Spherical target constrained unmanned aerial vehicle laser point cloud and image high-precision registration method

Abstract

The invention discloses a spherical target constrained unmanned aerial vehicle laser point cloud and image high-precision registration method, which comprises the following steps of: acquiring laser radar point cloud data and image data by using an unmanned aerial vehicle, arranging spherical targets according to preset point positions, and acquiring sphere center plane elevation coordinates of the spherical targets; acquiring spherical laser points of each spherical target from the laser radar point cloud data; obtaining a potential area of the spherical target, performing least square ellipse fitting in the image data according to the potential area of the spherical target, and calculating image coordinates of the sphere center of the target; taking the obtained image coordinate of the target sphere center as an observation value, taking the obtained spherical laser point as control information, and carrying out unmanned aerial vehicle image combined block adjustment constrained by the spherical normal distance and the photogrammetry sphere center back projection distance to obtain camera intrinsic parameters and image exterior orientation element optimal values; and precise registration of the unmanned aerial vehicle image and the laser radar point cloud is realized. The problem of difficulty in point cloud and image registration in a natural scene with deficient artificial features is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of laser point cloud mapping technology, and particularly relates to a method for high-precision registration of unmanned aerial vehicle laser point cloud and image based on spherical target constraint. BACKGROUND

[0002] With the development of software and hardware devices and sensor technology, airborne laser radar technology is gradually becoming an important means of spatial remote sensing data acquisition. Compared with traditional aerial photogrammetry, laser radar has the advantages of active measurement and strong penetration, and can obtain ground point coordinates in vegetation covered areas, so it has been widely used in land surveying, railway, highway, water conservancy and other engineering surveying fields. In addition, due to the efficiency of laser radar measurement, it can directly obtain the three-dimensional information of the space target, avoiding the high complexity and high time-consuming processing of traditional photogrammetry dense matching, so the timeliness of the results is greatly improved. However, airborne laser radar also has some defects, mainly including: (1) low resolution, the average point spacing of current laser point cloud is generally between 0.1m~1m, which is much lower than the ground resolution of low-altitude aerial images represented by unmanned aerial vehicles; (2) laser radar measurement cannot directly obtain color information, so it is not easy to identify part of the ground target with unobvious shape and height such as road. Therefore, the two types of data sources of laser radar point cloud and aerial image show obvious complementarity, and joint application of the two types of data can realize complementary advantages, which can quickly obtain high-precision three-dimensional coordinate information and also obtain spectral and texture information of the ground object, which will have wide application prospects. However, due to the existence of camera installation error, GNSS positioning error and other factors, the coordinate system of the laser point cloud and image data directly collected will have certain deviation, which leads to the fact that the two types of data cannot be directly combined. For urban areas, artificial ground features such as building corners, roof edge lines or wall surfaces widely exist in laser point cloud, so the two types of data can be registered. For natural or outdoor scenes lacking artificial ground features, laser point cloud can only be interpolated into a raster image according to height or intensity, and then an image registration method is used for processing, but the height accuracy of this registration method is usually low. Therefore, how to realize high-precision registration of airborne laser point cloud and image for application scenarios lacking artificial ground features is still a difficulty. SUMMARY

[0003] Therefore, the purpose of the present application is to provide a method for high-precision registration of unmanned aerial vehicle laser point cloud and image based on spherical target constraint, which solves the problems of difficult registration of point cloud and image and unguaranteed registration accuracy in natural scenes lacking artificial ground features; uses the homonymous mapping relationship of the spherical target in the unmanned aerial vehicle image and the laser radar point cloud as the image orientation control information, constructs an image area network adjustment model constrained by spherical laser points, and realizes accurate registration of unmanned aerial vehicle image and laser point cloud.

[0004] To achieve the above objectives, this invention provides a high-precision registration method for UAV laser point clouds and images constrained by a spherical target, comprising the following steps: S1. Use drones to collect lidar point cloud data and image data, set up spherical targets according to preset points, and collect the elevation coordinates of the center plane of the spherical targets. Obtain the spherical laser points of each spherical target from the lidar point cloud data. S2. Based on the spherical laser point, the potential area of ​​the spherical target is obtained. According to the potential area of ​​the spherical target, ellipse detection is performed in the image data. The boundary of the detected ellipse is fitted with least squares ellipse to obtain the coordinates of the ellipse center, the length of the semi-major axis and the semi-minor axis. The image coordinates of the target center are then calculated. S3. Using the image coordinates of the target sphere center obtained in S2 as the observation value and the spherical laser point obtained in S1 as the control information, perform joint regional network adjustment of UAV images constrained by the spherical normal distance and the photogrammetric sphere center back projection distance to obtain the optimal values ​​of camera intrinsic parameters and image exterior orientation elements, thereby achieving accurate registration between UAV images and lidar point clouds.

[0005] Further preferably, S1 also includes performing POS-assisted aerial triangulation on the lidar point cloud data and image data collected by the UAV to obtain the exterior orientation elements, camera intrinsic parameters and photogrammetric tie point coordinates of the adjusted UAV image, and using the camera intrinsic parameters to correct image distortion to obtain a distortion-free image.

[0006] More preferably, in S2, the potential region of the spherical target is obtained by following these steps: S201. Calculate the average coordinates of the spherical laser points obtained in S1, and use this as the approximate location of the spherical target. ; S202. Using exterior orientation elements and camera intrinsic parameters from UAV imagery, determine the approximate location of the spherical target. By back-projecting the target onto the image, the approximate location of the spherical target on the image can be obtained. ; S203, Approximate Location Centered on the target, the potential area of ​​the spherical target is divided according to a preset radius.

[0007] In a further preferred embodiment, based on the potential region of the spherical target obtained in S203, ellipse detection is performed, and the detected elliptical boundary is fitted with least-squares ellipse to obtain the length of the major semi-axis of the ellipse. Length of the short half-axis The coordinates of the two endpoints of the major axis of the ellipse are respectively and ,in The endpoint is closer to the principal point of the image; Assuming the exterior orientation elements of the image that can be observed for a spherical target are: focal length is Approximate location of the spherical target The coordinates are Then the coordinates of the center of the spherical target on the image are... for: In the above formula, and These are all intermediate variables for coordinate transformation.

[0008] In a further preferred embodiment, in S3, the image coordinates of the target sphere center obtained in S2 are used as the observation values, and the spherical laser points obtained in S1 are used as the control information. The joint regional network adjustment of the UAV image constrained by the spherical normal distance and the photogrammetric sphere center back projection distance is performed according to the following process: S301. Using the image coordinates of the target center obtained in S2 as the observation value, the photogrammetric coordinates of the center are calculated using the least squares forward intersection method of photogrammetry. ; S302. Construct a spherical normal distance constraint cost function to calculate the cost of the normal distance from the target laser point to the photogrammetric sphere. ; S303. Optimize the UAV image bundle method regional network adjustment constrained by the spherical normal distance and the photogrammetric sphere center back projection distance. Use the spherical target as the absolute orientation control unit of the UAV image regional network and use the connection points to maintain the geometric consistency inside the regional network. S304. Based on the optimization results and the cost value of the spherical normal phase distance obtained in S302, the overall cost function of the regional adjustment model is obtained as shown in the following formula: In the formula, This represents the total cost of regional adjustment. This represents the backprojection cost of the photogrammetric connection point. This represents the cost of backprojecting the photogrammetric sphere center onto the image. This represents the normal distance cost from the target laser point to the photogrammetric sphere.

[0009] Further preferably, in S3, the optimal values ​​of camera intrinsic parameters and image exterior orientation elements are obtained, including: performing Taylor series expansion on each term in the overall cost function of the regional adjustment model in S304, neglecting higher-order terms, obtaining the linearized error equation, and accurately solving the parameters in the error equation according to the least squares adjustment criterion to obtain the accurate exterior orientation elements and camera intrinsic parameters of the UAV image, thereby achieving overall geometric registration of UAV image and laser point cloud data.

[0010] More preferably, in S302, a spherical normal distance constraint cost function is constructed to calculate the cost of the normal distance from the target laser point to the photogrammetric sphere. ,include: Assuming the radius of the spherical target is The coordinates of the spherical point on the lidar target are The coordinates of the photogrammetric center of the target are: The formula for calculating the cost value of the laser point is as follows: The total cost of the normal distance from the target laser point to the photogrammetric sphere is as follows: In the formula: This represents the cost of the normal distance from the target laser point to the photogrammetric sphere. Indicates the first A spherical target; Indicates a spherical target The corresponding number One laser point; The cost function is the distance from the laser point on the sphere to the center of the sphere measured by photography.

[0011] More preferably, in S303, when optimizing the UAV image bundle method area network adjustment constrained by the spherical normal distance and the photogrammetric sphere center back projection distance, the optimization parameters are expressed by the following formula. in, Represents the vector of parameters to be optimized. Represents the image exterior orientation vector. This represents the object-space coordinate vector of the photogrammetric connection point. Represents the camera intrinsic parameter vector. Represents the vector of photogrammetric sphere center coordinate parameters; This represents the backprojection cost of the photogrammetric connection point. This represents the cost of backprojecting the photogrammetric sphere center onto the image. This represents the normal distance cost from the target laser point to the photogrammetric sphere.

[0012] More preferably, in S304, the cost of backprojecting the photogrammetric sphere center onto the image is... It is expressed by the following formula:

[0013] In the formula, For photogrammetry, the back projection function is used. Indicates the first The photogrammetric center of a spherical target This indicates the number of observable spheres. Zhang's images include camera interior and exterior orientation elements. Indicates the first The photogrammetric center of the sphere is at the... Observations on Zhang's images; The Euclidean distance function represents the image point.

[0014] Further preferred, the backprojection cost of photogrammetric connection points The following formula is used for calculation: (10) In the formula, For photogrammetry, the back projection function is used. Indicates the first A photogrammetric connection point, This indicates the first node that can be observed as a connection point. Zhang's images include camera interior and exterior orientation elements. Indicates the first The photogrammetric connection point at the ... Observations on Zhang's image, The Euclidean distance function represents the image point.

[0015] The high-precision registration method for UAV laser point cloud and image constrained by a spherical target disclosed in this application has at least the following advantages compared with the prior art: 1. This application achieves accurate registration of UAV imagery and LiDAR point cloud based on a spherical target. This method does not rely on artificial or natural features in the scene and is also applicable to natural or field scenes with relatively scarce textures, meeting the application requirements of high-precision registration of imagery and LiDAR point cloud in field engineering surveys and topographic mapping.

[0016] 2. This application utilizes the spherical normal constraint of the spherical target to effectively overcome the influence of point cloud density on the target extraction accuracy and registration accuracy while simultaneously achieving geometric control in the plane and elevation directions, thereby effectively improving the processing accuracy.

[0017] 3. This application designs a UAV imagery regional network adjustment model constrained by a spherical target. Based on the registration of UAV imagery and laser point cloud, it can ensure the geometric consistency within the imagery regional network, which facilitates subsequent work such as orthophoto production and topographic map production. It has strong practical application and promotion value. Attached Figure Description

[0018] Figure 1 This invention provides a flowchart illustrating a method for high-precision registration of UAV laser point clouds and images constrained by a spherical target.

[0019] Figure 2 This is a schematic diagram illustrating the constraint between the normal distance of the spherical laser point and the back projection distance of the photogrammetric sphere center according to the present invention. Detailed Implementation

[0020] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] like Figure 1 As shown, one embodiment of the present invention provides a method for high-precision registration of UAV laser point clouds and images constrained by a spherical target, comprising the following steps: S1. Using a drone to collect lidar point cloud data and image data, spherical targets are set up according to preset points, and the elevation coordinates of the center plane of the spherical targets are collected. The spherical laser points of each spherical target are obtained from the lidar point cloud data. Further, in S1, POS-assisted aerial triangulation is performed on the lidar point cloud data and image data collected by the drone to obtain the exterior orientation elements, camera intrinsic parameters and photogrammetric connection point coordinates of the adjusted drone image. Image distortion is corrected using the camera intrinsic parameters to obtain a distortion-free image.

[0022] Specifically, depending on the survey area, a certain number of spherical targets are deployed, and the elevation coordinates of the sphere's center plane are acquired using methods such as RTK, leveling, or total station surveying. UAVs are used to collect lidar point clouds and imagery, and POS-assisted aerial triangulation is performed to obtain the adjusted image exterior orientation elements, camera intrinsic parameters, and photogrammetric tie point coordinates. Image distortion is corrected using the camera intrinsic parameters to obtain distortion-free images. Laser points on the surface of the spherical targets are then obtained from the lidar point cloud through human-computer interaction or automatic detection.

[0023] S2. Based on the spherical laser point, the potential area of ​​the spherical target is obtained. According to the potential area of ​​the spherical target, ellipse detection is performed in the image data. The boundary of the detected ellipse is fitted with least squares ellipse to obtain the coordinates of the ellipse center, the length of the semi-major axis and the semi-minor axis. The image coordinates of the target center are then calculated. S2 also includes obtaining the potential region of the spherical target by following these steps: S201. Calculate the average coordinates of the spherical laser points obtained in S1, and use this as the approximate location of the spherical target. ; S202. Using exterior orientation elements and camera intrinsic parameters from UAV imagery, determine the approximate location of the spherical target. By back-projecting the target onto the image, the approximate location of the spherical target on the image can be obtained. ; S203, Approximate Location Centered on the target, the potential area of ​​the spherical target is divided according to a preset radius.

[0024] In a further preferred embodiment, based on the potential region of the spherical target obtained in S203, ellipse detection is performed, and the detected elliptical boundary is fitted with least-squares ellipse to obtain the length of the major semi-axis of the ellipse. Length of the short half-axis The coordinates of the two endpoints of the major axis of the ellipse are respectively and , where the endpoint is closer to the principal point of the image; Assuming the exterior orientation elements of the image that can be observed for a spherical target are: focal length is Approximate location of the spherical target The coordinates are Then the coordinates of the center of the spherical target on the image are... for: (1) In the above formula, and These are all intermediate variables for coordinate transformation, mainly angle variables that assist in coordinate transformation. It is the angle between the projected ray from the center of the sphere and the image plane. This is an auxiliary angle formed based on the photogrammetric rotation angle and the fitted ellipse.

[0025] In the above formula, the parameters The calculation method is as follows: (2) in, (3) in, , , These represent the three rotation angles in photogrammetry. Indicates the heading angle Indicates image rotation angle Indicates the lateral deflection angle.

[0026] parameter The calculation method is as follows: (4) and This represents the lengths of the major and minor axes of the fitted ellipse.

[0027] S3. Using the image coordinates of the target sphere center obtained in S2 as the observation value and the spherical laser point obtained in S1 as the control information, perform joint regional network adjustment of UAV images constrained by the spherical normal distance and the photogrammetric sphere center back projection distance to obtain the optimal values ​​of camera intrinsic parameters and image exterior orientation elements, thereby achieving accurate registration between UAV images and lidar point clouds.

[0028] In S3, the image coordinates of the target sphere center obtained in S2 are used as the observation values, and the spherical laser points obtained in S1 are used as the control information. The joint regional network adjustment of UAV images constrained by the spherical normal distance and the photogrammetric sphere center back projection distance is performed according to the following process: S301, Assuming there is a total A spherical target For each target, there is a set of image points where the center of the target sphere lies in the UAV image. Using the image coordinates of the target sphere center obtained in S2 as the observation value, the photogrammetric coordinates of the sphere center are calculated using the photogrammetric least squares forward intersection method. ; S302. Construct a spherical normal distance constraint cost function to calculate the cost of the normal distance from the target laser point to the photogrammetric sphere. ; like Figure 2 When the point cloud and image coordinate systems are precisely aligned, the spherical points in the laser point cloud should lie on the photogrammetric sphere, meaning the normal distance from the lidar spherical point to the photogrammetric sphere is 0. Using the normal distance from the laser point to the photogrammetric sphere as the cost, a cost equation is derived. The method is as follows: Assume the radius of the spherical target is... The coordinates of the spherical point on the lidar target are The coordinates of the photogrammetric center of the target are: The formula for calculating the cost value of the laser point is as follows: (5) The total cost of the normal distance from the target laser point to the photogrammetric sphere is as follows: (6) In the formula: This represents the cost of the distance from the spherical laser point to the photogrammetric sphere. Indicates the first A spherical target; Indicates a spherical target The corresponding number One laser point. Let be the cost function for the distance from the laser point on the sphere to the center of the sphere measured by photography. Figure 2 d p Represents the two-dimensional distance between points in the image.

[0029] S303. Optimize the UAV image bundle method regional network adjustment constrained by the spherical normal distance and the photogrammetric sphere center back projection distance. Use the spherical target as the absolute orientation control unit of the UAV image regional network and use the connection points to maintain the geometric consistency inside the regional network. More preferably, in S303, when optimizing the UAV image bundle method area network adjustment constrained by the spherical normal distance and the photogrammetric sphere center back projection distance, the optimization parameters are expressed by the following formula. (7) in, Represents the vector of parameters to be optimized. Represents the image exterior orientation vector. This represents the object-space coordinate vector of the photogrammetric connection point. Represents the camera intrinsic parameter vector. Represents the vector of photogrammetric sphere center coordinate parameters; This represents the backprojection cost of the photogrammetric connection point. This represents the cost of backprojecting the photogrammetric sphere center onto the image. This represents the normal distance cost from the target laser point to the photogrammetric sphere.

[0030] S304. Based on the optimization results and the cost value of the spherical normal phase distance obtained in S302, the overall cost function of the regional adjustment model is obtained as shown in the following formula: (8) In the formula, This represents the total cost of regional adjustment. This represents the backprojection cost of the photogrammetric connection point. This represents the cost of backprojecting the photogrammetric sphere center onto the image. This represents the normal distance cost from the target laser point to the photogrammetric sphere.

[0031] The cost of backprojecting the center of the sphere onto the image in photogrammetry It is expressed by the following formula: (9) In the formula, For photogrammetry, the back projection function is used. Indicates the first The photogrammetric center of a spherical target This indicates the number of observable spheres. Zhang's images include camera interior and exterior orientation elements. Indicates the first The photogrammetric center of the sphere is at the... Observations on the image. The Euclidean distance function represents the image point.

[0032] Further preferred, the backprojection cost of photogrammetric connection points The following formula is used for calculation: (10) In the formula, For photogrammetry, the back projection function is used. Indicates the first A photogrammetric connection point, This indicates the first node that can be observed as a connection point. Zhang's images include camera interior and exterior orientation elements. Indicates the first The photogrammetric connection point at the ... Observations on Zhang's image, The Euclidean distance function represents the image point.

[0033] exist Figure 2 In this process, the extraction point of the target center on the image is the potential area of ​​the spherical target obtained by using spherical laser points in step S2 above. Based on this potential area, ellipse detection is performed on the image data. Least-squares ellipse fitting is then performed based on the detected ellipse boundary to obtain the ellipse center coordinates, semi-major axis length, and semi-minor axis length. The image coordinates of the target center are then calculated using the fitted coordinates. The back-projection position of the photogrammetric center point on the image is the object-space coordinate of the center obtained by photogrammetric forward intersection in step S3 above. This object-space coordinate is then back-projected back onto the image, which is obtained using formula 10. The back-projection function of photogrammetry is used to calculate the back-projection. In other words, the target sphere's center, obtained from the image through ellipse fitting and geometric back-calculation, is extracted onto the image as the observed value, used for forward intersection and regional network adjustment. The back-projected position of the photogrammetric sphere's center onto the image, obtained from the object point obtained through forward intersection, is used as the theoretical value, constructed for the back-projection error term in the adjustment. The optimal UAV parameters are obtained when the error between the observed and theoretical values ​​is minimized. This allows for precise matching between UAV imagery and radar point clouds.

[0034] Further preferably, in S3, the optimal values ​​of camera intrinsic parameters and image exterior orientation elements are obtained, including: performing Taylor series expansion on each term in the overall cost function of the regional adjustment model in S304, neglecting higher-order terms, obtaining the linearized error equation, and accurately solving the parameters in the error equation according to the least squares adjustment criterion to obtain the accurate exterior orientation elements and camera intrinsic parameters of the UAV image, thereby achieving overall geometric registration of UAV image and laser point cloud data.

[0035] The linearized error equation takes the following form: (11) In the formula, , and Let represent the back projection residual vector of the photogrammetric connection point, the back projection residual vector of the photogrammetric center of the target sphere, and the normal distance residual vector from the laser point of the target to the center of the photogrammetric sphere, respectively. This represents the vector of corrections to the exterior orientation elements of the UAV imagery. Corrected vector for photogrammetric connection points. This is the camera intrinsic parameter correction vector. The correction vector for measuring the center of the target sphere in a photographic test. , , , , , and These are the first-order partial derivative matrices of the objective function with respect to each optimization parameter. , and It is a constant vector; , and The weight matrix is ​​used. Following the least squares adjustment criterion, the above parameters are precisely solved to obtain the accurate exterior orientation elements and camera intrinsic parameters of the UAV imagery, thereby achieving overall geometric registration between the UAV imagery and the laser point cloud data.

[0036] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for high-precision registration of UAV laser point clouds and images constrained by a spherical target, characterized in that, Includes the following steps: S1. Use drones to collect lidar point cloud data and image data, set up spherical targets according to preset points, and collect the elevation coordinates of the center plane of the spherical targets. Obtain the spherical laser points of each spherical target from the lidar point cloud data. S2. Based on the spherical laser point, the potential area of ​​the spherical target is obtained. According to the potential area of ​​the spherical target, ellipse detection is performed in the image data. The boundary of the detected ellipse is fitted with least squares ellipse to obtain the coordinates of the ellipse center, the length of the semi-major axis and the semi-minor axis. The image coordinates of the target center are then calculated. S3. Using the image coordinates of the target sphere center obtained in S2 as the observation value and the spherical laser point obtained in S1 as the control information, perform joint regional network adjustment of UAV images constrained by the spherical normal distance and the photogrammetric sphere center back projection distance to obtain the optimal values ​​of camera intrinsic parameters and image exterior orientation elements, thereby achieving accurate registration between UAV images and lidar point clouds.

2. The method for high-precision registration of UAV laser point clouds and images constrained by a spherical target according to claim 1, characterized in that, S1 also includes POS-assisted aerial triangulation of lidar point cloud data and image data collected by UAVs to obtain exterior orientation elements, camera intrinsic parameters and photogrammetric tie point coordinates of the adjusted UAV images. The camera intrinsic parameters are then used to correct image distortion to obtain distortion-free images.

3. The method for high-precision registration of UAV laser point clouds and images constrained by a spherical target according to claim 2, characterized in that, S2 also includes obtaining the potential region of the spherical target by following these steps: S201. Calculate the average coordinates of the spherical laser points obtained in S1, and use this as the approximate location of the spherical target. ; S202. Using exterior orientation elements and camera intrinsic parameters from UAV imagery, determine the approximate location of the spherical target. By back-projecting the target onto the image, the approximate location of the spherical target on the image can be obtained. ; S203, Approximate Location Centered on the target, the potential area of ​​the spherical target is divided according to a preset radius.

4. The method for high-precision registration of UAV laser point clouds and images constrained by a spherical target according to claim 3, characterized in that, Based on the potential spherical target region obtained in S203, ellipse detection is performed, and least-squares ellipse fitting is applied to the detected elliptical boundary to obtain the length of the ellipse's major semi-axis. Length of the short half-axis The coordinates of the two endpoints of the major axis of the ellipse are respectively and ,in The endpoint is closer to the principal point of the image; Assuming the exterior orientation elements of the image that can be observed for a spherical target are: focal length is Approximate location of the spherical target The coordinates are Then the coordinates of the center of the spherical target on the image are... for: In the above formula, and These are all intermediate variables for coordinate transformation.

5. The method for high-precision registration of UAV laser point clouds and images constrained by a spherical target according to claim 1, characterized in that, In S3, the image coordinates of the target sphere center obtained in S2 are used as the observation values, and the spherical laser points obtained in S1 are used as the control information. The joint regional network adjustment of UAV images constrained by the spherical normal distance and the photogrammetric sphere center back projection distance is performed according to the following process: S301. Using the image coordinates of the target center obtained in S2 as the observation value, the photogrammetric coordinates of the center are calculated using the least squares forward intersection method of photogrammetry. ; S302. Construct a spherical normal distance constraint cost function to calculate the cost of the normal distance from the target laser point to the photogrammetric sphere. ; S303. Optimize the UAV image bundle method regional network adjustment constrained by the spherical normal distance and the photogrammetric sphere center back projection distance. Use the spherical target as the absolute orientation control unit of the UAV image regional network and use the connection points to maintain the geometric consistency inside the regional network. S304. Based on the optimization results and the cost value of the spherical normal phase distance obtained in S302, the overall cost function of the regional adjustment model is obtained as shown in the following formula: In the formula, This represents the total cost of regional adjustment. This represents the backprojection cost of the photogrammetric connection point. This represents the cost of backprojecting the photogrammetric sphere center onto the image. This represents the normal distance cost from the target laser point to the photogrammetric sphere.

6. The method for high-precision registration of UAV laser point clouds and images constrained by a spherical target according to claim 5, characterized in that, In S3, the optimal values ​​of camera intrinsic parameters and image exterior orientation elements are obtained, including: performing Taylor series expansion on each term in the overall cost function of the regional adjustment model in S304, neglecting higher-order terms, obtaining the linearized error equation, and accurately solving the parameters in the error equation according to the least squares adjustment criterion to obtain the accurate exterior orientation elements and camera intrinsic parameters of the UAV image, thereby achieving the overall geometric registration of UAV image and laser point cloud data.

7. The method for high-precision registration of UAV laser point clouds and images constrained by a spherical target according to claim 5, characterized in that, In S302, a spherical normal distance constraint cost function is constructed to calculate the cost of the normal distance from the target laser point to the photogrammetric sphere. ,include: Assuming the radius of the spherical target is The coordinates of the spherical point on the lidar target are The coordinates of the photogrammetric center of the target are: The formula for calculating the cost value of the laser point is as follows: The total cost of the normal distance from the target laser point to the photogrammetric sphere is as follows: In the formula: This represents the cost of the normal distance from the target laser point to the photogrammetric sphere. Indicates the first A spherical target; Indicates a spherical target The corresponding number One laser point; The cost function is the distance from the laser point on the sphere to the center of the sphere measured by photography.

8. The method for high-precision registration of UAV laser point clouds and images constrained by a spherical target according to claim 5, characterized in that, In S303, when optimizing the bundle-method area network adjustment of UAV images constrained by the spherical normal distance and the photogrammetric sphere center back projection distance, the optimization parameters are expressed by the following formula. in, Represents the vector of parameters to be optimized. Represents the image exterior orientation vector. This represents the object-space coordinate vector of the photogrammetric connection point. Represents the camera intrinsic parameter vector. Represents the vector of photogrammetric sphere center coordinate parameters; This represents the backprojection cost of the photogrammetric connection point. This represents the cost of backprojecting the photogrammetric sphere center onto the image. This represents the normal distance cost from the target laser point to the photogrammetric sphere.

9. The method for high-precision registration of UAV laser point clouds and images constrained by a spherical target according to claim 5, characterized in that: In S304, the cost of backprojecting the photogrammetric sphere center onto the image. It is expressed by the following formula: In the formula, For photogrammetry, the back projection function is used. Indicates the first The photogrammetric center of a spherical target This indicates the number of observable spheres. Zhang's images include camera interior and exterior orientation elements. Indicates the first The photogrammetric center of the sphere is at the... Observations on Zhang's images; The Euclidean distance function represents the image point.

10. The method for high-precision registration of UAV laser point clouds and images constrained by a spherical target according to claim 5, characterized in that, Backprojection cost of photogrammetric connection points The following formula is used for calculation: (10) In the formula, For photogrammetry, the back projection function is used. Indicates the first A photogrammetric connection point, This indicates the first node that can be observed as a connection point. Zhang's images include camera interior and exterior orientation elements. Indicates the first The photogrammetric connection point at the ... Observations on Zhang's image, The Euclidean distance function represents the image point.

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