Calibration method and device for background shadow tomography measurement system

Abstract

The application provides a calibration method and device of a background schlieren tomography system. The method comprises the following steps: acquiring a set of camera pictures of a calibration board and corresponding center feature point pixel positions of the calibration board collected by a plurality of cameras with a common field of view, and acquiring background pictures and corresponding angle feature point pixel positions collected by the plurality of cameras; determining camera intrinsic parameters of the plurality of cameras; determining relative extrinsic parameters of any two cameras; obtaining absolute extrinsic parameters of the plurality of cameras based on absolute extrinsic parameter calibration sequence and camera absolute extrinsic parameter algorithm; determining positions of corresponding background pictures in a unified camera coordinate system by using a background position calibration algorithm; obtaining camera extrinsic parameters of a part of the plurality of cameras and camera extrinsic parameters of another part of the plurality of cameras after rotation, and determining an initial center position of a flow field to be measured. The method can effectively improve calibration efficiency and robustness of the background schlieren tomography system.

Description

Technical Field

[0001] This application relates to the field of calibration technology for background schlieren tomography measurement systems, and more specifically, to a calibration method and apparatus for a background schlieren tomography measurement system. Background Technology

[0002] Background schlieren tomography system uses a combination of multiple cameras and backgrounds to measure the three-dimensional refractive index field within the intersection of the fields of view. Its calibration process involves determining the spatial positions of the camera, background, and flow field region in a unified coordinate system. The calibration results determine the accuracy of the flow field reconstruction.

[0003] However, existing calibration methods for background schlieren tomography systems, which involve simultaneous calibration of camera intrinsic and extrinsic parameters, are prone to causing ill-posed solutions. Their calibration efficiency and robustness are easily affected by the quantity and quality of the measurement samples. Furthermore, since the cameras in background schlieren tomography systems are focused on various background locations, their common field-of-view intersection areas are out of focus, and there is a lack of calibration methods for the spatial positions of out-of-focus cameras. Summary of the Invention

[0004] The purpose of this application is to provide a calibration method and apparatus for a background schlieren tomography measurement system. The calibration method adopts the approach of first theoretically determining the intrinsic parameters of a single camera and then solving to determine the extrinsic parameters of the camera. This can effectively improve the efficiency and robustness of the intrinsic parameter calibration. In response to the problem that the calibration plate is in a defocused state when calibrating the camera in pairs in background schlieren measurement, it was found that compared with the feature points of the checkerboard calibration pattern, the feature points of the circular asymmetric array pattern are not affected even if the outer circle is blurred. Therefore, it is more suitable for calibrating the relative extrinsic parameters of the camera in a defocused state.

[0005] A first aspect provides a calibration method for a background schlieren tomography measurement system, the background schlieren tomography measurement system comprising multiple cameras, background plates corresponding to each camera, and a flow field device for generating a flow field to be measured; wherein each background plate is positioned within the focusing range of the corresponding camera, each background plate is provided with a calibration plate having a checkerboard pattern, each camera is positioned around the flow field device, and the flow field device is positioned within the common field of view of all cameras when they are out of focus, the method comprising:

[0006] The calibration board images and corresponding center feature point pixel positions of the calibration board are acquired by a group of cameras with a common field of view of the flow field device, respectively. The background pattern with checkerboard markings and the corner feature point pixel positions of the checkerboard in the corresponding background pattern are also acquired by each camera. The calibration board of the circular asymmetric array pattern is set within the range where each camera is in defocus.

[0007] The camera performance parameters are processed to determine the camera intrinsic parameters of each camera;

[0008] If the set of cameras consists of any two cameras, then the MATLAB algorithm is used to process the intrinsic parameters of the cameras and the pixel positions of the feature points at the center of the calibration board of any two cameras to obtain the relative extrinsic parameters of the cameras.

[0009] The absolute extrinsic parameter calibration order among the multiple cameras is determined using the camera coordinate system of the target camera as a unified camera coordinate system. The absolute extrinsic parameter algorithm is used to calculate the relative extrinsic parameters of any two cameras in sequence according to the absolute extrinsic parameter calibration order, so as to obtain the absolute extrinsic parameters of each camera in the unified camera coordinate system. The target camera is any one of the multiple cameras.

[0010] A background position calibration algorithm is used to calculate the spatial position of the corner feature point pixel position of each camera in the corresponding camera coordinate system and the absolute extrinsic parameters of the corresponding camera in the unified camera coordinate system, so as to obtain the spatial position of the background pattern captured by the corresponding camera in the unified camera coordinate system.

[0011] If the set of cameras is a subset of the plurality of cameras, then the camera extrinsic parameters of each camera in that subset are obtained, and after rotating the calibration plate of the circular asymmetric array pattern in a preset direction, the camera extrinsic parameters of each camera in another subset are obtained, the spatial positions of the calibration plate before and after rotation in a unified camera coordinate system are obtained, and the intersection of the calibration plate before and after rotation in space is taken as the initial center position of the flow field to be measured.

[0012] In one possible implementation, the camera performance parameters are processed to obtain the camera intrinsic parameters of each camera, including:

[0013] Based on the pixel length and the corresponding actual length at any two points in the chessboard pattern captured by each camera, the actual length corresponding to each pixel is obtained.

[0014] Based on the thin lens camera model, the camera performance parameters and the actual length corresponding to each pixel are processed to obtain the distance between the imaging surface and the center of the lens;

[0015] The camera intrinsic parameters of each camera are determined by processing the distance between the imaging plane and the center of the lens, the camera performance parameters, and the offset of the camera coordinate system origin from the pixel coordinate system origin.

[0016] In one possible implementation, the algorithm for calculating the distance between the imaging plane and the center of the lens is expressed as:

[0017]

[0018] Where D is the distance between the imaging plane and the center of the lens, f is the focal length of the lens, and Z is the focal length of the lens. BOsz is the theoretical distance from the background to the center of the camera, ca is the actual size of each pixel, and ca is the actual length of each pixel.

[0019] The camera intrinsic parameters are represented as follows:

[0020]

[0021] Where u0 and v0 are the offsets of the origin of the camera coordinate system relative to the origin of the pixel coordinate system, and sx and sy are the sizes of each pixel on the background surface corresponding to the camera's imaging plane. In one possible implementation, the absolute extrinsic parameter calibration order among the multiple cameras is determined using the target camera's camera coordinate system as a unified camera coordinate system, including:

[0022] The camera coordinate system of the target camera is determined to be a unified camera coordinate system;

[0023] Based on the preset absolute extrinsic parameter transfer conditions, the target camera is used as the starting camera for the transfer of absolute extrinsic parameters, and the absolute extrinsic parameter calibration order among the multiple cameras is determined; the preset absolute extrinsic parameter transfer conditions are the minimum number of cameras required to transfer absolute extrinsic parameters through the camera coordinate system.

[0024] In one possible implementation, the camera absolute extrinsic algorithm is expressed as:

[0025] R b =R ba R a

[0026] T b =T ba +R ba T a

[0027] The absolute extrinsic parameter calibration order for camera a and camera b is camera b -> camera a; the relative extrinsic parameters transformed from the coordinate system of camera b to the coordinate system of camera a include the rotation matrix R. ba With translation matrix T ba Camera a is the target camera, and the absolute extrinsic parameters of camera a include the preset rotation matrix R. a =I(3) and the preset translation matrix T a =0(3,1); R b and T b These are the absolute extrinsic parameters of camera b in a unified camera coordinate system.

[0028] In one possible implementation, the background location calibration algorithm is expressed as:

[0029]

[0030] in, The spatial position of the background pattern captured by the camera in a unified camera coordinate system; R b With T b Let X be the rotation and translation matrices of camera b in the absolute extrinsic parameters of a unified camera coordinate system; * ,Y * Z * ] T The spatial position of the angular feature point in the camera's b-coordinate system;

[0031] Among them, the spatial position of the corner feature point in the camera's b coordinate system [X] * ,Y * Z * ] T It can be calculated using the following formula:

[0032]

[0033] Among them, R b * ,T b * Let [X,Y,Z] be the rotation and translation matrices of camera b in the camera's extrinsic parameters within the camera's coordinate system. T The spatial position of the corner feature points of the background pattern in the world coordinate system.

[0034] In one possible implementation, after taking the spatial intersection of the calibration plate before and after rotation as the initial center position of the flow field to be measured, the method further includes:

[0035] Obtain the center position of the circle in the calibration plate before rotation that corresponds exactly to the outlet of the flow field device that generates the flow field to be measured;

[0036] The average value of the initial center position and the center position of the circle is determined as the target center position of the flow field to be measured.

[0037] In one possible implementation, the method further includes:

[0038] Based on the placement of each camera, background plate, and flow field device, as well as the target center location of the flow field to be measured, a spatial location map is drawn.

[0039] In the spatial location diagram, the edge points of the background patterns on each background board are connected to the center of the lens of the corresponding camera to obtain the spatial range of light between each camera and the background patterns on the corresponding background board.

[0040] The overlapping portion of the light space corresponding to each camera is defined as the flow field to be measured.

[0041] In one possible implementation, if the set of cameras is a subset of the plurality of cameras, then the camera extrinsic parameters of each camera in that subset are obtained, and after rotating the calibration plate of the circular asymmetric array pattern in a preset direction, the camera extrinsic parameters of each camera in another subset are obtained, including:

[0042] If the set of cameras is a subset of the plurality of cameras, then for any camera, the MATLAB algorithm is used to process the camera intrinsic parameter K and the corresponding calibration board center feature point pixel position to obtain the camera extrinsic parameter.

[0043] After rotating the calibration board of the circular asymmetric array pattern in a preset direction, obtain the calibration board images and corresponding calibration board center feature point pixel positions captured by each camera in another part of the cameras, and return to the execution step: For any camera, use the MATLAB algorithm to process the camera intrinsic parameter K of the camera and the corresponding calibration board center feature point pixel positions to obtain the camera extrinsic parameters of each camera in the other part of the cameras.

[0044] Secondly, a calibration device for a background schlieren tomography measurement system is provided. The background schlieren tomography measurement system includes multiple cameras, background plates corresponding to each camera, and a flow field device for generating the flow field to be measured. Each background plate is positioned within the focusing range of its respective camera, and each background plate is provided with a calibration plate bearing a checkerboard pattern. Each camera is positioned around the flow field device, and the flow field device is positioned within the common field of view of all cameras when they are out of focus. The device includes:

[0045] The acquisition unit is used to acquire calibration board images and corresponding calibration board center feature point pixel positions captured by a group of cameras with a common field of view of the flow field device, respectively, on a calibration board of a circular asymmetric array pattern, as well as background patterns with checkerboard markings on the corresponding background board and corner feature point pixel positions of the checkerboard in the corresponding background patterns captured by each camera; the calibration board of the circular asymmetric array pattern is set within the range where each camera is in a defocused state;

[0046] The determination unit is used to process camera performance parameters and determine the camera intrinsic parameters of each camera.

[0047] The processing unit is configured to, if the set of cameras consists of any two cameras, use a MATLAB algorithm to process the camera intrinsic parameters and the pixel position of the center feature point of the calibration board of any two cameras to obtain the relative extrinsic parameters of any two cameras, and to process the camera intrinsic parameters and the pixel position of the corner feature point of the two cameras to obtain the camera extrinsic parameters of each camera.

[0048] The calculation unit is used to determine the absolute extrinsic parameter calibration order among the multiple cameras using the camera coordinate system of the target camera as the unified camera coordinate system. It employs a camera absolute extrinsic parameter algorithm to calculate the relative extrinsic parameters of any two cameras sequentially according to the absolute extrinsic parameter calibration order, thereby obtaining the absolute extrinsic parameters of each camera in the unified camera coordinate system. The target camera is any one of the multiple cameras.

[0049] Furthermore, a background position calibration algorithm is used to calculate the spatial position of the corner feature point pixel position of each camera in the corresponding camera coordinate system and the absolute external parameters of the corresponding camera in the unified camera coordinate system, so as to obtain the spatial position of the background pattern captured by the corresponding camera in the unified camera coordinate system.

[0050] The acquisition unit is further configured to, if the group of cameras is a subset of the plurality of cameras, obtain the camera extrinsic parameters of each camera in that subset, and after rotating the calibration plate of the circular asymmetric array pattern in a preset direction, obtain the camera extrinsic parameters of each camera in another subset, obtain the spatial positions of the calibration plate before and after rotation in a unified camera coordinate system, and take the intersection of the calibration plate before and after rotation in space as the initial center position of the flow field to be measured.

[0051] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0052] Memory, used to store computer programs;

[0053] When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0054] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0055] This application provides a calibration method for a background schlieren tomography measurement system. After acquiring images of a calibration board with a circular asymmetric array pattern and the corresponding pixel positions of the center feature points of the calibration board from a group of cameras sharing a common field of view, and images of a background pattern with a checkerboard pattern and the pixel positions of the corner feature points of the checkerboard pattern from each camera, the calibration board is set within the range where each camera is out of focus. Camera performance parameters are processed to determine the intrinsic parameters of each camera. If the group of cameras consists of any two cameras, a MATLAB algorithm is used to process the intrinsic parameters of any two cameras and the pixel positions of the center feature points of the calibration board to obtain the relative extrinsic parameters of any two cameras. The absolute extrinsic parameter calibration order among multiple cameras is determined using the target camera's camera coordinate system as a unified camera coordinate system. An absolute extrinsic parameter algorithm is used to calibrate the absolute extrinsic parameters according to their respective values. The calibration sequence involves calculating the relative extrinsic parameters of any two cameras sequentially to obtain the absolute extrinsic parameters of each camera in a unified camera coordinate system. The target camera is any one of multiple cameras. A background position calibration algorithm is used to calculate the spatial position of the corresponding corner feature point pixels in their respective camera coordinate systems and the absolute extrinsic parameters of those cameras in the unified camera coordinate system, thus obtaining the spatial position of the background pattern acquired by each camera in the unified camera coordinate system. If a group of cameras comprises only a portion of multiple cameras, the camera extrinsic parameters of each camera in that portion are obtained. Furthermore, after rotating the calibration plate of the circular asymmetric array pattern in a preset direction, the camera extrinsic parameters of each camera in another portion are obtained. This yields the spatial positions of the calibration plate before and after rotation in the unified camera coordinate system, and the intersection of the two rotations is used as the initial center position of the flow field to be measured. This method effectively improves the efficiency and robustness of intrinsic parameter calibration. Attached Figure Description

[0056] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a schematic diagram of the structure of a background schlieren tomography measurement system provided in an embodiment of this application;

[0058] Figure 2 A schematic flowchart illustrating a calibration method for a background schlieren tomography measurement system provided in this application embodiment;

[0059] Figure 3A schematic diagram of a circular asymmetric array pattern calibration plate provided in an embodiment of this application;

[0060] Figure 4 A schematic diagram of a background pattern with a checkerboard pattern provided for an embodiment of this application;

[0061] Figure 5 A spatial location map in a unified coordinate system is provided for embodiments of this application;

[0062] Figure 6 A schematic diagram of the structure of a calibration device for a background schlieren tomography measurement system provided in this application embodiment;

[0063] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0064] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0065] Figure 1 This is a schematic diagram of a background schlieren tomography measurement system provided in an embodiment of this application. Figure 1 As shown, the system may include: multiple cameras (6 cameras in the figure), a background plate corresponding to each camera, and a flow field device for generating the flow field to be measured; wherein, each background plate is set within the range where the corresponding camera is in focus, each background plate is set with a background pattern marked with a checkerboard, each camera is set around the flow field device, and the flow field device is set within the common field of view of each camera when it is out of focus.

[0066] The calibration method for the background schlieren tomography measurement system provided in this application includes:

[0067] 1. Single-camera intrinsic parameter calibration.

[0068] 2. Calibration of relative external parameters between pairs of cameras.

[0069] 3. Absolute external parameter calibration for each camera.

[0070] 4. Background position marking.

[0071] 5. Flow field location calibration.

[0072] 6. Flow field position correction.

[0073] It has the following advantages:

[0074] 1. To address the ill-posed problem caused by simultaneous calibration of camera intrinsic and extrinsic parameters in traditional methods, this application adopts a calibration method that first theoretically determines the intrinsic parameters of a single camera and then solves to determine the extrinsic parameters of the camera, which can effectively improve the efficiency and robustness of intrinsic parameter calibration.

[0075] 2. In response to the problem that the calibration plate is in a defocused state when the camera is calibrated in pairs during the background schlieren measurement in traditional methods, it was found that compared with the feature points of the checkerboard calibration pattern, the feature points of the circular asymmetric array pattern are not affected even if the outer circle is blurred. Therefore, it is more suitable for calibrating the relative extrinsic parameters of the camera in a defocused state.

[0076] 3. In order to simplify the process of determining the spatial position of the flow field to be measured and the background pattern, this application designs a circular asymmetric array pattern and a checkerboard pattern to sequentially mark the flow field to be measured and the background pattern.

[0077] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0078] Figure 2 This is a flowchart illustrating a business data processing method provided in an embodiment of this application. Figure 2 As shown, the method may include:

[0079] Step S210: Obtain calibration board images and corresponding calibration board center feature point pixel positions captured by a group of cameras with a common field of view of the convection field device, respectively, on the calibration board of the circular asymmetric array pattern, as well as background patterns with checkerboard markings and corner feature point pixel positions of the checkerboard in the corresponding background patterns captured by each camera.

[0080] In this step, the calibration plate of the circular asymmetric array pattern is set within the common field of view of each camera when it is out of focus, such as the location of the flow field device.

[0081] Step S220: Process the camera performance parameters to determine the camera intrinsic parameters of each camera.

[0082] In practice, based on the pixel length L1[px] and the corresponding actual length L2[mm] at any two corner points of the checkerboard pattern in the background pattern captured by each camera, the actual length ca for each pixel is obtained as L2 / L1[mm / px].

[0083] Based on the thin lens camera model, the camera performance parameters and the actual length corresponding to each pixel are processed to obtain the distance between the imaging surface and the center of the lens;

[0084] The camera intrinsic parameters of each camera are determined by processing the distance between the imaging plane and the center of the lens, the camera performance parameters, and the offset of the camera coordinate system origin from the pixel coordinate system origin.

[0085] Assuming the camera is focused on the background, the algorithm for calculating the distance between the image plane and the center of the lens can be expressed as:

[0086]

[0087] Where D is the distance between the imaging plane and the center of the lens, f is the focal length of the lens, and Z is the focal length of the lens. BO sz represents the theoretical distance from the background to the center of the camera, in mm; ca represents the actual size of each pixel, in mm / px;

[0088] The camera intrinsic parameter K can be expressed as:

[0089]

[0090] Where K is a matrix, u0 and v0 are the offsets of the origin of the camera coordinate system relative to the origin of the pixel coordinate system, and sx and sy are the sizes of each pixel on the background surface corresponding to the image plane. The units of sx, sy, and sz are mm / px, and sx, sy, and sz are all known parameters inherent to the camera at the factory. Let the center of the image plane pixels (i.e., the center point of the image captured by the camera) be the origin of the camera coordinate system, then u0 = W / 2, v0 = H / 2, where W and H are the number of pixels in the width and height directions of the image plane, respectively.

[0091] Step S230: If a set of cameras consists of any two cameras, then use the MATLAB algorithm to process the intrinsic parameters of the cameras and the pixel positions of the feature points at the center of the calibration board for any two cameras to obtain the relative extrinsic parameters of any two cameras.

[0092] The MATLAB algorithm is the one that is executed in the existing MATLAB Stereo Camera Calibrator toolbox.

[0093] Step S240: Determine the absolute extrinsic parameter calibration order among multiple cameras using the camera coordinate system of the target camera as the unified camera coordinate system. Using the camera absolute extrinsic parameter algorithm, calculate the relative extrinsic parameters of any two cameras in sequence according to the absolute extrinsic parameter calibration order to obtain the absolute extrinsic parameters of each camera in the unified camera coordinate system.

[0094] The target camera is any one of multiple cameras.

[0095] In practice, the camera coordinate system of the target camera is determined to be a unified camera coordinate system;

[0096] Based on the preset absolute extrinsic parameter transfer conditions, the target camera is used as the starting camera for the transfer of absolute extrinsic parameters, and the absolute extrinsic parameter calibration order among multiple cameras is determined; wherein, the preset absolute extrinsic parameter transfer conditions are the minimum number of cameras required to transfer absolute extrinsic parameters through the camera coordinate system.

[0097] The camera absolute extrinsic parameter algorithm is expressed as follows:

[0098] R b =R ba R a

[0099] T b =T ba +R ba T a

[0100] The absolute extrinsic parameter calibration order for camera a and camera b is camera b -> camera a; the relative extrinsic parameters transformed from the coordinate system of camera b to the coordinate system of camera a include the rotation matrix R. ba With translation matrix T ba Camera a is the target camera, and the absolute extrinsic parameters of camera a include the preset rotation matrix R. a =I(3) and the preset translation matrix T a =0(3,1); R b and T b These are the absolute extrinsic parameters of camera b in a unified camera coordinate system.

[0101] Step S250: Using a background position calibration algorithm, calculate the spatial position of the corner feature point pixel position of each camera in the corresponding camera coordinate system and the absolute extrinsic parameters of the corresponding camera in the unified camera coordinate system to obtain the spatial position of the background pattern captured by the corresponding camera in the unified camera coordinate system.

[0102] The background location calibration algorithm can be expressed as:

[0103]

[0104] in, The spatial position of the background pattern captured by the camera in a unified camera coordinate system; R b With T b Let X be the rotation and translation matrices of camera b in the absolute extrinsic parameters of a unified camera coordinate system; * ,Y * Z * ] T The spatial position of the angular feature point in the camera's b-coordinate system;

[0105] Among them, [X * ,Y * Z* ] T The following formula is used to calculate:

[0106]

[0107] Among them, R b * ,T b * Let [X,Y,Z] be the rotation and translation matrices of camera b in the camera's extrinsic parameters within the camera's coordinate system. T The spatial position of the corner feature points of the background pattern in the world coordinate system.

[0108] Step S260: Using a flow field position calibration algorithm, if a group of cameras is a subset of multiple cameras, the camera extrinsic parameters of each camera in that subset are obtained. After rotating the calibration plate of the circular asymmetric array pattern in a preset direction, the camera extrinsic parameters of each camera in another subset are obtained. The spatial positions of the calibration plate before and after rotation in a unified camera coordinate system are obtained. The intersection of the calibration plate before and after rotation in space is taken as the initial center position of the flow field to be measured.

[0109] By rotating the calibration plate of the circular asymmetric array pattern in a preset direction, the calibration plate of the circular asymmetric array pattern after being rotated in the preset direction can be captured by each camera in another part of the camera.

[0110] If a group of cameras consists of a subset of multiple cameras, then for any given camera, a MATLAB algorithm is used to process the camera's intrinsic parameters and the corresponding calibration board's central feature point pixel position to obtain the camera's extrinsic parameters for each camera in that subset. Additionally, after rotating the calibration board of the circular asymmetric array pattern in a preset direction, images of the calibration board acquired by each camera in the other subset and the corresponding calibration board's central feature point pixel position are obtained. The process then returns to the execution steps: For any given camera, a MATLAB algorithm is used to process the camera's intrinsic parameters and the corresponding calibration board's central feature point pixel position to obtain the camera's extrinsic parameters for each camera in the other subset; the spatial positions of the calibration board before and after rotation in a unified camera coordinate system are obtained.

[0111] The flow field location calibration algorithm can be expressed as:

[0112]

[0113] in, The spatial position of a circular asymmetric array pattern captured by the camera in a unified camera coordinate system; R b With T bLet Xk be the rotation and translation matrices of camera b in the absolute extrinsic parameters of the unified camera coordinate system; * ,Yk * ,Zk * ] T The spatial position of the pixel location of the center feature point on the calibration board in the camera's b-coordinate system;

[0114] The spatial intersection of the calibration plate before and after rotation is taken as the initial center position of the flow field to be measured.

[0115] In some embodiments, the center position of the circle in the calibration plate before rotation that corresponds to the outlet of the flow field to be measured generated by the flow field device is obtained; the average value of the initial center position and the center position is determined as the target center position of the flow field to be measured.

[0116] Furthermore, based on the placement of each camera, each background plate, and the flow field device, as well as the target center position of the flow field to be measured, a spatial position diagram is drawn. In the spatial position diagram, the edge points of the background patterns on each background plate are connected to the lens centers of the corresponding cameras to obtain the spatial range of light rays between each camera and the background patterns on the corresponding background plates. The overlapping portion of the spatial range of light rays corresponding to each camera is determined as the flow field to be measured.

[0117] In one specific embodiment, the calibration method for the background schlieren tomography measurement system provided in this application may include:

[0118] 1. Single-camera intrinsic parameter calibration:

[0119] Using a thin-lens camera model, the intrinsic parameter K of a single camera is directly determined theoretically based on the camera performance parameters.

[0120] The following processing was performed on each camera:

[0121] (1) Printing Figure 4 The background pattern shown is a checkerboard pattern and is fixed to the background board.

[0122] (2) Take a picture of the background pattern and extract the pixel length L1[px] at any two corner points of the chessboard. Its actual length is L2[mm]. Record the actual length of each pixel as ca=L2 / L1[mm / px].

[0123] (3) Let the center of the imaging plane pixel (the center point of the image) be the origin of the camera coordinate system, then u0 = W / 2, v0 = H / 2, where W and H are the number of pixels in the width and height directions of the imaging plane, respectively.

[0124] (4) Calculate the distance D between the imaging surface and the center of the lens based on the thin lens camera model.

[0125] Then, the intrinsic parameters K of the single camera can be obtained by referring to the specific algorithm in step S220.

[0126] 2. Relative extrinsic parameter calibration between pairs of cameras:

[0127] After determining the intrinsic parameter K of a single camera, in order to unify the coordinate systems of each camera into a single coordinate system, it is also necessary to calibrate the relative extrinsic parameters between each pair of cameras. Since the cameras are focused on the background plate, the common field of view between the cameras is in the defocused flow field to be measured. Therefore, the calibration plate that can be jointly photographed by each pair of cameras will also be in a severely defocused state.

[0128] When out of focus, compared to the checkerboard calibration pattern, the blurred feature points are not easy to extract. Even if the outer circle of the circular asymmetric array pattern is blurred, the feature points of the center circle are still unaffected. Therefore, it is more suitable for calibrating the relative extrinsic parameters of the camera in the out-of-focus state.

[0129] The following processing is performed between pairs of cameras:

[0130] (1) Printing Figure 3 The calibration plate with the circular asymmetric array pattern shown is placed in the common field of view of the two cameras, that is, the calibration plate with the circular asymmetric array pattern is set within the range where the two cameras are in out-of-focus state.

[0131] (2) Detect the position of the center feature point of the calibration board in the calibration board image of the two cameras, such as the pixel position of the center feature point of each circle in the circular asymmetric array pattern.

[0132] (3) Based on the camera intrinsic parameters K and the position of the center feature point of each camera, the relative extrinsic parameters of the two cameras are solved by using the MATLAB Stereo CameraCalibrator toolbox and the calibration board images at different positions in the defocus state range acquired by the two cameras. That is, the relative extrinsic parameters under the ideal distortion-free condition, and the accuracy of the relative extrinsic parameters is iteratively optimized by using maximum likelihood estimation.

[0133] 3. Absolute extrinsic parameter calibration for each camera:

[0134] The specific implementation method is as follows:

[0135] (1) Select one of the cameras as the starting camera, and denote the camera coordinate system of the starting camera as the unified coordinate system.

[0136] (2) When determining the pairwise calibration order of cameras, errors caused by the transfer of absolute extrinsic parameters of the cameras should be avoided as much as possible. For example, assuming that camera 2 is the initial camera, the pairwise calibration order can be "2-1, 2-3, 2-6, 6-5, 6-4". In this case, the transfer of absolute extrinsic parameters of the cameras only includes the "2-6" step.

[0137] (3) According to the pairwise calibration order of the cameras and the absolute extrinsic parameter algorithm of step S240, the camera coordinate system of other cameras is sequentially transferred to the starting camera coordinate system, thereby obtaining the absolute extrinsic parameters of each camera under the unified camera coordinate system.

[0138] 4. Background position marking:

[0139] Based on establishing a unified coordinate system, it is also necessary to determine the spatial positions of the flow field to be measured and the background within that unified coordinate system. Therefore, the specific implementation method for background position calibration is as follows:

[0140] (1) Printing Figure 4 The background pattern shown is marked with a checkerboard pattern, and the background pattern is fixed to the background board.

[0141] (2) Detect the pixel positions of the corner feature points of the chessboard in the image captured by each camera.

[0142] (3) Based on the camera intrinsic parameter K and the pixel positions of the corner feature points, the MATLAB Camera Calibrator toolbox is used to solve the camera extrinsic parameters (R) of each camera under ideal distortion-free conditions using a background pattern image. b * ,T b * The accuracy of the single-camera extrinsic parameters is optimized iteratively using maximum likelihood estimation.

[0143] (4) If the background pattern is captured by camera b, the position of its corner feature points in the camera b coordinate system is [X]. * ,Y * Z * ] T Spatial location: Taking the camera a coordinate system as a unified camera coordinate system, then the position of the background pattern in the unified camera coordinate system. for:

[0144]

[0145] The spatial position of the corner feature point in the camera's b-coordinate system [X] * ,Y * Z * ] T It can be calculated using the following formula:

[0146]

[0147] Among them, R b * ,T b * Let [X,Y,Z] be the extrinsic parameters of camera b in the camera b coordinate system. TThis represents the spatial position of the background pattern feature points in the world coordinate system.

[0148] 5. Flow field location calibration:

[0149] (1) Print a calibration plate with a circular asymmetric array pattern and place it above the flow field to be measured.

[0150] (2) Figure 1 As shown, cameras 1, 2, and 3 are grouped together to photograph the calibration board at one of its locations. Based on the obtained camera intrinsic parameters K, the MATLAB toolbox is used to solve for the single-camera extrinsic parameters of the calibration board in each camera coordinate system under ideal distortion-free conditions, and the accuracy is iteratively optimized using maximum likelihood estimation.

[0151] Similarly, such as Figure 1 As shown, cameras 4, 5, and 6 are grouped together, and the calibration plates at their intersection positions are photographed. The single-camera extrinsic parameters in each camera coordinate system are calculated. The intersection position is the spatial position where the calibration plates before and after rotating the circular asymmetric array pattern calibration plates in a preset direction intersect.

[0152] (3) Based on the absolute external parameters of the camera, the spatial position of the background pattern calibration board captured by each camera in a unified spatial coordinate system can be obtained by the following formula.

[0153]

[0154] in, The spatial position of a circular asymmetric array pattern captured by the camera in a unified camera coordinate system; R b With T b Let Xk be the rotation and translation matrices of camera b in the absolute extrinsic parameters of the unified camera coordinate system; * ,Yk * ,Zk * ] T The spatial position of the pixel location of the center feature point on the calibration board in the camera's b-coordinate system;

[0155] Ideally, the calibration plates captured by cameras 1, 2, and 3 should coincide in a unified spatial coordinate system; the calibration plates captured by cameras 4, 5, and 6 should also coincide in a unified spatial coordinate system; and these two sets of calibration plates should intersect. The intersection position of the two sets of calibration plates is taken as the initial value of the spatial position of the center of the flow field to be measured.

[0156] 6. Flow field position correction:

[0157] The specific implementation method is as follows:

[0158] Draw a spatial position diagram of the camera, background, and flow field initial positions in a unified coordinate system, such as... Figure 5As shown. Connect the light rays emitted from the spots at the edge of the background pattern to the center of the lens to obtain the range of light between each camera and the background pattern.

[0159] Observe the overlapping areas of the light rays from each camera. Adjust the center position and range of the flow field to be measured, with the principle of placing the flow field to be measured as close as possible to the center of the overlapping light range.

[0160] Corresponding to the above method, embodiments of this application also provide a calibration device for a background schlieren tomography measurement system, such as... Figure 6 As shown, the device includes:

[0161] The acquisition unit 610 is used to acquire calibration board images and corresponding calibration board center feature point pixel positions captured by a group of cameras with a common field of view of the flow field device, respectively, as well as background patterns with checkerboard markings on the corresponding background board and corner feature point pixel positions of the checkerboard in the corresponding background patterns captured by each camera; the calibration board of the circular asymmetric array pattern is set within the range where each camera is in a defocused state.

[0162] The determination unit 620 is used to process camera performance parameters and determine the camera intrinsic parameters of each camera;

[0163] The processing unit 630 is used to process the camera intrinsic parameters and the pixel position of the center feature point of the calibration board of any two cameras using a MATLAB algorithm if the set of cameras consists of any two cameras, so as to obtain the relative extrinsic parameters of the two cameras.

[0164] The calculation unit 640 is used to determine the absolute extrinsic parameter calibration order among the multiple cameras using the camera coordinate system of the target camera as the unified camera coordinate system. It uses the camera absolute extrinsic parameter algorithm to calculate the relative extrinsic parameters of any two cameras in sequence according to the absolute extrinsic parameter calibration order, so as to obtain the absolute extrinsic parameters of each camera in the unified camera coordinate system. The target camera is any one of the multiple cameras.

[0165] Furthermore, a background position calibration algorithm is used to calculate the spatial position of the corner feature point pixel position of each camera in the corresponding camera coordinate system and the absolute external parameters of the corresponding camera in the unified camera coordinate system, so as to obtain the spatial position of the background pattern captured by the corresponding camera in the unified camera coordinate system.

[0166] The acquisition unit 610 is further configured to, if the group of cameras is a subset of the plurality of cameras, obtain the camera extrinsic parameters of each camera in that subset of cameras, and after rotating the calibration plate of the circular asymmetric array pattern in a preset direction, obtain the camera extrinsic parameters of each camera in another subset of cameras, obtain the spatial positions of the calibration plate before rotation and the calibration plate after rotation in a unified camera coordinate system, and take the intersection position of the calibration plate before rotation and the calibration plate after rotation in space as the initial center position of the flow field to be measured.

[0167] The functions of each functional unit of the calibration device for the background schlieren tomography measurement system provided in the above embodiments of this application can be implemented through the above-described method steps. Therefore, the specific working process and beneficial effects of each unit in the calibration device for the background schlieren tomography measurement system provided in the embodiments of this application will not be repeated here.

[0168] This application also provides an electronic device, such as... Figure 7 As shown, it includes a processor 710, a communication interface 720, a memory 730, and a communication bus 740, wherein the processor 710, the communication interface 720, and the memory 730 communicate with each other through the communication bus 740.

[0169] Memory 730 is used to store computer programs;

[0170] When the processor 710 executes the program stored in the memory 730, it performs the following steps:

[0171] The calibration board images and corresponding center feature point pixel positions of the calibration board are acquired by a group of cameras with a common field of view of the flow field device, respectively. The background pattern with checkerboard markings and the corner feature point pixel positions of the checkerboard in the corresponding background pattern are also acquired by each camera. The calibration board of the circular asymmetric array pattern is set within the range where each camera is in defocus.

[0172] The camera performance parameters are processed to determine the camera intrinsic parameters of each camera;

[0173] If the set of cameras consists of any two cameras, then the MATLAB algorithm is used to process the intrinsic parameters of the cameras and the pixel positions of the feature points at the center of the calibration board of any two cameras to obtain the relative extrinsic parameters of the cameras.

[0174] The absolute extrinsic parameter calibration order among the multiple cameras is determined using the camera coordinate system of the target camera as a unified camera coordinate system. The absolute extrinsic parameter algorithm is used to calculate the relative extrinsic parameters of any two cameras in sequence according to the absolute extrinsic parameter calibration order, so as to obtain the absolute extrinsic parameters of each camera in the unified camera coordinate system. The target camera is any one of the multiple cameras.

[0175] A background position calibration algorithm is used to calculate the spatial position of the corner feature point pixel position of each camera in the corresponding camera coordinate system and the absolute extrinsic parameters of the corresponding camera in the unified camera coordinate system, so as to obtain the spatial position of the background pattern captured by the corresponding camera in the unified camera coordinate system.

[0176] If the set of cameras is a subset of the plurality of cameras, then the camera extrinsic parameters of each camera in that subset are obtained, and after rotating the calibration plate of the circular asymmetric array pattern in a preset direction, the camera extrinsic parameters of each camera in another subset are obtained, the spatial positions of the calibration plate before and after rotation in a unified camera coordinate system are obtained, and the intersection of the calibration plate before and after rotation in space is taken as the initial center position of the flow field to be measured.

[0177] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0178] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0179] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0180] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0181] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 2 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0182] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform the calibration method of the background schlieren tomography measurement system described in any of the above embodiments.

[0183] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the calibration method of the background schlieren tomography measurement system described in any of the above embodiments.

[0184] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0185] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0186] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0187] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0188] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0189] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims in this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. A calibration method for a background schlieren tomography measurement system, characterized in that, The background schlieren tomography system includes multiple cameras, background plates corresponding to each camera, and a flow field device for generating the flow field to be measured. Each background plate is positioned within the focusing range of its respective camera, and each background plate has a calibration plate with a checkerboard pattern. Each camera is positioned around the flow field device, and the flow field device is positioned within the common field of view of all cameras when they are out of focus. The method includes: The calibration board images and corresponding center feature point pixel positions of the calibration board are acquired by a group of cameras with a common field of view of the flow field device, respectively. The background pattern with checkerboard markings and the corner feature point pixel positions of the checkerboard in the corresponding background pattern are also acquired by each camera. The calibration board of the circular asymmetric array pattern is set within the range where each camera is in defocus. The camera performance parameters are processed to determine the camera intrinsic parameters of each camera; If the set of cameras consists of any two cameras, then the MATLAB algorithm is used to process the intrinsic parameters of the cameras and the pixel positions of the feature points at the center of the calibration board of any two cameras to obtain the relative extrinsic parameters of the cameras. The absolute extrinsic parameter calibration order among the multiple cameras is determined using the camera coordinate system of the target camera as a unified camera coordinate system. The absolute extrinsic parameter algorithm is used to calculate the relative extrinsic parameters of any two cameras in sequence according to the absolute extrinsic parameter calibration order, so as to obtain the absolute extrinsic parameters of each camera in the unified camera coordinate system. The target camera is any one of the multiple cameras. A background position calibration algorithm is used to calculate the spatial position of the corner feature point pixel position of each camera in the corresponding camera coordinate system and the absolute extrinsic parameters of the corresponding camera in the unified camera coordinate system, so as to obtain the spatial position of the background pattern captured by the corresponding camera in the unified camera coordinate system. If the set of cameras is a subset of the plurality of cameras, then the camera extrinsic parameters of each camera in that subset are obtained, and after rotating the calibration plate of the circular asymmetric array pattern in a preset direction, the camera extrinsic parameters of each camera in another subset are obtained, the spatial positions of the calibration plate before and after rotation in a unified camera coordinate system are obtained, and the intersection of the calibration plate before and after rotation in space is taken as the initial center position of the flow field to be measured.

2. The method as described in claim 1, characterized in that, The camera performance parameters are processed to obtain the camera intrinsic parameters for each camera, including: Based on the pixel length and the corresponding actual length at any two points in the chessboard pattern captured by each camera, the actual length corresponding to each pixel is obtained. Based on the thin lens camera model, the camera performance parameters and the actual length corresponding to each pixel are processed to obtain the distance between the imaging surface and the center of the lens; The camera intrinsic parameters of each camera are determined by processing the distance between the imaging plane and the center of the lens, the camera performance parameters, and the offset of the camera coordinate system origin from the pixel coordinate system origin.

3. The method as described in claim 2, characterized in that, The algorithm for calculating the distance between the imaging plane and the center of the lens is expressed as follows: Where D is the distance between the imaging plane and the center of the lens, f is the focal length of the lens, and Z is the focal length of the lens. BO sz is the theoretical distance from the background to the center of the camera, ca is the actual size of each pixel, and ca is the actual length of each pixel. The camera intrinsic parameters are represented as follows: Where K is a matrix, u0 and v0 are the offsets of the origin of the camera coordinate system relative to the origin of the pixel coordinate system, and sx and sy are the sizes of each pixel on the background surface corresponding to each pixel on the camera imaging surface.

4. The method as described in claim 1, characterized in that, Determining the absolute extrinsic parameter calibration order among the multiple cameras using the target camera's camera coordinate system as a unified camera coordinate system includes: The camera coordinate system of the target camera is determined to be a unified camera coordinate system; Based on the preset absolute extrinsic parameter transfer conditions, the target camera is used as the starting camera for the transfer of absolute extrinsic parameters, and the absolute extrinsic parameter calibration order among the multiple cameras is determined; the preset absolute extrinsic parameter transfer conditions are the minimum number of cameras required to transfer absolute extrinsic parameters through the camera coordinate system.

5. The method as described in claim 4, characterized in that, The camera absolute extrinsic parameter algorithm is expressed as follows: R b =R ba R a T b =T ba +R ba T a The absolute extrinsic parameter calibration order for camera a and camera b is camera b -> camera a; the relative extrinsic parameters transformed from the coordinate system of camera b to the coordinate system of camera a include the rotation matrix R. ba With translation matrix T ba Camera a is the target camera, and the absolute extrinsic parameters of camera a include the preset rotation matrix R. a =I(3) and the preset translation matrix T a =0(3,1); R b and T b These are the absolute extrinsic parameters of camera b in a unified camera coordinate system.

6. The method as described in claim 5, characterized in that, The background location calibration algorithm is expressed as follows: in, The spatial position of the background pattern captured by the camera in a unified camera coordinate system; R b With T b Let X be the rotation and translation matrices of camera b in the absolute extrinsic parameters of a unified camera coordinate system; * ,Y * Z * ] T The spatial position of the angular feature point in the camera's b-coordinate system; Among them, the spatial position of the corner feature point in the camera's b coordinate system [X] * ,Y * Z * ] T The following formula is used to calculate: Among them, R b * ,T b * Let [X,Y,Z] be the rotation and translation matrices of camera b in the camera's extrinsic parameters within the camera's coordinate system. T The spatial position of the corner feature points of the background pattern in the world coordinate system.

7. The method as described in claim 1, characterized in that, After using the spatial intersection of the calibration plate before and after rotation as the initial center position of the flow field to be measured, the method further includes: Obtain the center position of the circle in the calibration plate before rotation that corresponds exactly to the outlet of the flow field device that generates the flow field to be measured; The average value of the initial center position and the center position of the circle is determined as the target center position of the flow field to be measured.

8. The method as described in claim 7, characterized in that, The method further includes: Based on the placement of each camera, background plate, and flow field device, as well as the target center location of the flow field to be measured, a spatial location map is drawn. In the spatial location diagram, the edge points of the background patterns on each background board are connected to the center of the lens of the corresponding camera to obtain the spatial range of light between each camera and the background patterns on the corresponding background board. The overlapping portion of the light space corresponding to each camera is defined as the flow field to be measured.

9. The method as described in claim 1, characterized in that, If the set of cameras is a subset of the plurality of cameras, then the camera extrinsic parameters of each camera in that subset are obtained, and after rotating the calibration plate of the circular asymmetric array pattern in a preset direction, the camera extrinsic parameters of each camera in another subset are obtained, including: If the set of cameras is a subset of the plurality of cameras, then for any camera, the MATLAB algorithm is used to process the camera intrinsic parameters of the camera and the pixel position of the corresponding calibration board center feature point to obtain the camera extrinsic parameters of the camera. After rotating the calibration board of the circular asymmetric array pattern in a preset direction, obtain the calibration board images and corresponding calibration board center feature point pixel positions captured by each camera in another part of the cameras, and return to the execution step: for any camera, use the MATLAB algorithm to process the camera intrinsic parameters of the camera and the corresponding calibration board center feature point pixel positions to obtain the camera extrinsic parameters of each camera in the other part of the cameras.

10. A calibration device for a background schlieren tomography measurement system, characterized in that, The background schlieren tomography system includes multiple cameras, background plates corresponding to each camera, and a flow field device for generating the flow field to be measured. Each background plate is positioned within the focusing range of its respective camera, and each background plate has a calibration plate with a checkerboard pattern. Each camera is positioned around the flow field device, and the flow field device is positioned within the common field of view of all cameras when they are out of focus. The device includes: The acquisition unit is used to acquire calibration board images and corresponding calibration board center feature point pixel positions captured by a group of cameras with a common field of view of the flow field device, respectively, on a calibration board of a circular asymmetric array pattern, as well as background patterns with checkerboard markings on the corresponding background board and corner feature point pixel positions of the checkerboard in the corresponding background patterns captured by each camera; the calibration board of the circular asymmetric array pattern is set within the range where each camera is in a defocused state; The determination unit is used to process camera performance parameters and determine the camera intrinsic parameters of each camera. The processing unit is used to process the intrinsic parameters of the cameras and the pixel positions of the center feature points of the calibration board using a MATLAB algorithm if the set of cameras consists of any two cameras, so as to obtain the relative extrinsic parameters of the two cameras. The calculation unit is used to determine the absolute extrinsic parameter calibration order among the multiple cameras using the camera coordinate system of the target camera as the unified camera coordinate system. It employs a camera absolute extrinsic parameter algorithm to calculate the relative extrinsic parameters of any two cameras sequentially according to the absolute extrinsic parameter calibration order, thereby obtaining the absolute extrinsic parameters of each camera in the unified camera coordinate system. The target camera is any one of the multiple cameras. Furthermore, a background position calibration algorithm is used to calculate the spatial position of the corner feature point pixel position of each camera in the corresponding camera coordinate system and the absolute external parameters of the corresponding camera in the unified camera coordinate system, so as to obtain the spatial position of the background pattern captured by the corresponding camera in the unified camera coordinate system. The acquisition unit is further configured to, if the group of cameras is a subset of the plurality of cameras, obtain the camera extrinsic parameters of each camera in that subset, and after rotating the calibration plate of the circular asymmetric array pattern in a preset direction, obtain the camera extrinsic parameters of each camera in another subset, obtain the spatial positions of the calibration plate before and after rotation in a unified camera coordinate system, and take the intersection of the calibration plate before and after rotation in space as the initial center position of the flow field to be measured.

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