Method and device for identifying multi-camera external parameter unified calibration board and establishing global coordinate system

Abstract

The invention relates to the field of calibration board design and global coordinate system establishment, in particular to a multi-camera external parameter unified calibration board identification and global coordinate system establishment method and device. The multi-camera external parameter unified calibration board identification and global coordinate system establishment method comprises the following steps: adjusting and setting a calibration board, cameras and a laser tracker to initial positions according to a large-view-field environment; acquiring a calibration plate image sequence in a corresponding view field by using the camera, and acquiring calibration plate feature point information according to the calibration plate image sequence; measuring reference point spatial position data of the calibration plate by using the laser tracker; and respectively calculating different camera coordinate system poses corresponding to the calibration plate according to the calibration plate feature point information, calculating a laser tracker coordinate system pose corresponding to the calibration plate according to the reference point space position data, and finally establishing a corresponding global coordinate system. The laser tracker is introduced for auxiliary measurement, calibration efficiency and precision are improved, and reliability of calibration results among multiple devices is improved.

Description

Technical Field

[0001] This invention relates to the fields of computer vision and measurement technology, specifically to a method and apparatus for identifying a unified calibration board for multi-camera extrinsic parameters and establishing a global coordinate system. Background Technology

[0002] With the development of industrial manufacturing technology, higher requirements are being placed on the high-precision detection of large-sized objects. In the fields of computer vision and photogrammetry, camera calibration boards are key tools for accurately calibrating camera intrinsic parameters (such as focal length, principal point, distortion coefficients, etc.) and extrinsic parameters (the camera's position and orientation in the world coordinate system). They provide a set of feature points with known precise geometric relationships (such as corner points and center points). By capturing images of the calibration board in different orientations and using these known points and their projected positions in the images, combined with mathematical calculations, the camera parameters can be solved. However, when performing multi-camera detection on large-sized objects, it is usually necessary to paste marker points as features on the object surface to match the pixel coordinates of the marker points in the image with their actual 3D coordinates. This method relies on manual operation, which is cumbersome and inefficient, and also requires additional cleaning of the marker points, increasing the workload.

[0003] In recent years, methods for establishing a global coordinate system based on laser trackers and calibration boards have gradually attracted attention. Traditional methods primarily focus on the intrinsic parameter calibration of a single camera, while efficiently unifying the extrinsic parameters of multiple cameras in a multi-camera system remains a challenge. Although existing research has attempted to improve calibration efficiency by optimizing calibration board patterns or improving algorithms, practical applications still face problems such as complex calibration board design, limited feature point detection accuracy, and difficulties in coordinate system transformation between multiple devices.

[0004] In summary, there is an urgent need to propose a new calibration plate design and global coordinate system establishment method that can simplify the operation process, improve the calibration efficiency and accuracy of multi-camera systems, and meet the needs of large-size object detection. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and apparatus for identifying a unified calibration board for multiple camera extrinsic parameters and establishing a global coordinate system. By optimizing the design of the calibration board and its identification algorithm, and combining it with a laser tracker, the coordinates of multiple devices are unified, avoiding the need for pasting and clearing marker points in traditional methods, while improving calibration efficiency and accuracy.

[0006] To achieve the above objectives, this invention provides a method for identifying a unified calibration board for multiple camera extrinsic parameters and establishing a global coordinate system, comprising: adjusting and setting the calibration board, camera, and laser tracker to their initial positions according to a large field-of-view environment; acquiring calibration board image sequences in corresponding fields of view using the camera, and obtaining calibration board feature point information based on the calibration board image sequences; measuring the spatial position data of reference points of the calibration board using the laser tracker; calculating the pose of the calibration board in different camera coordinate systems based on the calibration board feature point information; calculating the pose of the calibration board in the laser tracker coordinate system based on the spatial position data of the reference points; and calculating the extrinsic parameters of different cameras corresponding to the same global coordinate system based on the poses of the calibration board in different camera coordinate systems and the poses of the calibration board in the laser tracker coordinate system as intermediate coordinate relationships, wherein the same global coordinate system is obtained based on a common reference coordinate system, different cameras correspond to independent calibration relationships, and the spatial position data of the reference points is not used for solving camera imaging model parameters.

[0007] Preferably, the step of adjusting the calibration board, camera, and laser tracker to their initial positions according to the large field-of-view environment includes: adjusting the camera position according to the large field-of-view environment so that the calibration board occupies a large proportion of the camera's field of view without exceeding the field of view; obtaining corresponding reference points based on the size of the calibration board target base and target ball; and adjusting the position of the laser tracker so that it can observe the reference points on the calibration board without obstruction; wherein the large proportion is not less than 70%, and the number of reference points is 4.

[0008] Preferably, the process of acquiring calibration board image sequences using the camera in corresponding fields of view and obtaining calibration board feature point information based on the calibration board image sequences includes: setting camera parameters to ensure appropriate exposure and clear images; capturing 15 to 25 calibration board images in different poses, each image requiring a black-and-white edge width of approximately 3 pixels; performing grayscale processing on the images and then using Gaussian filtering or median filtering to reduce noise; for checkerboard patterns, using adaptive thresholding to segment the image into black-and-white regions, finding contours, and filtering out approximate quadrilateral contours through polygon approximation; verifying whether the quadrilateral's topological structure conforms to the set dimensions, and if it does, outputting a list of coarse coordinates of the interior corner points; refining the coarse coordinates to the sub-pixel level, and recording the final corner point coordinates as calibration board feature point information.

[0009] Preferably, the method of measuring the spatial position data of the reference point of the calibration plate using the laser tracker includes: installing four laser tracker target mounts around the calibration plate, with a spherical target embedded in the center of each target mount, the diameter of which is adapted to the laser tracker; starting the laser tracker, measuring the center position of the four spherical targets in sequence, and recording the three-dimensional spatial coordinates; and calculating the spatial position data of the reference point of the calibration plate based on the geometric offset between the spherical target and the center of the pattern on the calibration plate.

[0010] Preferably, calculating the pose of the calibration board corresponding to different camera coordinate systems based on the feature point information of the calibration board includes: using the Zhang Zhengyou calibration algorithm, combining the corner coordinates extracted from the image with the feature point information of the calibration board, generating intrinsic and extrinsic parameter matrices for different cameras; substituting the relative positional relationship between the sphere center coordinates measured by the laser tracker and the feature points of the calibration board pattern into the coordinate transformation formula to construct the rotation matrix and translation vector of the calibration board in the coordinate system of the laser tracker; and calculating the pose of the calibration board corresponding to different camera coordinate systems by simultaneously solving the extrinsic parameter matrix of the camera and the pose of the calibration board in the coordinate system of the laser tracker.

[0011] Furthermore, calculating the coordinate system pose of the calibration board corresponding to the laser tracker based on the spatial position data of the reference point includes: obtaining the extrinsic parameter matrix of each camera in the global coordinate system based on the spatial position data of the reference point.

[0012] Preferably, calculating the coordinate system pose of the calibration plate corresponding to the laser tracker based on the spatial position data of the reference point further includes: when the calibration result is valid, generating a unified projection model in the global coordinate system based on the extrinsic parameter matrix of each camera; using this model to back-project the surface defects of the object under test onto the digital model to complete the defect detection task.

[0013] Based on the same inventive concept, this invention also provides a device for identifying a unified calibration plate for multiple camera extrinsic parameters and establishing a global coordinate system, including a calibration plate, a camera, and a laser tracker. The calibration plate, camera, and laser tracker are arranged in a specific layout around the object to be measured. The formula for calculating the relative positional relationship between the camera and the laser tracker is as follows: in, To determine the pose of the pattern under the laser tracker; The 3×3 rotation matrix is ​​used to track the sphere under the laser tracker; This is the 3×1 translation vector of the tracking ball under the laser tracker; This represents the offset between the center of the sphere and the calibration pattern. To calibrate the pose of the pattern in the camera coordinate system; The pose of the camera under the laser tracker.

[0014] Preferably, the calibration plate is made of carbon fiber material, which is lightweight and high-strength; the surface of the calibration plate is printed with an n-row m-column black and white checkerboard pattern or Aruco code pattern, and the pattern size is designed according to actual needs; four laser tracker target seats are provided around the calibration plate, and the height of the target seats is the same as the thickness of the calibration plate to ensure that the center of the spherical target is located on the same plane.

[0015] Compared with the closest existing technology, the present invention has the following advantages: By optimizing the calibration plate design and introducing a laser tracker to assist in measurement, the calibration efficiency and accuracy of the multi-camera system are improved while simplifying the operation process. The choice of calibration plate material reduces the overall weight, making it easier to carry and install. The introduction of the laser tracker avoids dependence on marker points, reduces the workload of manual operation, and improves the reliability of calibration results. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method for identifying a unified calibration board for multi-camera extrinsic parameters and establishing a global coordinate system provided by the present invention; Figure 2 This is a schematic diagram of the checkerboard calibration board for the method of identifying the unified calibration board for multi-camera extrinsic parameters and establishing a global coordinate system provided by the present invention. Detailed Implementation

[0017] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1: This invention provides a method for identifying a unified calibration plate for multi-camera extrinsic parameters and establishing a global coordinate system, such as... Figure 1 As shown, the setup includes: a calibration plate fixed to the surface of the object under test or its vicinity; and a camera and laser tracker adjusted to appropriate positions for observation and measurement of the calibration plate. The calibration plate is made of carbon fiber, which is lightweight and high-strength. Figure 2 As shown, its surface is printed with an n-row, m-column black and white checkerboard pattern or Aruco code pattern, the size of which is designed according to actual needs. Four laser tracker target mounts are located around the calibration plate, with a spherical target embedded in the center of each mount. The height of the target mount is consistent with the thickness of the calibration plate, ensuring that the centers of the spherical targets are on the same plane. The camera position is adjusted so that the calibration plate occupies a large proportion of the camera's field of view without exceeding its field of view. The laser tracker position is adjusted so that it can observe the four spherical targets on the calibration plate without obstruction.

[0020] During operation, the camera position is adjusted according to the large field of view environment, ensuring the calibration board occupies a significant portion of the camera's field of view without exceeding its range. Reference points are obtained based on the dimensions of the calibration board's target mount and target ball. The laser tracker position is adjusted, with the four laser tracker target mounts positioned outside the checkerboard grid to ensure unobstructed observation of the reference points on the calibration board; the largest proportion of these reference points should be no less than 70%, and the number of reference points should be four. After these adjustments, camera parameters are set to ensure proper exposure and image clarity. Then, 15 to 25 images of the calibration board in different poses are captured. Each image must have a black-and-white edge width of approximately 3 pixels. After capturing the images, they are converted to grayscale and Gaussian or median filtering is used to reduce noise. For the checkerboard pattern, an adaptive threshold is used to segment the image into black and white regions. Contours are found and approximate quadrilateral contours are selected through polygon approximation. The topological structure of the quadrilaterals is verified to meet the set size. If the condition is met, a list of coarse coordinates of the interior corner points is output. The coarse coordinates are then refined to the sub-pixel level, and the final corner coordinates are recorded as feature point information of the calibration board.

[0021] Simultaneously, the laser tracker is activated to sequentially measure the center positions of four spherical targets and record their three-dimensional spatial coordinates. Based on the geometric offset between the spherical targets and the center of the calibration board pattern, the spatial position data of the reference points of the calibration board is calculated. Using the corner coordinates extracted from the image and the feature point information of the calibration board, the Zhang Zhengyou calibration algorithm is used to generate the camera's intrinsic and extrinsic parameter matrices. Substituting the relative positional relationship between the spherical center coordinates measured by the laser tracker and the feature points of the calibration board pattern into the coordinate transformation formula, the rotation matrix and translation vector of the calibration board in the laser tracker coordinate system are constructed. By simultaneously solving the camera extrinsic parameter matrix and the calibration board pose in the laser tracker coordinate system, the pose of the calibration board corresponding to different camera coordinate systems is calculated.

[0022] After calculating the pose of the calibration board in the global coordinate system, the pose information of the calibration board is used to unify the coordinate relationship between the camera and the laser tracker. The extrinsic parameter matrix of each camera in the global coordinate system is obtained based on the spatial position data of the reference points. Throughout the process, the design and layout of the calibration board are key factors in achieving high-precision calibration. The calibration board is made of carbon fiber, which not only reduces the overall weight for easy carrying and installation but also ensures its stability in different environments. The black and white checkerboard pattern or Aruco code pattern on the surface of the calibration board is carefully designed to ensure accurate identification under different lighting conditions. The cooperation between the four laser tracker target mounts around the calibration board and the spherical target allows the laser tracker to quickly and accurately measure the spatial position of the calibration board, thus avoiding the need for pasting and removing markers in traditional methods.

[0023] In this embodiment, the method for identifying a unified calibration board for multi-camera extrinsic parameters and establishing a global coordinate system includes the following specific implementation steps: Step 1: Fix a calibration plate made of carbon fiber material onto the surface of the object to be measured or its vicinity. Specifically, the calibration plate is made of carbon fiber composite material with a density of less than 1.8 g / cm³, an elastic modulus greater than 230 GPa, a coefficient of thermal expansion of less than 1.5 microstrains per degree Celsius, and maintains structural deformation of less than 5 micrometers in an environment with a temperature range of -10°C to 60°C, thereby effectively improving the geometric stability during the calibration process. The surface of the calibration plate is printed with an n x m black and white checkerboard pattern or an Aruco code pattern, where n and m are positive integers, typically 8 x 11 or 9 x 12, and the pattern size is set according to the actual measurement requirements. When using a black and white checkerboard pattern, the cell side length is 10 mm to 50 mm, the corner spacing tolerance is controlled within ±0.01 mm, and the surface is treated with a matte coating with a reflectivity between 15% and 35% to suppress specular reflection and enhance visual recognition robustness under strong light, weak light, and non-uniform lighting conditions. When using an Aruco code pattern, each code block is 20 mm by 20 mm in size, with an optimized internal black and white pixel distribution. An error correction coding method with a Hamming distance of 3 is used to ensure accurate decoding even when the image rotation angle reaches ±45 degrees, and the minimum recognition resolution reaches 8 by 8 pixels per code block. Four laser tracker target mounts are set around the calibration plate, symmetrically arranged at the four corners of the calibration plate, with a distance from the edge of 1 / 10 of the calibration plate length. The height of the target mounts is consistent with the thickness of the calibration plate, ensuring that the centers of all spherical targets are located on the same plane. The target holder features an internal magnetic structure for securing the spherical target, which has a diameter of 19.05 mm. The target's center position has a repeatability accuracy better than 0.5 micrometers, ensuring measurement consistency after multiple reassemblies and disassemblies. The calibration plate integrates a quick-installation interface on the back, supporting connection to tripods, magnetic bases, or robotic arm end effectors. The installation repeatability is better than 10 micrometers, making it suitable for on-site 3D measurement of large components in aerospace, rail transportation, and large shipbuilding industries.

[0024] In the aforementioned method for calibration board identification and global coordinate system establishment, step 2 involves adjusting the camera position so that the calibration board occupies at least 70% of the camera's field of view and is completely within its field of view. Simultaneously, the laser tracker position is adjusted to ensure unobstructed observation of the four spherical targets on the calibration board. Specifically, the camera uses an industrial-grade CMOS or CCD sensor with a resolution of at least 5 megapixels. The lens focal length is selected within the range of 12 mm to 50 mm based on the working distance, ensuring the calibration board dominates the imaging area and avoiding background interference. The laser tracker uses an interferometric or absolute distance measurement device with a measurement range covering 0 to 40 meters and a single-point measurement accuracy better than 5 micrometers plus 0.5 micrometers per meter. Its beam emitter must be directly facing the area where the calibration board is located, ensuring that all four spherical targets are within the unobstructed field of view of the laser beam, and that the incident angle does not exceed 60 degrees to guarantee ranging accuracy. The goal of this step is to provide a high-quality data input foundation for subsequent image processing and spatial measurement.

[0025] In the above-mentioned calibration board identification and global coordinate system establishment method, step 3 involves setting camera parameters to obtain a well-exposed and clear image. 15 to 25 images of the calibration board in different poses are captured, with the width of the black and white edges in each image controlled to approximately 3 pixels. After capturing the images, they are converted to grayscale and Gaussian or median filtering is used to eliminate noise interference. Specifically, camera parameters include exposure time, gain, white balance, and focus mode, adjusted automatically or manually to ensure a uniform distribution of the image histogram, with no overexposed or underexposed areas. Different shooting poses include rotation angles around the optical axis within ±30 degrees, and pitch and yaw angles controlled within ±45 degrees to ensure feature point projection covers the entire image area, improving the convergence of the calibration algorithm and the accuracy of parameter solving. During shooting, if the system contains multiple cameras, synchronous shooting is triggered through a hardware synchronization module, with a synchronization error of less than 10 microseconds, ensuring the time consistency of multi-view image acquisition and avoiding calibration deviations introduced by motion. After image acquisition, it is first converted into an 8-bit grayscale image, and then smoothed using a 3x3 or 5x5 Gaussian filter kernel (standard deviation σ=1.0), or a 3x3 median filter is used to remove salt-and-pepper noise, providing clean image data for subsequent contour detection.

[0026] In the above-mentioned calibration board recognition and global coordinate system establishment method, step 4, for the checkerboard pattern, divides the image into black and white regions through adaptive threshold segmentation, detects contours, and uses a polygon approximation algorithm to filter out candidate regions that are approximately quadrilaterals. It verifies whether the topological structure conforms to the preset number of rows and columns and geometric dimensions. If the conditions are met, it outputs a list of rough coordinates of the interior corner points, and refines the coordinates through a sub-pixel corner optimization algorithm, recording the final corner coordinates as visual feature point information. Specifically, adaptive threshold segmentation uses a local mean or Gaussian weighted method, with a window size of 15 x 15 pixels and an offset of 2 to adapt to uneven lighting scenarios. Contour detection uses a Canny edge detector combined with chain code tracing to extract closed boundaries. Polygon approximation uses the Douglas-Peucker algorithm with an error threshold of 2 pixels to filter out candidate regions with 4 vertices. Subsequently, perspective transformation correction is performed on each candidate quadrilateral, and its internal structure is checked to ensure it contains a regular grid structure with a preset number of rows and columns (e.g., 8x11). The grid line spacing consistency error is no more than 5%, and the overall size deviates from the calibration board design value by less than 2%. If the verification passes, a rough list of interior corner coordinates is output using Harris corner detection or direct calculation based on grid intersections. Subpixel-level corner optimization employs an iterative search algorithm based on image gradients, with an initial search window of 5x5 pixels and a convergence threshold set to a coordinate change of less than 0.05 pixels. During optimization, local quadratic surface fitting is combined to further improve corner positioning accuracy, ultimately achieving a corner positioning error of less than 0.1 pixels. For Aruco code patterns, a dedicated decoding library is used for codeword recognition and pose estimation, outputting the center coordinates and direction vector of each code block, and subpixel optimization is also performed.

[0027] In the above-described method for calibration board identification and global coordinate system establishment, step 5 involves starting the laser tracker to sequentially measure the center positions of four spherical targets, obtaining their three-dimensional spatial coordinates in the laser tracker coordinate system, and calculating the theoretical positions of the feature points of the calibration board pattern in the laser tracker coordinate system based on the known geometric offsets between the spherical targets and the center of the calibration board pattern. Specifically, the laser tracker performs no less than 50 continuous samplings of the center of each spherical target, with a sampling frequency of no less than 20 Hz. After removing outliers, the arithmetic mean of the spatial coordinates is taken as the final measurement result, with a measurement standard deviation of less than 1.5 micrometers to ensure high repeatability and reliability of the spatial coordinate data. The geometric offsets are pre-calibrated using a high-precision coordinate measuring machine with a calibration accuracy better than 0.5 micrometers and stored in the system database. When called, the corresponding parameters are automatically matched according to the current calibration board number to avoid human input errors. This offset is a fixed 3D vector or a 4x4 homogeneous transformation matrix, describing the transformation relationship from the coordinate system of the center of any spherical target to the origin of the calibration board's world coordinate system (usually the geometric center of the pattern). By substituting the coordinates of the four center points into this relationship, the position and orientation of the calibration board's world coordinate system in the laser tracker's coordinate system can be deduced, and then the theoretical 3D coordinates of all visual feature points (such as checkerboard corner points) in the laser tracker's coordinate system can be calculated.

[0028] In the aforementioned calibration board recognition and global coordinate system establishment method, step 6 involves using the Zhang Zhengyou calibration algorithm to solve for the camera's intrinsic parameter matrix and the extrinsic parameter matrix corresponding to a single image, based on the corner coordinates extracted from the image and the corresponding known feature point coordinates in the calibration board's world coordinate system. Specifically, the three-dimensional coordinates of the feature points in the calibration board's world coordinate system are known constants. For example, for an 8x11 chessboard, the corner coordinates are (0,0,0), (d,0,0), (0,d,0)...(10d,7d,0), where d is the side length of the cell. These three-dimensional points are paired with their two-dimensional pixel coordinates in the image to form a system of projection equations. Zhang Zhengyou's calibration algorithm first solves for the initial extrinsic parameters using a linear method (such as DLT), then optimizes the camera intrinsic parameters using a nonlinear least squares method. These intrinsic parameters include focal lengths fx and fy, principal point coordinates cx and cy, radial distortion coefficients k1, k2, and k3, and tangential distortion coefficients p1 and p2. The objective function is to minimize the reprojection error, i.e., to minimize the sum of squared Euclidean distances between the predicted pixel positions and the actual extracted positions of all points. The iterative convergence condition is that the error rate of change is less than 0.01%, and the average error of a single calibration reprojection is less than 0.3 pixels. This step outputs the rotation matrix R and translation vector t corresponding to each image, forming the extrinsic parameter matrix.

[0029] In the above-described method for calibration board identification and global coordinate system establishment, step 7 involves substituting the relative positional relationship between the sphere center coordinates measured by the laser tracker and the feature points of the calibration board pattern into the coordinate transformation model to construct the rotation matrix and translation vector of the calibration board in the laser tracker coordinate system, thus forming a pose description of the calibration board relative to the laser tracker. Specifically, let the coordinates of the four sphere centers in the laser tracker coordinate system be P1, P2, P3, and P4, and their coordinates in the calibration board's world coordinate system be Q1, Q2, Q3, and Q4 (known). Then, the optimal rigid body transformation T_LT2Board is solved using the least squares method, such that T_LT2Board * Qi ≈ Pi. This transformation includes the rotation matrix R_LT2Board and the translation vector t_LT2Board, which together constitute the pose of the calibration board in the laser tracker coordinate system.

[0030] In the above-described method for calibration board identification and global coordinate system establishment, step 8 establishes a mathematical relationship between multi-view visual information and high-precision spatial measurement data by simultaneously combining the camera extrinsic parameter matrix and the calibration board's pose in the laser tracker coordinate system, thus calculating the final pose of the calibration board in a unified global coordinate system. Specifically, the global coordinate system is defined with the initial measurement origin of the laser tracker as the origin and the laser tracker's own coordinate system as the reference direction. For each image, the camera extrinsic parameters describe the transformation T_Cam2Board from the calibration board's world coordinate system to the camera coordinate system; while T_LT2Board obtained in step 7 describes the transformation from the calibration board's world coordinate system to the laser tracker coordinate system. Therefore, the laser tracker does not participate in solving the camera imaging model; by using the laser tracker as an intermediary, the extrinsic parameters of multiple cameras in a large field of view are calculated, and the transformation from the camera coordinate system to the laser tracker coordinate system (i.e., the global coordinate system) is T_Global = T_LT2Board * inv(T_Cam2Board). A more robust global pose estimate can be obtained by performing consistency checks and averaging on the T_Global of multiple images. This process uses a least-squares registration algorithm to spatially align the camera extrinsic parameter matrix with the laser tracker's measured pose, solves for the optimal transformation matrix, and achieves seamless fusion of multi-source data.

[0031] In the aforementioned calibration board identification and global coordinate system establishment method, step 9 utilizes the pose information of the calibration board in the global coordinate system to unify the coordinate systems between the camera and the laser tracker. The same processing flow is applied to the calibration board images acquired by multiple cameras to obtain the extrinsic parameter matrices of each camera in the global coordinate system, thus completing the collaborative calibration of the multi-camera system. Specifically, each camera independently executes steps 3 to 8, ultimately obtaining its extrinsic parameter matrix relative to the same global coordinate system. Therefore, the observation data from all cameras can be unified under this global coordinate system, achieving spatial information fusion. Furthermore, the system includes a process for repeatability verification of the calibration results. By repeatedly executing the complete calibration process 3 to 5 times within the same time period, the Euclidean distance standard deviation of each camera's extrinsic parameter matrix is ​​calculated. If the deviation exceeds a set threshold (e.g., the standard deviation of the translation vector is greater than 0.1 mm, and the standard deviation of the rotation angle is greater than 0.05 degrees), the system automatically prompts for re-acquisition of data, ensuring the stability and reliability of the calibration results. The entire calibration process can be completed within 30 minutes without the need for pre-setting manual markers, enabling joint calibration of the multi-camera system and laser tracker, significantly improving on-site detection efficiency and automation.

[0032] To verify the practical effectiveness of this invention, a measurement system comprising four industrial cameras and one laser tracker was deployed at the assembly site of a large aircraft wing. The calibration plate, made of carbon fiber, measures 400 mm x 500 mm with a 9 x 12 checkerboard grid, each cell having a side length of 30 mm, and four 19.05 mm spherical targets mounted at the corners. Calibration was performed according to the above steps, acquiring 20 images at different attitudes. Image processing took approximately 45 seconds, laser tracker measurement took approximately 30 seconds, and coordinate fusion calculation took approximately 25 seconds. Ultimately, the average reprojection error of the extrinsic parameter matrix of each camera in the global coordinate system was 0.22 pixels, and the coordinate transformation residual between the laser tracker and the vision system was less than 0.08 mm. The overall calibration accuracy reached sub-millimeter level, meeting the consistency requirements of aerospace assembly for spatial measurements. Example

[0033] This invention provides a calibration plate identification and global coordinate system establishment device, including a calibration plate, a camera, and a laser tracker. The calibration plate, camera, and laser tracker are arranged in a specific layout around the object to be measured. The formula for calculating the relative positional relationship between the camera and the laser tracker is as follows: in, To determine the pose of the pattern under the laser tracker; The 3×3 rotation matrix is ​​used to track the sphere under the laser tracker; This is the 3×1 translation vector of the tracking ball under the laser tracker; This represents the offset between the center of the sphere and the calibration pattern. To calibrate the pose of the pattern in the camera coordinate system; The camera pose is determined by the laser tracker. The camera captures an image sequence containing the calibration board and extracts feature point information. Combined with the laser tracker's measurement of the spatial position of the reference points on the calibration board, the pose of the calibration board in the global coordinate system is calculated. This unified coordinate process across multiple devices simplifies the operation, reduces manual intervention, and improves the reliability of the calibration results. Furthermore, by repeatedly acquiring images and verifying the consistency of the calibration results, the stability and accuracy of the calibration process are ensured.

[0034] In this embodiment, a calibration board identification and global coordinate system establishment device is implemented as follows: the calibration board uses an Aruco code pattern instead of a checkerboard pattern, and an online calibration mechanism for dynamic environments is introduced. Specifically, the calibration board surface is printed with 4x4 Aruco codes, each code block is 25 mm x 25 mm in size, uses ID sequences from 0 to 15, and is encoded using a Hamming distance of 3. At the assembly site of large ship sections, due to drastic changes in ambient light and vibration interference, the system adopts a high dynamic range imaging mode, with the camera exposure time automatically adjusted from 1 ms to 100 ms, and electronic shutter synchronization enabled. In the image processing stage, morphological closing operations are first used to eliminate shadow interference, and then the OpenCV Aruco module is called to perform codeword detection and pose calculation, outputting the sub-pixel coordinates of the center of each code block. The laser tracker sampling frequency is increased to 100 times, with a sampling frequency of 50 Hz, to suppress measurement jitter caused by vibration. In the coordinate fusion stage, Kalman filtering is introduced to smooth the multi-frame measurement results temporally. The state vector includes the calibration plate pose and its first derivative, and the process noise covariance is set according to the field vibration spectrum. Actual measurements show that this scheme can stably complete calibration even under conditions of varying illumination from 50 lux to 10000 lux and peak environmental vibration acceleration reaching 0.5g. The global coordinate system establishment time is reduced to 22 minutes, and the standard deviation of the extrinsic parameters of each camera is better than 0.12 mm, verifying the robustness and adaptability of this invention in extreme industrial environments.

[0035] The specific embodiments of this invention fully disclose the design of the calibration board, the layout of the camera and laser tracker, and the detailed steps of their coordinated operation, ensuring that those skilled in the art can fully implement the technical solution based on the contents of the specification. By optimizing the calibration board design and its recognition algorithm, and combining it with a laser tracker to achieve coordinate unification among multiple devices, this invention significantly improves calibration efficiency and accuracy, while avoiding dependence on marker points and reducing the workload of manual operation.

[0036] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0037] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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 and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0038] 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.

[0039] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment 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.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for identifying a unified calibration plate for multi-camera extrinsic parameters and establishing a global coordinate system, characterized in that, include: S1. Adjust the calibration board, camera and laser tracker to their initial positions according to the large field of view environment, wherein the number of cameras shall not be less than two; S2. Use the camera to acquire calibration board image sequences in the corresponding fields of view, and obtain calibration board feature point information based on the calibration board image sequences; S3. Measure the spatial position data of the reference point of the calibration plate using the laser tracker; S4. Calculate the pose of the calibration board in different camera coordinate systems based on the feature point information of the calibration board. S5. Calculate the pose of the laser tracker coordinate system corresponding to the calibration plate based on the spatial position data of the reference point. S6. Calculate the extrinsic parameters of different cameras corresponding to the same global coordinate system based on the coordinate system pose of the calibration board corresponding to different camera coordinate systems and the coordinate system pose of the laser tracker corresponding to the calibration board as the intermediate coordinate relationship. The same global coordinate system is obtained based on a common reference coordinate system. Different cameras have independent calibration relationships. The spatial position data of the reference point is not used to solve the camera imaging model parameters.

2. The method for identifying a unified calibration board for multi-camera extrinsic parameters and establishing a global coordinate system as described in claim 1, characterized in that, The step of adjusting the calibration board, camera, and laser tracker to their initial positions according to the large field-of-view environment includes: Adjust the camera position according to the large field of view environment so that the calibration board occupies a large proportion of the camera's field of view without exceeding the field of view. Obtain the corresponding reference points based on the dimensions of the calibration plate target holder and the target ball; Adjust the position of the laser tracker so that it can observe the reference point on the calibration plate without obstruction; Of these, the larger proportion is no less than 70%, and the number of reference points is 4.

3. The method for identifying a unified calibration board for multi-camera extrinsic parameters and establishing a global coordinate system as described in claim 1, characterized in that, The process of acquiring calibration board image sequences using the camera in corresponding fields of view, and obtaining calibration board feature point information based on the calibration board image sequences includes: Set the camera parameters to ensure proper exposure and sharp images; The calibration board image sequence is obtained by taking pictures based on the initial position. Each image must meet the requirement that the black and white edge width is three pixels. After converting the image to grayscale, Gaussian filtering or median filtering is used to reduce the impact of noise. For the checkerboard pattern, an adaptive threshold is used to segment the image into black and white regions, and contours are found and approximate quadrilateral contours are selected by polygon approximation. Verify whether the topology of the quadrilateral meets the set dimensions. If it does, output a list of approximate coordinates of the interior corner points. The coarse coordinates are refined to the sub-pixel level, and the final corner coordinates are recorded as feature point information of the calibration board.

4. The method for identifying a unified calibration board for multi-camera extrinsic parameters and establishing a global coordinate system as described in claim 1, characterized in that, The measurement of the reference point spatial position data of the calibration board using the laser tracker includes: Four laser tracker target mounts are installed around the calibration plate, with a spherical target embedded in the center of each target mount, the diameter of which is adapted to the laser tracker. Start the laser tracker and measure the center positions of the four spherical targets in sequence, recording their three-dimensional spatial coordinates; Based on the geometric offset between the spherical target and the center of the calibration plate pattern, the spatial position data of the reference point of the calibration plate is calculated.

5. The method for identifying a unified calibration board for multi-camera extrinsic parameters and establishing a global coordinate system as described in claim 1, characterized in that, Calculate the pose of the calibration board in different camera coordinate systems based on the feature point information of the calibration board, including: Using Zhang Zhengyou's calibration algorithm, combined with the corner coordinates extracted from the image and the feature point information of the calibration board, different camera intrinsic and extrinsic parameter matrices are generated; Substitute the relative positional relationship between the center coordinates of the sphere measured by the laser tracker and the feature points of the calibration plate pattern into the coordinate transformation formula to construct the rotation matrix and translation vector of the calibration plate in the coordinate system of the laser tracker. The poses of the calibration board in different camera coordinate systems are calculated by combining the camera extrinsic parameter matrix and the calibration board pose in the laser tracker coordinate system.

6. The method for identifying a unified calibration board for multi-camera extrinsic parameters and establishing a global coordinate system as described in claim 1, characterized in that, Calculating the pose of the calibration board in the laser tracker coordinate system based on the spatial position data of the reference point includes: The extrinsic parameter matrix of each camera in the global coordinate system is obtained using the spatial position data of the reference point. The coordinate system pose of the laser tracker corresponding to the calibration plate is established by performing consistency comparison processing using the aforementioned extrinsic parameter matrix.

7. An apparatus for the method of identifying a unified calibration board for multi-camera extrinsic parameters and establishing a global coordinate system as described in any one of claims 1-6, characterized in that, The system includes a calibration plate, a camera, and a laser tracker, which are arranged in a specific layout around the object under test. The formula for calculating the relative positional relationship between the camera and the laser tracker is as follows: in, To calibrate the pose of the pattern under the laser tracker; The 3×3 rotation matrix is ​​used to track the sphere under the laser tracker; This is the 3×1 translation vector of the tracking ball under the laser tracker; This represents the offset between the center of the sphere and the calibration pattern. To calibrate the pose of the pattern in the camera coordinate system; The pose of the camera under the laser tracker.

8. The calibration board identification and global coordinate system establishment device as described in claim 7, characterized in that, The calibration plate is made of carbon fiber material, and its surface is printed with an n-row m-column black and white checkerboard pattern or Aruco code pattern. The pattern size is designed according to actual needs. Four laser tracker target seats are provided around the calibration plate. The height of the target seats is the same as the thickness of the calibration plate to ensure that the center of the spherical target is located on the same plane.

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