, as the working scene changes, the parameters between cameras in the system change, and the conventional binocular vision external parameter calibration method cannot be used for calibrating by means of a checkerboard because of the limitation of the working environment. Therefore, the research on a quick, accurate and flexible external parameter calibration method of the camera is a serious problem in the development of a multi-camera space three-coordinate measurement system.
Disclosure of Invention
In order to solve the problems, the invention aims to provide a calibration method for external azimuth parameters of a multi-camera by adopting a mutual shooting method.
In order to achieve the above object, the method for calibrating external azimuth parameters of a multi-camera by using a mutual shooting method provided by the invention comprises the following steps in sequence:
step 1) establishing a calibration system of the multi-camera space measurement system: the system comprises a first measuring camera, a second measuring camera, a target, an auxiliary camera and a precise rotating platform; the lower ends of the first measuring camera, the second measuring camera and the auxiliary camera are respectively provided with a precise rotary table, and the three precise rotary tables are arranged in a triangle; a target is respectively arranged on the first measuring camera and the second measuring camera;
step 2) calibrating parameters in the monocular camera: respectively establishing mathematical models of a first measuring camera, a second measuring camera and an auxiliary camera according to the small-hole imaging model; calibrating an internal reference matrix and distortion parameters of the first measuring camera, the second measuring camera and the auxiliary camera by means of a checkerboard calibration plate according to a homography matrix mapping principle and a nonlinear optimization principle;
step 3) auxiliary cameras are combined with a measurement camera to calibrate external parameters of the binocular vision system: the first measuring camera and the second measuring camera respectively form a binocular vision system with the auxiliary camera, and then the external parameter matrixes of the two binocular vision systems are calibrated according to the binocular vision external parameter calibration principle;
step 4) calculating the pose of the target under an auxiliary camera coordinate system: rotating the auxiliary camera, and obtaining an external parameter matrix between the rotated auxiliary camera and the first or second measuring camera by using the external parameter matrix of the calibrated binocular vision system: shooting a target by using an auxiliary camera, and calculating the accurate pose of the target under an auxiliary camera coordinate system;
step 5) calculating the pose of the target under the self-measuring camera coordinate system: calculating the pose of the target under the coordinate system of the self-measuring camera through simple coordinate conversion by utilizing the external parameter matrix between the rotated auxiliary camera and the first measuring camera or the second measuring camera obtained in the step 4) and the calculated pose of the target under the coordinate system of the auxiliary camera;
and 6) calibrating external parameters of mutual shooting: shooting targets on the opposite side measuring cameras by using the two measuring cameras, calculating the pose of the opposite side targets, and calculating the current external parameters of the two measuring cameras through conversion;
and 7) rotating the measuring camera to a working posture by using the precision rotating table and measuring.
In the step 2), the parameter calibration in the monocular camera is realized by a calibration tool box in Matlab or a calibration function in OpenCV.
In step 3), the method for calibrating the external parameters of the binocular vision system by the auxiliary camera combined measurement camera comprises the following steps: firstly, a binocular vision system is formed by a first measuring camera and a second measuring camera and an auxiliary camera respectively, then, the first measuring camera and the auxiliary camera as well as the second measuring camera and the auxiliary camera are utilized to shoot the checkerboard calibration plate at the same time, space feature point matching is carried out after shooting, an essential matrix is calculated after paired space feature points are obtained, and a rotation matrix R and a translation vector T in an external parameter matrix can be decomposed through the essential matrix.
In step 4), the method for calculating the pose of the target under the auxiliary camera coordinate system is as follows: the method comprises the steps of rotating an auxiliary camera to be opposite to a target on a first measuring camera or a second measuring camera by using a precise rotating table, then imaging the target by using the auxiliary camera, obtaining a pixel coordinate of the target after obtaining an image, wherein the world coordinate of the target is known, so that the pose of the target under an auxiliary camera coordinate system can be obtained by using the association between the world coordinate of the target and the pixel coordinate.
In step 5), the method for calculating the pose of the target under the self-measurement camera coordinate system is as follows: multiplying the external parameter matrix between the rotated auxiliary camera obtained in the step 4) and the first measuring camera or the second measuring camera by the calculated pose of the target under the auxiliary camera coordinate system, namely, the coordinate of the target under the self measuring camera coordinate system can be obtained.
In step 6), the method for calibrating the external parameters of mutual shooting is as follows: the pose of the target under the coordinate system of the self-measuring camera is calculated through the step 5), the target on the second measuring camera is shot by utilizing the first measuring camera, or the target on the first measuring camera is shot by utilizing the second measuring camera, then the pose of the opposite target is calculated by utilizing a PnP algorithm, and the current external parameters of the two measuring cameras are calculated through conversion.
In step 7), the method for rotating the measuring camera to the working posture and measuring by using the precision rotating table is as follows: before the measurement work is carried out, the first measurement camera and the second measurement camera are rotated to an angle opposite to the measured object; the rotation angles of the first measuring camera and the second measuring camera from the calibration posture to the working posture are recorded by using the precise rotating table, and the external parameters of the two measuring cameras in the working posture are obtained through calculation, so that the measuring work can be carried out.
The external azimuth parameter calibration method of the multi-camera by adopting the mutual shooting method can quickly and accurately calibrate the external parameters between each measuring camera in the multi-camera measuring system, and can flexibly adapt to various working environments, so that the measuring work can be efficiently carried out. In addition, the external parameters between cameras can be calibrated rapidly, accurate parameters are provided for subsequent measurement work, the working efficiency and the precision can be improved, and the measurement cost is reduced.
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FIG. 1 is a flow chart of a method for calibrating external azimuth parameters of a multi-camera by using a mutual shooting method.
FIG. 2 is a schematic diagram of the external parameter calibration process of the binocular vision system in the present invention.
Fig. 3 is a schematic diagram of coordinate transformation in the present invention.
Detailed Description
The external azimuth parameter calibration method of the multi-camera adopting the mutual shooting method provided by the invention is described in detail below with reference to the accompanying drawings and specific embodiments. The drawings are for reference and description only and are not intended to limit the scope of the invention.
As shown in fig. 1, 2 and 3, the method for calibrating external azimuth parameters of a multi-camera by adopting a mutual shooting method provided by the invention comprises the following steps in sequence:
step 1) establishing a calibration system of the multi-camera space measurement system: the system comprises a first measuring camera 1, a second measuring camera 2, a target L, an auxiliary camera 3 and a precision rotating table 4; wherein, the lower ends of the first measuring camera 1, the second measuring camera 2 and the auxiliary camera 3 are respectively provided with a precise rotary table 4, and the three precise rotary tables 4 are arranged in a triangle; a target L is respectively arranged on the first measuring camera 1 and the second measuring camera 2; the first measuring camera 1 and the second measuring camera 2 are image acquisition devices; the target L is a mutual shooting calibration auxiliary tool for realizing rapid external parameter calibration before measurement work; the auxiliary camera 3 is used to calculate the coordinates of each target L in the measurement camera coordinate system; the precision rotary table 4 is used for rotating the camera thereon to a proper measuring angle;
step 2) calibrating parameters in the monocular camera: respectively establishing mathematical models of the first measuring camera 1, the second measuring camera 2 and the auxiliary camera 3 according to the small-hole imaging model; calibrating an internal reference matrix and distortion parameters of the first measuring camera 1, the second measuring camera 2 and the auxiliary camera 3 by means of a checkerboard calibration plate according to a homography matrix mapping principle and a nonlinear optimization principle;
and the parameter calibration in the monocular camera is realized by a calibration tool box in Matlab or a calibration function in OpenCV.
Step 3) auxiliary cameras are combined with a measurement camera to calibrate external parameters of the binocular vision system: the first measuring camera 1, the second measuring camera 2 and the auxiliary camera 3 respectively form a binocular vision system, and then the external parameter matrixes of the two binocular vision systems are calibrated according to the binocular vision external parameter calibration principle;
FIG. 2 is a schematic diagram of the external parameter calibration process of the binocular vision system in the present invention. As shown in FIG. 2, O L X L Y L Z L For measuring the camera coordinate system, the imaging plane coordinate system is o l x l y l The optical axis direction is Z L The method comprises the steps of carrying out a first treatment on the surface of the Similarly, O R X R Y R Z R For auxiliary camera coordinate system, the imaging plane coordinate system is o R x R y R The optical axis direction is Z R ；P L ，P R Respectively the pixel coordinates of the space feature points at the imaging points of the image surfaces of the measuring camera and the auxiliary camera, and the intersection point P of two rays in the figure W Namely, the space feature point is in the world coordinate system X W Y W Z W The coordinates below. The coordinate conversion relation between any two coordinate systems is as follows:
wherein,,
representing a coordinate system O R X R Y R To the coordinate system O L X L Y L Is a rotation matrix of (a); />
T＝(t 1 t 2 t 3 ) T A translation vector corresponding to the rotation matrix R is represented.
In the measuring camera coordinate system O in FIG. 3 1 X 1 Y 1 Z 1 And an auxiliary camera coordinate system O 3 X 3 Y 3 Z 3 For example, the coordinate conversion relationship is:
wherein,,
representing a coordinate system O 1 X 1 Y 1 To the coordinate system O 3 X 3 Y 3 Is a rotation matrix of (a);
T＝(t 1 t 2 t 3 ) T a translation vector corresponding to the rotation matrix R is represented.
The rotation matrix is calibrated. The external parameter calibration of the binocular vision system is the process of solving the rotation matrix R and the translation vector T. The external matrix of the two binocular vision systems is R in figure 3 13 T 13 And R is 23 T 23 。
Step 4) calculating the pose of the target under an auxiliary camera coordinate system: rotating the auxiliary camera, and obtaining an external parameter matrix between the rotated auxiliary camera and the first or second measuring camera by using the external parameter matrix of the calibrated binocular vision system: shooting a target by using an auxiliary camera, and calculating the accurate pose of the target under an auxiliary camera coordinate system;
in step 3), after the rotation matrix R and the translation vector T are obtained, the auxiliary camera 3 is rotated by a certain angle by using the precision rotation table 4 toSo that it can shoot the target L fixed on the first measuring camera 1 or the second measuring camera 2, the rotation of the auxiliary camera 3 is performed around the Z axis of the measuring camera coordinate system, the rotation angle theta and the rotation matrix R z The conversion relationship of (θ) is:
the rotation matrix R marked in the step 3) is rotated by the precision rotating table 4 together with the translation vector T and the rotation matrix R obtained by rotating the precision rotating table z And (theta) multiplying to obtain the external parameter matrix between the current auxiliary camera 3 and the first measuring camera 1 or the second measuring camera 2 after rotating.
Rotating the precise rotating table 4 under the auxiliary camera 3 until the auxiliary camera 3 can shoot the target L on the first measuring camera 1 or the second measuring camera 2, shooting the target L, calculating the pose of the target L under the auxiliary camera coordinate system according to a PnP algorithm, and optimizing the re-projection error of the characteristic points on the target by using a nonlinear optimization algorithm so as to obtain the precise pose of the target L under the auxiliary camera coordinate system;
because the world coordinates of the feature points on the target L are known, the pixel coordinates on the imaging plane can be obtained from the image. Shooting the target L by using the auxiliary camera 3 is to prepare for solving the pose of the target L under the coordinate system of the self-measuring camera;
the principle of the PnP algorithm is as follows:
PNP (Perspotive-N-Points) is an algorithm for solving the pose of a camera according to 3D to 2D point pairs, and describes how to solve the pose of the camera when N pairs of space and image matching Points exist.
The target L has N threeThe dimensional space feature point P is provided with N projection points P on an imaging plane, and the pose R and T of the target L need to be calculated, and the lie algebra of the pose R and T is denoted as xi. Let the coordinates of the spatial coordinate point on the target L be P i ＝[X i Y i Z i ] T The corresponding pixel coordinate on the imaging plane is U i ＝[u i v i ] T The positional relationship between the pixel position and the spatial feature point is as follows:
wherein K is an internal reference matrix of the camera marked and obtained in the step 1), xi is the pose of a target L represented by a lie algebra, and the pose is written into a matrix form of s i U i ＝Kexp(ξ^)P i Because of factors such as pose of the target L, imaging noise and the like, errors exist in the equation, all the errors are added up to form a nonlinear least square problem, and the accurate pose can be obtained by iterative solution:
the error term in the PnP problem is an error obtained by comparing the observed pixel coordinates with the position of the 3D point projected onto the imaging plane according to the currently estimated pose, and is called a reprojection error. There are many methods for solving the nonlinear least squares problem, such as first-order steepest descent, second-order gaussian newton, and linear optimization algorithms including levenberg marquark.
Step 5) calculating the pose of the target under the self-measuring camera coordinate system: calculating the pose of the target under the coordinate system of the self-measuring camera through simple coordinate conversion by utilizing the external parameter matrix between the rotated auxiliary camera and the first measuring camera or the second measuring camera obtained in the step 4) and the calculated pose of the target under the coordinate system of the auxiliary camera;
because the target L is fixedly arranged on the first measuring camera 1 and the second measuring camera 2, i.e. the target L is under the coordinate system of the measuring cameraThe pose of the calculation target is unchanged, and a schematic diagram of the pose of the calculation target under the self-measurement camera coordinate system is shown in fig. 3. The external parameter matrix R of two binocular vision systems can be known through the external parameter calibration of the step 3) 13 T 13 And R is 23 T 23 Is known and the external matrix and target L between the rotated auxiliary camera 3 and the first measuring camera 1 or the second measuring camera 2 have been obtained in step 4) in the auxiliary camera coordinate system O 3 X 3 Y 3 Z 3 The exact pose of the first measuring camera 1 can be determined by simple coordinate transformation 1 X 1 Y 1 Z 1 Measurement camera coordinate system and target L on second measurement camera 2 at O 2 X 2 Y 2 Z 2 And measuring the pose under the coordinate system of the camera.
And 6) calibrating external parameters of mutual shooting: shooting targets on the opposite side measuring cameras by using the two measuring cameras, calculating the pose of the opposite side targets, and calculating the current external parameters of the two measuring cameras through conversion;
the pose of the target L under the coordinate system of the self-measuring camera is calculated through the step 5), the target L on the second measuring camera 2 is shot by using the first measuring camera 1, or the target L on the first measuring camera 1 is shot by using the second measuring camera 2, then the pose of the opposite target L is calculated by using a PnP algorithm, and the current external parameters of the two measuring cameras can be calculated through conversion;
step 7) rotating the measuring camera to a working posture by using a precision rotating table and measuring:
in step 6) the first measuring camera 1 and the second measuring camera 2 are opposite, but both measuring cameras need to face the object to be measured when the measuring work is performed, and therefore both measuring cameras need to be rotated to an angle facing the object to be measured before the measuring work is performed. The rotation angles of the first measuring camera 1 and the second measuring camera 2 from the calibration posture to the working posture are recorded by using the precise rotating table 3, and the external parameters of the two measuring cameras in the working posture can be obtained through calculation, so that the measuring work can be carried out.
After rotation, the rotation matrix in the external parameters of the two measurement cameras changes again, the rotation angle of the precision rotary table 3 is required to be converted into the rotation matrix, the external parameter matrix is updated, and the relation between the rotation angle and the rotation matrix is as follows:
if the rotation angle is θ and the rotation axes are X, Y and Z axes, respectively, the rotation matrices are:
the above description of the embodiments of the invention has been presented in connection with the drawings but these descriptions should not be construed as limiting the scope of the invention, which is defined by the appended claims, and any changes based on the claims are intended to be covered by the invention.
Claims (7)
1. A method for calibrating external azimuth parameters of a multi-camera by adopting a mutual shooting method is characterized by comprising the following steps of: the external azimuth parameter calibration method of the multi-camera adopting the mutual shooting method comprises the following steps of:
step 1) establishing a calibration system of the multi-camera space measurement system: the system comprises a first measuring camera (1), a second measuring camera (2), a target (L), an auxiliary camera (3) and a precision rotating table (4); the lower ends of the first measuring camera (1), the second measuring camera (2) and the auxiliary camera (3) are respectively provided with a precise rotary table (4), and the three precise rotary tables (4) are arranged in a triangle; a target (L) is respectively arranged on the first measuring camera (1) and the second measuring camera (2);
step 2) calibrating parameters in the monocular camera: respectively establishing mathematical models of a first measuring camera (1), a second measuring camera (2) and an auxiliary camera (3) according to the small-hole imaging model; calibrating an internal reference matrix and distortion parameters of the first measuring camera (1), the second measuring camera (2) and the auxiliary camera (3) by means of a checkerboard calibration plate according to a homography matrix mapping principle and a nonlinear optimization principle;
step 3) auxiliary cameras are combined with a measurement camera to calibrate external parameters of the binocular vision system: a binocular vision system is formed by a first measuring camera (1), a second measuring camera (2) and an auxiliary camera (3), and then external parameter matrixes of the two binocular vision systems are calibrated according to a binocular vision external parameter calibration principle;
step 4) calculating the pose of the target under an auxiliary camera coordinate system: rotating the auxiliary camera, and obtaining an external parameter matrix between the rotated auxiliary camera and the first or second measuring camera by using the external parameter matrix of the calibrated binocular vision system: shooting a target by using an auxiliary camera, and calculating the accurate pose of the target under an auxiliary camera coordinate system;
step 5) calculating the pose of the target under the self-measuring camera coordinate system: calculating the pose of the target under the coordinate system of the self-measuring camera through simple coordinate conversion by utilizing the external parameter matrix between the rotated auxiliary camera and the first measuring camera or the second measuring camera obtained in the step 4) and the calculated pose of the target under the coordinate system of the auxiliary camera;
and 6) calibrating external parameters of mutual shooting: shooting targets on the opposite side measuring cameras by using the two measuring cameras, calculating the pose of the opposite side targets, and calculating the current external parameters of the two measuring cameras through conversion;
and 7) rotating the measuring camera to a working posture by using the precision rotating table and measuring.
2. The method for calibrating external azimuth parameters of a multi-camera by adopting a mutual shooting method according to claim 1, wherein the method is characterized in that: in the step 2), the parameter calibration in the monocular camera is realized by a calibration tool box in Matlab or a calibration function in OpenCV.
3. The method for calibrating external azimuth parameters of a multi-camera by adopting a mutual shooting method according to claim 1, wherein the method is characterized in that: in step 3), the method for calibrating the external parameters of the binocular vision system by the auxiliary camera combined measurement camera comprises the following steps: firstly, a binocular vision system is formed by a first measuring camera (1) and a second measuring camera (2) and an auxiliary camera (3), then, shooting is carried out on a checkerboard calibration plate by utilizing the first measuring camera (1) and the auxiliary camera (3) as well as the second measuring camera (2) and the auxiliary camera (3), space feature point matching is carried out after shooting, an essential matrix is calculated after paired space feature points are obtained, and a rotation matrix R and a translation vector T in an external parameter matrix can be decomposed through the essential matrix.
4. The method for calibrating external azimuth parameters of a multi-camera by adopting a mutual shooting method according to claim 1, wherein the method is characterized in that: in step 4), the method for calculating the pose of the target under the auxiliary camera coordinate system is as follows: firstly, a precision rotary table (4) is used for rotating an auxiliary camera (3) to be opposite to a target (L) on a first measuring camera (1) or a second measuring camera (2), then the auxiliary camera (3) is used for imaging the target (L), after an image is obtained, the pixel coordinates of the target (L) can be obtained, and the world coordinates of the target (L) are known, so that the pose of the target (L) under an auxiliary camera coordinate system can be obtained by using the correlation between the world coordinates of the target (L) and the pixel coordinates.
5. The method for calibrating external azimuth parameters of a multi-camera by adopting a mutual shooting method according to claim 1, wherein the method is characterized in that: in step 5), the method for calculating the pose of the target under the self-measurement camera coordinate system is as follows: and (3) multiplying the external parameter matrix between the rotated auxiliary camera (3) obtained in the step (4) and the first measuring camera (1) or the second measuring camera (2) by the calculated pose of the target (L) under the auxiliary camera coordinate system, namely, the coordinate of the target (L) under the self measuring camera coordinate system can be obtained.
6. The method for calibrating external azimuth parameters of a multi-camera by adopting a mutual shooting method according to claim 1, wherein the method is characterized in that: in step 6), the method for calibrating the external parameters of mutual shooting is as follows: the pose of the target (L) under the coordinate system of the self-measuring camera is calculated through the step 5), the target (L) on the second measuring camera (2) is shot by utilizing the first measuring camera (1), or the target (L) on the first measuring camera (1) is shot by utilizing the second measuring camera (2), then the pose of Fang Babiao (L) is calculated by utilizing the PnP algorithm, and the current external parameters of the two measuring cameras are calculated through conversion.
7. The method for calibrating external azimuth parameters of a multi-camera by adopting a mutual shooting method according to claim 1, wherein the method is characterized in that: in step 7), the method for rotating the measuring camera to the working posture and measuring by using the precision rotating table is as follows: before measuring work, the first measuring camera (1) and the second measuring camera (2) are rotated to an angle opposite to the measured object; the precise rotary table (3) is used for recording the rotation angles of the first measuring camera (1) and the second measuring camera (2) from the calibration posture to the working posture, and the external parameters of the two measuring cameras in the working posture are obtained through calculation, so that the measuring work can be carried out.
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