A self-calibration method, system, medium and device for drone imaging camera

Abstract

The present invention discloses a self-calibration method, system, medium, and device for unmanned aerial vehicle (UAV) imaging cameras, relating to the technical field of camera self-calibration. The method comprises: extracting features and matching images from UAV images to construct an undirected graph #imgabs0#; generating an undirected graph #imgabs2# based on a minimum spanning tree from the undirected graph #imgabs1#; constructing an epipolar geometry cost function based on three images of a triangular structure in the undirected graph #imgabs3# and object-space points visible in all three images; selecting three seed images and an optimal image for an incremental SFM based on the undirected graph #imgabs4#, and performing an incremental unstructured bundle adjustment using the epipolar geometry cost function. During this process, weighted iterative self-calibration is performed on the camera parameters to be calibrated; utilizing GNSS-assisted absolute orientation to perform GNSS constrained bundle adjustment to further improve the self-calibration accuracy, and selecting the optimal camera parameters after self-calibration. The present invention can improve the accuracy of model reconstruction.

Description

Technical Field

[0001] The present invention relates to the field of camera self-calibration technology, and in particular to a self-calibration method, system, medium, and equipment for an unmanned aerial vehicle imaging camera. Background Art

[0002] Unmanned aerial vehicles (UAVs), a key means of collecting data for aerial photogrammetry, are widely used in emergency response, digital cities, and power inspections. However, for efficiency, UAVs typically fly back-and-forth rectangular or S-shaped patterns when collecting imagery in strips, lacking effective geometric constraints between flight strips. Furthermore, the presence of flat areas in the scene and the fact that the UAV collects data at fixed altitudes and angles create a typical degenerate configuration problem. Bundle adjustment self-calibration of UAV images collected using this flight pattern and scenario is prone to "bowl-shaped" effects, resulting in distortion and distortion in the reconstructed model, making high-precision 3D reconstruction impossible. Current mainstream methods rely on control points for constrained adjustment, but collecting control points in the field is time-consuming and labor-intensive. Furthermore, traditional bundle adjustment uses information from matching tie points between images to jointly determine camera parameters and the 3D coordinates of these tie points. However, the large number of tie point data required for matching UAV images results in a large number of unknown parameters to be jointly determined, making the calculation of the covariance matrix difficult and preventing effective weighted adjustment. Summary of the Invention

[0003] The purpose of the present invention is to solve the problem of large number of unknown parameters in UAV image reconstruction and distortion of the reconstruction model in bundle adjustment self-calibration, and to propose a UAV image camera self-calibration method, comprising the following steps:

[0004] S1. Obtain drone images, perform feature extraction and image matching on drone images, and construct an undirected graph , represents the vertices of an undirected graph, Represents the edge of an undirected graph, where the vertex of the undirected graph is each image and the edge is the matching relationship between two images;

[0005] S2, according to the undirected graph , generating an undirected graph based on the minimum spanning tree ;

[0006] S3, based on undirected graph The three images of the three vertices of the triangle structure in , and the object point P visible in all three images, construct the epipolar geometry cost function;

[0007] S4, based on undirected graph The seed three views and optimal image of the incremental SFM are selected, and the incremental unstructured bundle adjustment is performed using the epipolar geometric cost function. The camera parameters to be calibrated are subjected to weighted iterative self-calibration. GNSS-assisted absolute orientation is used to perform GNSS constrained bundle adjustment to further improve the self-calibration accuracy, and the optimal camera parameters after self-calibration are selected.

[0008] Furthermore, S2 is specifically:

[0009] Traversing an undirected graph All edges, where the edge weight is set to the number of common connection points between the images represented by adjacent vertices, and a minimum spanning tree is generated based on the edge weight, and the minimum spanning tree structure is converted into an undirected graph , traverse the undirected graph Each edge in Find and The two vertices of the middle edge At the same time, for other connected vertices V, select the optimal candidate image from the vertices V that meets the constraints of the number of connection points and intersection angles. ,Will With two vertices Connected edges are added without duplication Among them and Constitute the basic unit of epipolar geometric constraint; iterate through the newly added in the last cycle Each edge of , until no new edges are added So far, we get an undirected graph .

[0010] Furthermore, the following cost function is constructed based on the geometric relationship of the epipolar lines:

[0011]

[0012]

[0013]

[0014] in, represents the geometric cost of the epipolar lines of the two views of the i-th image and the j-th image, Represents the normalized three-dimensional coordinates of the feature point projected by the object point P in the i-th image, represents the relative translation vector between the i-th image and the j-th image, Represents the normalized three-dimensional coordinates of the feature point projected by the object point P in the jth image, represents the geometric cost of the epipolar lines of the two views of the j-th image and the k-th image, represents the relative translation vector between the j-th image and the k-th image, Represents the normalized three-dimensional coordinates of the feature point projected by the object point P in the kth image, Represents the epipolar geometric cost of the three views of the i-th, j-th, and k-th images.

[0015] Furthermore,

[0016] Using undirected graphs The connection points of the images corresponding to the vertices of the two longest sides of each triangle are used to establish the epipolar line geometric cost function of the two views, and the common connection points of the images corresponding to the three vertices of the triangle are used to construct the three-view geometric cost function.

[0017] Furthermore, based on the undirected graph Select the seed three views and optimal image of incremental SFM, specifically:

[0018] Undirected graph The triangle basic unit with the largest number of common visible connection points inside the triangle is selected as the seed three-view image of the incremental SFM for image reconstruction;

[0019] Traverse along the boundary edge of the reconstructed image triangulation structure, in the undirected graph The vertices A of the triangles that share the same edge with the boundary edge and have not been reconstructed are searched respectively, and the number of common connection points between the vertex A and the image corresponding to the boundary edge is calculated, and the vertex in the vertex A with the largest number of connection points is taken as the optimal image.

[0020] Furthermore,

[0021] The calculation formula of GNSS constrained bundle adjustment is:

[0022]

[0023] in, represents the error of GNSS constrained bundle adjustment, represents the jth weight function, Indicates that the object point Projection function projected to the image coordinate system, X represents the camera parameters, represents the two-dimensional coordinates of the j-th connection point in the image, w represents the residual weight between the GNSS position and the projection center, represents the projection center, Indicates GNSS position.

[0024] Furthermore,

[0025] The inequality constrained GNSS-assisted adjustment is expressed as:

[0026]

[0027] in, represents the error of the inequality-constrained GNSS-aided adjustment, , represents the projection center of all images, Indicates other parameters except the image projection center parameters, Indicates custom weights, and , represents the residual threshold, represents the current accumulated reprojection error, , I represents the identity matrix, Indicates GNSS position.

[0028] The present invention also provides a self-calibration system for a drone imaging camera, comprising:

[0029] The first undirected graph construction module is used to obtain drone images, perform feature extraction and image matching on drone images, and construct an undirected graph , represents the vertices of an undirected graph, Represents the edge of an undirected graph, where the vertex of the undirected graph is each image and the edge is the matching relationship between two images;

[0030] The second undirected graph construction module is used to construct an undirected graph , generating an undirected graph based on the minimum spanning tree ;

[0031] Geometric cost function building block for undirected graphs The three images of the three vertices of the triangle structure in , and the object point P visible in all three images, construct the epipolar geometry cost function;

[0032] Camera parameter self-calibration module for undirected graph-based The seed three views and optimal image of the incremental SFM are selected, and the incremental unstructured bundle adjustment is performed using the epipolar geometric cost function. The camera parameters to be calibrated are subjected to weighted iterative self-calibration. GNSS-assisted absolute orientation is used to perform GNSS constrained bundle adjustment to further improve the self-calibration accuracy, and the optimal camera parameters after self-calibration are selected.

[0033] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned self-calibration method for the drone imaging camera.

[0034] The present invention also proposes an electronic device, including a processor and a memory, wherein the processor and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes computer-readable instructions, and the processor is configured to call the computer-readable instructions to execute the above-mentioned drone imaging camera self-calibration method.

[0035] The beneficial effects brought about by the technical solution provided by the present invention are:

[0036] The present invention utilizes the epipolar geometry cost function to effectively eliminate the unknown parameters of the connection points and retains only the camera parameters as unknowns, greatly reducing the number of unknown parameters and facilitating their weighted adjustment. To address the problem of deformation and distortion in the reconstructed model, a high-precision GNSS external constraint adjustment is used to alleviate the "bowl-shaped" effect of the self-calibration of the long-range UAV imaging camera, thereby improving the accuracy of model reconstruction. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of a self-calibration method for a drone imaging camera according to an embodiment of the present invention;

[0038] Figure 2 Schematic diagram of the geometric relationship of the three-view cost function according to an embodiment of the present invention;

[0039] Figure 3 is a block diagram of an electronic device in an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0040] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0041] Explanation of terms involved in the embodiments of the present invention:

[0042] SFM (Structure from Motion) estimates the positions of 3D points given a sparse set of correspondences between multiple images and their image features. This process typically involves the simultaneous estimation of 3D geometry (structure) and camera pose (motion). Due to the potential for disorder between images and the large amount of image data from a wide variety of sources, three SFM strategies have emerged: incremental, hierarchical, and global.

[0043] The incremental SFM process begins with a pair of images, computes the epipolar geometry constraints and the essential matrix, decomposes the poses of the two cameras (rotation matrix R and displacement t), and uses triangulation to calculate a number of 3D points. This allows the two images to be used to compute an initial 3D reconstruction consisting of the two cameras and a number of 3D points. Next, using the point-n-point method (a method for finding 3D to 2D point correspondences), a third camera (a third image) is added. Using the correspondences between the computed 3D points and the newly added 2D points, the pose of the third camera is computed, and triangulation is performed. First, the pose is obtained using point-n-point method, then the 3D points are obtained using triangulation. Gradually, as more cameras (images) are added, the 3D points and their corresponding image points are gradually found.

[0044] Bundle Adjustment (BA): The core of the BA method is to minimize the reprojection error. That is, when there is a 2D point m in an image and the initial 3D coordinates P of a 3D point are calculated, the 3D point P is reprojected onto the image plane to obtain m', minimizing the error between the original image point m and the reprojected m'.

[0045] Connecting points: Connecting points are points with the same name in the overlapping parts of different images, which are used to stitch and adjust multiple images.

[0046] Intersection angle: The intersection angle refers to the angle between the rays connecting each point in the same name (connection point) and its corresponding photographic center.

[0047] Undirected graph: A graph in which every edge has no direction is called an undirected graph. In an undirected graph, the number of edges attached to a vertex is called the degree of the vertex.

[0048] Example 1: The flowchart of the self-calibration method of the drone imaging camera according to the embodiment of the present invention is as follows Figure 1 , specifically including the following steps:

[0049] S1. Obtain drone images, perform feature extraction and image matching on drone images, and construct an undirected graph , represents the vertices of an undirected graph, Represents the edge of an undirected graph, where the vertex of the undirected graph is each image and the edge is the matching relationship between two images.

[0050] The SIFT method can be used to extract features from drone images, perform image matching based on feature descriptors, connect matched images, use images as vertices, and connect the images where there is a matching relationship as edges to construct an undirected graph. .

[0051] S2, according to the undirected graph , generating an undirected graph based on the minimum spanning tree .

[0052] Specifically: traverse the undirected graph All edges, where the edge weight is set to the number of common connection points between the images represented by adjacent vertices, and a minimum spanning tree is generated based on the edge weight, and the minimum spanning tree structure is converted into an undirected graph , traverse the undirected graph Each edge in Find and The two vertices of the middle edge At the same time, for other connected vertices V, select the optimal candidate image from the vertices V that meets the constraints of the number of connection points and intersection angles. , where the intersection angle constraint should meet the requirement of being greater than 16°. With two vertices Connected edges are added without duplication Among them and Constitute the basic unit of epipolar geometric constraint; iterate through the newly added in the last cycle Each edge of , until no new edges are added So far, we get an undirected graph .

[0053] S3, based on undirected graph The three images of the three vertices of the triangle structure in , and the object point P visible in all three images, construct the epipolar line geometry cost function.

[0054] The following cost function is constructed based on the geometric relationship of the epipolar line:

[0055]

[0056]

[0057]

[0058] in, represents the geometric cost of the epipolar lines of the two views of the i-th image and the j-th image, Represents the normalized three-dimensional coordinates of the feature point projected by the object point P in the i-th image, represents the relative translation vector between the i-th image and the j-th image, Represents the normalized three-dimensional coordinates of the feature point projected by the object point P in the jth image, represents the geometric cost of the epipolar lines of the two views of the j-th image and the k-th image, represents the relative translation vector between the j-th image and the k-th image, Represents the normalized three-dimensional coordinates of the feature point projected by the object point P in the kth image, Represents the epipolar geometric cost of the three views of the i-th, j-th, and k-th images.

[0059] Using undirected graphs The connection points of the images corresponding to the vertices of the two longest sides of each triangle in the image are used to establish the epipolar geometric cost function of the two views, and the common connection points of the images corresponding to the three vertices of the triangle are used to construct the three-view geometric cost function. The geometric relationship of the three-view cost function of the embodiment of the present invention is as follows: Figure 2 As shown, 、 、 They represent the relative translation vector between the j-th and k-th images, the relative translation vector between the ith and k-th images, and the relative translation vector between the ith and j-th images respectively; 、 、 Respectively represent the rotation matrix between the jth and kth images, the rotation matrix between the ith and kth images, and the rotation matrix between the ith and jth images; 、 、 Respectively represent the normalized three-dimensional coordinates of the feature points projected by the object point P in the i-th, j-th, and k-th images; 、 、 is the scale factor. Geometric cost of the epipolar lines of the two views and Indicates that the light from the object point to the two cameras is coplanar with the translation vectors of the two cameras; three-view geometric cost Like the trifocal tensor, it can maintain scale consistency when three cameras are collinear.

[0060] S4, based on undirected graph The seed three views and optimal image of the incremental SFM are selected, and the incremental unstructured bundle adjustment is performed using the epipolar geometric cost function. The camera parameters to be calibrated are subjected to weighted iterative self-calibration. GNSS-assisted absolute orientation is used to perform GNSS constrained bundle adjustment to further improve the self-calibration accuracy, and the optimal camera parameters after self-calibration are selected.

[0061] Incremental SFM begins with an initial seed image pair, sequentially adding new optimal images and iteratively performing local and global bundle adjustments to improve the robustness and accuracy of the reconstruction. The selection of the initial seed image pair and the order in which the images are added are crucial steps in incremental bundle adjustment.

[0062] The present invention adopts the following method to select the seed three views and add the optimal image:

[0063] Seed three-view selection: undirected graph The triangle basic unit with the largest number of common visible connection points inside the triangle is selected as the seed three-view of the incremental SFM for image reconstruction.

[0064] Optimal image addition: traverse along the boundary edge of the reconstructed image triangulation structure. The boundary edge of the triangulation structure refers to the edge of the triangle in the triangulation structure that does not share the same edge with other triangles. In the algorithm, we search for the vertices A of the triangles that share the same edge as the boundary and have not been reconstructed. We then calculate the number of common connection points between vertex A and the image corresponding to that edge. Similarly, we select the vertex with the largest number of connection points from the vertex A as the optimal image. We then iteratively perform local and global adjustments, and perform weighted iterative self-calibration on the camera parameters to be calibrated.

[0065] High-precision differential GNSS positioning information can provide reliable external constraints for UAV imagery of degraded structures in long flight zones. Combined with GNSS-assisted camera self-calibration, it is an effective means to alleviate the "bowl-shaped" effect.

[0066] Estimate the rotation matrix R, translation matrix T and scaling factor s in the similarity transformation, and transform the image and the 3D connection points into the object coordinate system. Assume that the projection matrix of the image is P and the coordinates of the 3D connection points are , similarity transformation matrix , the two-dimensional point coordinates projected onto the image are ,Right now .3D point The coordinates after similarity transformation are , in order to ensure that the pixel coordinates of the three-dimensional point projection in the image remain unchanged, the projection matrix P needs to be transformed. The transformed projection matrix can be derived by the following formula .

[0067]

[0068] The absolute orientation constrained by traditional GNSS is combined with the high-precision GNSS weighted bundle adjustment. The calculation formula of the GNSS constrained bundle adjustment is:

[0069]

[0070] in, represents the error of GNSS constrained bundle adjustment, represents the jth weight function, Indicates that the object point Projection function projected to the image coordinate system, X represents the camera parameters, represents the two-dimensional coordinates of the j-th connection point in the image, w represents the residual weight between the GNSS position and the projection center, represents the projection center, Indicates GNSS position.

[0071] The present invention integrates GNSS based on the GNSS weighted bundle adjustment self-calibration and combines it with the inequality constrained bundle adjustment. The inequality constrained GNSS auxiliary adjustment is expressed as:

[0072]

[0073] in, represents the error of the inequality-constrained GNSS-aided adjustment, , represents the projection center of all images, Indicates other parameters except the image projection center parameters, Indicates custom weights, and , Indicates ratio A slightly larger residual threshold, represents the current accumulated reprojection error, , I represents the identity matrix.

[0074] Select the optimal camera parameters after self-calibration, specifically:

[0075] The above steps are used to parallelize the camera self-calibration of the sub-scenes. Due to the scene degradation problem, there are differences in the camera internal orientation parameters of different sub-scenes. Therefore, it is necessary to select the global optimal camera internal orientation parameters from the locally stable sub-scene. The reprojection error cannot directly measure the absolute orientation accuracy, but the residual difference between the projection center and the high-precision GNSS in the elevation direction can reflect the quality of the sub-model reconstruction. If there is a large error, it means that there is deformation distortion in the reconstructed sub-scene and the error of the camera internal orientation parameter is large. Therefore, the present invention uses the size of the residual difference between the reprojection center and the GNSS position information in the elevation direction to select the optimal camera parameters after self-calibration.

[0076] Through the above steps, a GNSS-constrained long-strip UAV imaging camera self-calibration method is implemented, which can robustly calibrate the long-strip structure UAV imaging camera parameters, improve the positioning and orientation accuracy of UAV 3D reconstruction of long-strip scenes, and provide accurate 3D model support for strip scene 3D reconstruction and spatial analysis applications.

[0077] Embodiment 2: The present invention also provides a self-calibration system for a drone imaging camera, comprising:

[0078] The first undirected graph construction module is used to obtain drone images, perform feature extraction and image matching on drone images, and construct an undirected graph , represents the vertices of an undirected graph, Represents the edge of an undirected graph, where the vertex of the undirected graph is each image and the edge is the matching relationship between two images;

[0079] The second undirected graph construction module is used to construct an undirected graph , generating an undirected graph based on the minimum spanning tree ;

[0080] Geometric cost function building block for undirected graphs The three images of the three vertices of the triangle structure in , and the object point P visible in all three images, construct the epipolar geometry cost function;

[0081] Camera parameter self-calibration module for undirected graph-based The seed three views and optimal image of the incremental SFM are selected, and the incremental unstructured bundle adjustment is performed using the epipolar geometric cost function. The camera parameters to be calibrated are subjected to weighted iterative self-calibration. GNSS-assisted absolute orientation is used to perform GNSS constrained bundle adjustment to further improve the self-calibration accuracy, and the optimal camera parameters after self-calibration are selected.

[0082] Example 3: In an exemplary embodiment, a computer-readable storage medium is included, which stores a computer program. When the computer program is executed by a processor, the above-mentioned drone imaging camera self-calibration method is implemented.

[0083] Example 4: Please refer to Figure 3 In an exemplary embodiment, an electronic device is also included, including at least one processor, at least one memory, and at least one communication bus.

[0084] Among them, a computer program is stored in the memory, and the computer program includes computer-readable instructions. The processor calls the computer-readable instructions stored in the memory through the communication bus to execute the above-mentioned drone imaging camera self-calibration method.

[0085] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A self-calibration method for a drone imaging camera, characterized in that: The following steps are involved: S1. Obtain drone images, perform feature extraction and image matching on drone images, and construct an undirected graph , represents the vertices of an undirected graph, Represents the edge of an undirected graph, where the vertex of the undirected graph is each image and the edge is the matching relationship between two images; S2, according to the undirected graph , generating an undirected graph based on the minimum spanning tree ; S3, based on undirected graph The three images of the three vertices of the triangle structure in , and the object point P visible in all three images, construct the epipolar geometry cost function; S4, based on undirected graph The seed three views and optimal image of the incremental SFM are selected, and the incremental unstructured bundle adjustment is performed using the epipolar geometric cost function. The camera parameters to be calibrated are subjected to weighted iterative self-calibration. GNSS-assisted absolute orientation is used to perform GNSS constrained bundle adjustment to further improve the self-calibration accuracy, and the optimal camera parameters after self-calibration are selected.

2. The self-calibration method for a drone imaging camera according to claim 1, characterized in that: S2 is specifically: Traversing an undirected graph All edges, where the edge weight is set to the number of common connection points between the images represented by adjacent vertices, and a minimum spanning tree is generated based on the edge weight, and the minimum spanning tree structure is converted into an undirected graph , traverse the undirected graph Each edge in Find and The two vertices of the middle edge At the same time, for other connected vertices V, select the optimal candidate image from the vertices V that meets the constraints of the number of connection points and intersection angles. ,Will With two vertices Connected edges are added without duplication Among them and Constitute the basic unit of epipolar geometric constraint; iterate through the newly added in the last cycle Each edge of , until no new edges are added So far, we get an undirected graph .

3. The self-calibration method for a drone imaging camera according to claim 1, characterized in that: The following cost function is constructed based on the geometric relationship of the epipolar line: in, represents the geometric cost of the epipolar line of the two views of the i-th image and the j-th image, Represents the normalized three-dimensional coordinates of the feature point projected by the object point P in the i-th image, represents the relative translation vector between the i-th image and the j-th image, Represents the normalized three-dimensional coordinates of the feature point projected by the object point P in the jth image, represents the geometric cost of the epipolar lines of the two views of the j-th image and the k-th image, represents the relative translation vector between the j-th image and the k-th image, Represents the normalized three-dimensional coordinates of the feature point projected by the object point P in the kth image, Represents the epipolar geometric cost of the three views of the i-th, j-th, and k-th images.

4. The self-calibration method for a drone imaging camera according to claim 3, characterized in that: Using undirected graphs The connection points of the images corresponding to the vertices of the two longest sides of each triangle are used to establish the epipolar line geometric cost function of the two views, and the common connection points of the images corresponding to the three vertices of the triangle are used to construct the three-view geometric cost function.

5. The self-calibration method for a drone imaging camera according to claim 1, characterized in that: Based on undirected graph Select the seed three views and optimal image of incremental SFM, specifically: Undirected graph The triangle basic unit with the largest number of common visible connection points inside the triangle is selected as the seed three-view image of the incremental SFM for image reconstruction; Traverse along the boundary edge of the reconstructed image triangulation structure, in the undirected graph The vertices A of the triangles that share the same edge with the boundary edge and have not been reconstructed are searched respectively, and the number of common connection points between the vertex A and the image corresponding to the boundary edge is calculated, and the vertex in the vertex A with the largest number of connection points is taken as the optimal image.

6. The self-calibration method for a drone imaging camera according to claim 1, characterized in that: The calculation formula of GNSS constrained bundle adjustment is: in, represents the error of GNSS constrained bundle adjustment, represents the jth weight function, Indicates that the object point Projection function projected to the image coordinate system, X represents the camera parameters, represents the two-dimensional coordinates of the j-th connection point in the image, w represents the residual weight between the GNSS position and the projection center, represents the projection center, Indicates GNSS position.

7. The self-calibration method for a drone imaging camera according to claim 1, characterized in that: The inequality constrained GNSS-assisted adjustment is expressed as: in, represents the error of the inequality-constrained GNSS-aided adjustment, , represents the projection center of all images, Indicates other parameters except the image projection center parameters, Indicates custom weights, and , represents the residual threshold, represents the current accumulated reprojection error, , I represents the identity matrix, Indicates GNSS position.

8. A self-calibration system for drone imaging cameras, characterized in that: include: The first undirected graph construction module is used to obtain drone images, perform feature extraction and image matching on drone images, and construct an undirected graph , represents the vertices of an undirected graph, Represents the edge of an undirected graph, where the vertex of the undirected graph is each image and the edge is the matching relationship between two images; The second undirected graph construction module is used to construct an undirected graph , generating an undirected graph based on the minimum spanning tree ; Geometric cost function building block for undirected graphs The three images of the three vertices of the triangle structure in , and the object point P visible in all three images, construct the epipolar geometry cost function; Camera parameter self-calibration module for undirected graph-based The seed three views and optimal image of the incremental SFM are selected, and the incremental unstructured bundle adjustment is performed using the epipolar geometric cost function. The camera parameters to be calibrated are subjected to weighted iterative self-calibration. GNSS-assisted absolute orientation is used to perform GNSS constrained bundle adjustment to further improve the self-calibration accuracy, and the optimal camera parameters after self-calibration are selected.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the processor and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes computer-readable instructions, and the processor is configured to call the computer-readable instructions to execute the method according to any one of claims 1 to 7.

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