Novel system splicing type surveying and mapping camera target positioning method

Abstract

The invention discloses a new system splicing type surveying and mapping camera target positioning method. The method comprises the following steps: obtaining interior orientation elements of each sub-image of a camera according to calibration parameters of a splicing type surveying and mapping camera; obtaining exterior orientation elements of the sub-images according to the POS data of the exposure points of the aerial photography operation and the conversion relation between the sub-images; establishing an overlapping relation table among the sub-images according to the image overlapping rate and the conversion relation among the sub-images; finding each pair of sub-images with an overlapping relationship according to the overlapping relationship table among the sub-images, extracting homonymy feature points in the overlapping region of the sub-images, and performing feature matching to obtain image plane coordinates of the homonymy feature points in each sub-image; and taking each sub-image of the obtained image plane coordinates as an independent camera, carrying out aerial triangulation, and carrying out combined solution by adopting beam method block adjustment to obtain a to-ground positioning result. According to the method, the precision loss caused by the splicing type surveying and mapping camera in the approximate single center projection processing process is avoided, and the earth positioning precision is improved.

Description

Technical Field

[0001] The invention belongs to the field of surveying and mapping and computer vision technology, and in particular relates to a target positioning method for a new type of splicing surveying and mapping camera. Background Art

[0002] Judging from the current practical situation, the use of multi-lens and multi-detector stitching to construct a complete large format not only meets the inevitable trend of the development of aerial mapping cameras with ultra-large image format, large field of view and high resolution, but also has obvious advantages over the method of using a single ultra-large format detector to cover the entire field of view of the camera. It has more advanced comprehensive indicators, greatly reduces the difficulty and cost of implementation, and is more cost-effective.

[0003] However, the current data processing method for stitched cameras still constructs a central projection image through image stitching, and then realizes ground positioning according to the traditional photogrammetry processing process. This process is relatively mature, but the stitched camera is not a strict single center projection in nature, and the stitching process only constructs an approximate equivalent center projection, which is bound to lose image accuracy. Summary of the invention

[0004] The technical problem solved by the present invention is: to overcome the shortcomings of the prior art and provide a new type of splicing surveying camera target positioning method, which avoids the accuracy loss caused by the approximate single-center projection process and improves the ground positioning accuracy by directly using the original image to carry out ground positioning.

[0005] The object of the present invention is achieved through the following technical solutions: a target positioning method for a new type of splicing surveying camera, comprising: obtaining the internal orientation elements of each sub-image of the camera according to the calibration parameters of the splicing surveying camera; obtaining the conversion relationship between each sub-image according to the calibration parameters of the splicing surveying camera and the internal orientation elements of each sub-image of the camera; obtaining the external orientation elements of each sub-image according to the POS data of each exposure point image of the preset aerial photography operation and the conversion relationship between each sub-image; obtaining the image overlap rate according to the POS data of each exposure point image of the preset aerial photography operation, and obtaining the overlap relationship table between each sub-image according to the image overlap rate and the conversion relationship between each sub-image; finding each pair of sub-images with an overlapping relationship according to the overlap relationship table between each sub-image, extracting the same-name feature points in the overlapping area of ​​the sub-images, and performing feature matching to obtain the image plane coordinates of the same-name feature points in each sub-image; treating each sub-image with the obtained image plane coordinates as an independent camera, performing aerial triangulation, and adopting the bundle method regional network adjustment to jointly solve to obtain the ground positioning result.

[0006] In the above-mentioned target positioning method for the new type of spliced ​​surveying and mapping camera, the calibration parameters of the spliced ​​surveying and mapping camera include lens calibration parameters and detector installation position parameters.

[0007] In the above-mentioned new type of splicing surveying and mapping camera target positioning method, the lens calibration parameters include lens focal length and distortion model.

[0008] In the above-mentioned new type of splicing surveying and mapping camera target positioning method, the detector installation position parameters include the coordinates of the camera image principal point.

[0009] In the above-mentioned new system stitching surveying camera target positioning method, the distortion model is obtained by the following formula:

[0010]

[0011] Among them, Δx is the distortion of the image point x coordinate, Δy is the distortion of the image point y coordinate, x is the image point x coordinate, y is the image point y coordinate, x0 is the image principal point x coordinate, y0 is the image principal point y coordinate, k1, k2, k3 and k n are radial distortion parameters, p1 and p2 are tangential distortion coefficients, r is the radiation distance of the actual image point, and n is the number of terms in the selected radial distortion polynomial.

[0012] In the above-mentioned new system stitching surveying camera target positioning method, the conversion relationship between each sub-image is obtained by the following formula (taking the conversion relationship between image a and image b as an example):

[0013]

[0014] Among them, λ a , b are the scaling factors of sub-image a and sub-image b relative to the reference image (the scaling factor of the reference image is 1), f a 、f b are the focal lengths of the cameras corresponding to sub-image a and sub-image b, respectively. a0 ,y a0 ) is the image plane coordinate of the principal point of sub-image a, (x b0 ,y b0 ) is the image plane coordinate of the principal point of sub-image b, (x a ,y a ) is the image plane coordinate of the image point with the same name in sub-image a, (x b ,y b ) is the image plane coordinate of the image point with the same name in sub-image b, (X a ,Y a ,Z a ) is the three-dimensional coordinate of the optical center of camera a in the camera system coordinate system, (X b ,Y b ,Z b ) is the three-dimensional coordinate of the optical center of camera b in the camera system coordinate system, are the rotation matrices between sub-image a and sub-image b and the reference image respectively.

[0015] A new type of splicing surveying camera target positioning system includes: a first module, which is used to obtain the internal orientation elements of each sub-image of the camera according to the calibration parameters of the splicing surveying camera; obtain the conversion relationship between each sub-image according to the calibration parameters of the splicing surveying camera and the internal orientation elements of each sub-image of the camera; obtain the external orientation elements of each sub-image according to the POS data of each exposure point image of the preset aerial photography operation and the conversion relationship between each sub-image; a second module, which is used to obtain the image overlap rate according to the POS data of each exposure point image of the preset aerial photography operation, and establish an overlap relationship table between each sub-image according to the image overlap rate and the conversion relationship between each sub-image; a third module, which is used to find each pair of sub-images with an overlapping relationship according to the overlap relationship table between each sub-image, extract the same-name feature points in the overlapping area of ​​the sub-images, and perform feature matching to obtain the image plane coordinates of the same-name feature points in each sub-image; a fourth module, which is used to treat each sub-image with the obtained image plane coordinates as an independent camera, perform aerial triangulation, and adopt the bundle method regional block adjustment joint solution to obtain the ground positioning result.

[0016] In the above-mentioned new type of splicing surveying and mapping camera target positioning system, the calibration parameters of the splicing surveying and mapping camera include lens calibration parameters and detector installation position parameters.

[0017] In the above-mentioned new type splicing surveying and mapping camera target positioning system, the lens calibration parameters include lens focal length and distortion model.

[0018] In the above-mentioned new type splicing surveying and mapping camera target positioning system, the detector installation position parameters include the camera image principal point coordinates.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] The present invention directly uses the original image to carry out ground positioning, thereby avoiding the accuracy loss caused by the approximate single-center projection process and improving the ground positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0022] Figure 1 It is a flow chart of a target positioning method of a new type of splicing surveying and mapping camera provided by an embodiment of the present invention;

[0023] Figure 2is a schematic diagram of image overlap provided by an embodiment of the present invention;

[0024] Figure 3 is a schematic diagram of the overlapping relationship between sub-images provided by an embodiment of the present invention;

[0025] Figure 4 is a schematic diagram of feature matching of sub-images provided by an embodiment of the present invention;

[0026] Figure 5 It is a schematic diagram of performing aerial triangulation using each sub-image as an independent camera according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to be able to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0028] Figure 1 1 is a flow chart of a target positioning method for a new type of splicing surveying and mapping camera provided by an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0029] The internal orientation elements of each sub-image of the camera are obtained according to the calibration parameters of the spliced ​​surveying camera; the conversion relationship between each sub-image is obtained according to the calibration parameters of the spliced ​​surveying camera and the internal orientation elements of each sub-image of the camera; the external orientation elements of each sub-image are obtained according to the POS data of the image at each exposure point of the aerial photography operation and the conversion relationship between each sub-image;

[0030] The image overlap rate is obtained according to the POS data of the image at each exposure point of the aerial photography operation, and the overlap relationship table between each sub-image is established according to the image overlap rate and the conversion relationship between each sub-image;

[0031] According to the overlapping relationship table between each sub-image, each pair of sub-images with overlapping relationship is found, the feature points with the same name are extracted in the overlapping area of ​​the sub-images, and feature matching is performed to obtain the image plane coordinates of the feature points with the same name in each sub-image;

[0032] Each sub-image with image plane coordinates is treated as an independent camera to perform aerial triangulation, and the bundle method regional block adjustment is used to jointly solve the ground positioning result.

[0033] The calibration parameters of the spliced ​​surveying and mapping camera include lens calibration parameters and detector installation position parameters. The lens calibration parameters include lens focal length and distortion model. The detector installation position parameters include camera image principal point coordinates.

[0034] The distortion model is obtained by the following formula:

[0035]

[0036] Among them, Δx is the distortion of the image point x coordinate, Δy is the distortion of the image point y coordinate, x is the image point x coordinate, y is the image point y coordinate, x0 is the image principal point x coordinate, y0 is the image principal point y coordinate, k1, k2, k3 and k n are radial distortion parameters, p1 and p2 are tangential distortion coefficients, r is the radiation distance of the actual image point, and n is the number of terms in the selected radial distortion polynomial.

[0037] The conversion relationship between each sub-image is obtained by the following formula (taking the conversion relationship between image a and image b as an example):

[0038]

[0039] Among them, λ a , b are the scaling factors of sub-image a and sub-image b relative to the reference image (the scaling factor of the reference image is 1), f a 、f b are the focal lengths of the cameras corresponding to sub-image a and sub-image b, respectively, (x a0 ,y a0 ) is the image plane coordinate of the principal point of sub-image a, (x b0 ,y b0 ) is the image plane coordinate of the principal point of sub-image b, (x a ,y a ) is the image plane coordinate of the image point with the same name in sub-image a, (x b ,y b ) is the image plane coordinate of the image point with the same name in sub-image b, (X a ,Y a ,Z a ) is the three-dimensional coordinate of the optical center of camera a in the camera system coordinate system, (X b ,Y b ,Z b ) is the three-dimensional coordinate of the optical center of camera b in the camera system coordinate system, are the rotation matrices between sub-image a and sub-image b and the reference image respectively. In this formula, the parameters such as camera focal length, zoom factor, image plane coordinates of the principal point, and rotation matrix between the image and the reference image are all given by the calibration parameters of the splicing surveying camera.

[0040] Specifically, the method comprises the following steps:

[0041] Step 1: Calculate the internal and external orientation elements of each camera sub-image

[0042] (1) According to the calibration parameters of the stitching type surveying camera, the internal orientation elements of each sub-image of the camera are calculated; according to the calibration parameters of the stitching type surveying camera and the internal orientation elements of each sub-image of the camera, the transformation relationship (i.e., relative position and rotation relationship) between each sub-image is obtained.

[0043] The calibration parameters of the spliced ​​mapping camera include lens calibration parameters and detector installation position parameters. The lens calibration parameters are the lens focal length f and the distortion model, such as:

[0044] Among them, k1, k2, k3 are radial distortion parameters; p1, p2 are tangential distortion coefficients; r is the radiation distance of the actual image point, that is, The detector installation position is the camera image principal point x0, y0 coordinates. The exterior orientation elements of each camera are the relative position relationship and relative attitude relationship of each camera. The geometric relationship at the imaging time satisfies the collinearity equation:

[0045]

[0046] Where x, y are the image plane coordinates of the image point; x0, y0, f are the internal orientation elements of the image; X S ,Y S ,Z S The object space coordinates of the camera station; X, Y, Z are the object space coordinates of the object point; a i ,b i ,c i (i=1,2,3) are the 9 direction cosines composed of the 3 exterior azimuth elements of the image.

[0047] The conversion relationship between each sub-image is obtained by the following formula (taking the conversion relationship between image a and image b as an example):

[0048]

[0049] Among them, λ a , b are the scaling factors of image a and image b relative to the reference image (the scaling factor of the reference image is 1), f a 、f b are the focal lengths of the cameras corresponding to image a and image b, respectively, (x a0 ,y a0 ) is the image plane coordinate of the principal point of image a, (x b0 ,y b0) is the image plane coordinate of the principal point of image b, (x a ,y a ) is the image plane coordinate of a certain image point with the same name in image a, (x b ,y b ) is the image plane coordinate of the same-name image point in image b, (X a ,Y a ,Z a ) is the three-dimensional coordinate of the optical center of camera a in the camera system coordinate system, (X b ,Y b ,Z b ) is the three-dimensional coordinate of the optical center of camera b in the camera system coordinate system, are the rotation matrices between image a and image b and the reference image respectively. In this formula, the camera focal length, zoom factor, image plane coordinates of the principal point, rotation matrix between the image and the reference image and other parameters are all given by the calibration parameters of the splicing surveying camera.

[0050] Based on this formula, the relative orientation elements of each camera relative to the reference can be calculated.

[0051] (2) Based on the POS data of the aerial photography image, combined with the transformation relationship between the sub-images (i.e., relative position and rotation relationship), the exterior orientation elements of each sub-image are calculated.

[0052] According to the results of POS calibration, the absolute imaging positions of the reference camera and each camera are calculated using the eccentric component of POS installation, and the reference camera and each imaging posture are calculated using the collimation axis calibration results and the posture angle measured by POS. Thus, the internal and external orientation elements of each camera are obtained.

[0053] Step 2: Estimate the overlap between sub-images

[0054] (1) Calculate the image overlap rate based on the POS data of the aerial photography image, such as Figure 2 As shown; according to the position and height of the shooting point obtained by POS and the parameters of the camera, the ground range corresponding to each overall image is calculated, thereby calculating the overlap rate between the overall images.

[0055] (2) According to the image overlap ratio and the conversion relationship between each sub-image, an overlap relationship table between each sub-image is established. Figure 3 As shown, the same area coverage relationship of sub-images of different images is constructed to form an overlapping relationship table of sub-images of the entire working area.

[0056] Step 3: Image connection point matching and point transfer

[0057] According to the predicted image overlap relationship, find each pair of sub-images with overlapping relationship, extract the same-name feature points in the overlapping area of ​​the sub-images, and perform feature matching to obtain the image plane coordinates of the same-name feature points in each sub-image. After the matching is completed, delete the mismatched points based on the image relative geometric relationship model. Figure 4 shown.

[0058] Step (IV): Treat each sub-image with image plane coordinates as an independent camera, perform aerial triangulation, and use bundle method block adjustment to jointly solve and obtain the ground positioning result. The ground positioning result includes the precise exterior orientation elements of each sub-image and the ground coordinates corresponding to the feature points with the same name.

[0059] like Figure 5 As shown in the figure, each sub-image is treated as an independent camera to carry out aerial triangulation of the detection area, and the bundle method regional network adjustment is used to jointly solve the precise exterior orientation elements of each image and the ground coordinates of the connection points to achieve ground positioning solution.

[0060] The method further comprises the steps of:

[0061] Step 5: Accuracy test

[0062] The positioning accuracy is assessed based on the measured ground image control points to confirm the ground positioning results.

[0063] The positioning accuracy estimation formula is as follows:

[0064]

[0065] Among them, μ X is the estimated value of the X coordinate error of the ground image control point, μ Y is the estimated error value of the Y coordinate of the image control point, μ Z is the estimated error value of the Z coordinate of the image control point, X 真实 is the X coordinate of the ground measurement of the image control point, X 平差 is the X coordinate of the image control point adjustment solution, Y 真实 is the Y coordinate of the ground measurement of the image control point, Y 平差 is the Y coordinate of the image control point adjustment solution, Z 真实 is the Z coordinate of the ground measurement of the image control point, Z 平差 is the Z coordinate of the image control point adjustment solution, and n is the number of image control points used for accuracy verification.

[0066] The concepts and methods of image feature point extraction, aerial triangulation, ground phase control points, positioning accuracy, etc. in this embodiment are common knowledge in surveying and mapping and will not be elaborated on.

[0067] The present embodiment also provides a new type of splicing surveying camera target positioning system, which includes: a first module, which is used to obtain the internal orientation elements of each sub-image of the camera according to the calibration parameters of the splicing surveying camera; obtain the conversion relationship between each sub-image according to the calibration parameters of the splicing surveying camera and the internal orientation elements of each sub-image of the camera; obtain the external orientation elements of each sub-image according to the POS data of the image at each exposure point of the aerial photography operation and the conversion relationship between each sub-image; a second module, which is used to obtain the image overlap rate according to the POS data of the image at each exposure point of the aerial photography operation, and establish an overlap relationship table between each sub-image according to the image overlap rate and the conversion relationship between each sub-image; a third module, which is used to find each pair of sub-images with an overlapping relationship according to the overlap relationship table between each sub-image, extract the same-name feature points in the overlapping area of ​​the sub-images, and perform feature matching to obtain the image plane coordinates of the same-name feature points in each sub-image; a fourth module, which is used to treat each sub-image with the image plane coordinates obtained as an independent camera, perform aerial triangulation, and adopt the bundle method regional block adjustment joint solution to obtain the ground positioning result.

[0068] This embodiment directly uses the original image to carry out ground positioning, thereby avoiding the accuracy loss caused by the approximate single-center projection process and improving the ground positioning accuracy.

[0069] Although the present invention has been disclosed as above in the form of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications to the technical solution of the present invention by using the methods and technical contents disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the protection scope of the technical solution of the present invention.

Claims

1. A new type of splicing surveying camera target positioning method, characterized in that include: According to the calibration parameters of the spliced ​​mapping camera, the internal orientation elements of each sub-image of the camera are obtained; The conversion relationship between each sub-image is obtained based on the calibration parameters of the splicing surveying camera and the internal orientation elements of each sub-image of the camera; the external orientation elements of each sub-image are obtained based on the POS data of each exposure point image of the preset aerial photography operation and the conversion relationship between each sub-image; The image overlap rate is obtained according to the POS data of the image at each exposure point of the preset aerial photography operation, and the overlap relationship table between each sub-image is obtained according to the image overlap rate and the conversion relationship between each sub-image; According to the overlapping relationship table between each sub-image, each pair of sub-images with overlapping relationship is found, the feature points with the same name are extracted in the overlapping area of ​​the sub-images, and feature matching is performed to obtain the image plane coordinates of the feature points with the same name in each sub-image; Each sub-image with image plane coordinates is treated as an independent camera to perform aerial triangulation, and the bundle method regional block adjustment is used to jointly solve the ground positioning result.

2. The target positioning method of the new type splicing surveying and mapping camera according to claim 1 is characterized by: The calibration parameters of the spliced ​​mapping camera include lens calibration parameters and detector installation position parameters.

3. The target positioning method of the new type splicing surveying and mapping camera according to claim 2 is characterized by: The lens calibration parameters include lens focal length and distortion model.

4. The target positioning method of the new type splicing surveying and mapping camera according to claim 2 is characterized in that: The detector installation position parameters include the camera image principal point coordinates.

5. The target positioning method of the new type splicing surveying and mapping camera according to claim 3 is characterized by: The distortion model is obtained by the following formula: Among them, Δx is the distortion of the image point x coordinate, Δy is the distortion of the image point y coordinate, x is the image point x coordinate, y is the image point y coordinate, x0 is the image principal point x coordinate, y0 is the image principal point y coordinate, k1, k2, k3 and k n are radial distortion parameters, p1 and p2 are tangential distortion coefficients, r is the radiation distance of the actual image point, and n is the number of terms in the selected radial distortion polynomial.

6. The target positioning method of the new type splicing surveying and mapping camera according to claim 1 is characterized by: The conversion relationship between each sub-image is obtained by the following formula: Among them, λ a , b are the scaling factors of sub-image a and sub-image b relative to the reference image, f a 、f b are the focal lengths of the cameras corresponding to sub-image a and sub-image b, respectively, (x a0 ,y a0 ) is the image plane coordinate of the principal point of sub-image a, (x b0 ,y b0 ) is the image plane coordinate of the principal point of sub-image b, (x a ,y a ) is the image plane coordinate of the image point with the same name in sub-image a, (x b ,y b ) is the image plane coordinate of the image point with the same name in sub-image b, (X a ,Y a ,Z a ) is the three-dimensional coordinate of the optical center of camera a in the camera system coordinate system, (X b ,Y b ,Z b ) is the three-dimensional coordinate of the optical center of camera b in the camera system coordinate system, are the rotation matrices between sub-image a and sub-image b and the reference image respectively.

7. A new type of splicing surveying camera target positioning system, characterized by include: The first module is used to obtain the internal orientation elements of each sub-image of the camera according to the calibration parameters of the splicing surveying and mapping camera; The conversion relationship between each sub-image is obtained based on the calibration parameters of the splicing surveying camera and the internal orientation elements of each sub-image of the camera; the external orientation elements of each sub-image are obtained based on the POS data of each exposure point image of the preset aerial photography operation and the conversion relationship between each sub-image; The second module is used to obtain the image overlap rate according to the POS data of the image of each exposure point of the preset aerial photography operation, and establish the overlap relationship table between each sub-image according to the image overlap rate and the conversion relationship between each sub-image; The third module is used to find each pair of sub-images with overlapping relationships according to the overlapping relationship table between each sub-image, extract the feature points with the same name in the overlapping area of ​​the sub-images, and perform feature matching to obtain the image plane coordinates of the feature points with the same name in each sub-image; The fourth module is used to treat each sub-image of the image plane coordinates as an independent camera to perform aerial triangulation, and use the bundle method regional network adjustment to jointly solve the ground positioning result.

8. The novel splicing surveying camera target positioning system according to claim 7 is characterized by: The calibration parameters of the spliced ​​mapping camera include lens calibration parameters and detector installation position parameters.

9. The novel splicing surveying camera target positioning system according to claim 8 is characterized by: The lens calibration parameters include lens focal length and distortion model.

10. The novel splicing surveying camera target positioning system according to claim 8 is characterized in that: The detector installation position parameters include the camera image principal point coordinates.

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