Single-frame unmanned aerial vehicle image pixel positioning method and system based on elevation map

Abstract

The application discloses a kind of single frame unmanned plane image pixel positioning method and system based on elevation map, it is related to unmanned plane image processing and geographic positioning technical field, obtain the single frame original image of unmanned plane shooting and the digital elevation map of the shooting area when unmanned plane attitude information, camera internal and external parameters, shooting;According to camera internal parameter, the pixel coordinates of target pixel in original image are converted to camera normalization plane coordinate system, and target pixel back projection point coordinates are obtained after coordinate distortion correction;According to camera external parameter and unmanned plane attitude information, camera-unmanned plane body coordinate system, body-geographic inertial reference coordinate system conversion is carried out, and the normalized ray direction vector corresponding to target pixel is obtained;Based on digital elevation map, construct ground height constraint, by iterative approximation to space point along ray direction, determine the intersection of ray and ground.The application can realize the fast and accurate conversion of single frame unmanned plane image pixel to geographic coordinate.

Description

Technical Field

[0001] This invention relates to the field of UAV image processing and geolocation technology, and in particular to a single-frame UAV image pixel positioning method and system based on elevation maps. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] As a crucial carrier of geographic information, UAV imagery relies heavily on the mapping between its pixel coordinates and the actual latitude, longitude, and altitude (LLA) coordinates of its geographic location. This mapping is fundamental for applications such as UAV mapping, environmental monitoring, and emergency rescue. However, UAV imagery only contains two-dimensional pixel coordinate information and cannot be directly mapped to geographic spatial coordinates, requiring specific technical means to achieve coordinate transformation.

[0004] In existing technologies, geospatial location calculations often rely on feature point matching from multiple image frames. This involves first using feature extraction algorithms such as Scale Invariant Feature Transform (SIFT) and Speeded Robust Feature Transform (SURF) to obtain key points from different image frames. Then, feature point matching is used to determine the pixel coordinates of the key points in each frame. Finally, triangulation is used to calculate the spatial coordinates of the key points based on the camera pose. However, this method has significant drawbacks: first, it must rely on multiple image frames and cannot perform coordinate transformation on single-frame images, limiting its applicability; second, most pixels lack salient features, making effective feature extraction and matching difficult, leading to mismatches and decreased coordinate calculation accuracy. Summary of the Invention

[0005] To address the shortcomings of the existing technologies, this invention proposes a single-frame UAV image pixel localization method and system based on an elevation map. This method does not rely on multi-frame images and feature point matching algorithms, nor does it depend on external positioning devices. It can achieve rapid and accurate conversion of single-frame UAV image pixels to geographic coordinates simply by combining UAV attitude information, camera parameters, and a digital elevation map.

[0006] In a first aspect, the present invention proposes a pixel localization method for single-frame UAV images based on elevation maps.

[0007] A single-frame UAV image pixel localization method based on elevation maps includes:

[0008] Acquire single-frame raw images captured by the drone, as well as drone attitude information, camera internal and external parameters, and digital elevation maps of the shooting area during the shooting process;

[0009] Based on the camera intrinsic parameters, the pixel coordinates of the target pixel in the original image are transformed into the camera normalized plane coordinate system. After coordinate distortion correction, the coordinates of the back projection point of the target pixel are obtained.

[0010] Based on the camera extrinsic parameters and UAV attitude information, the coordinates of the back-projection points of the target pixels are transformed into the camera-UAV body coordinate system and the body-geographic inertial reference coordinate system to obtain the normalized ray direction vector corresponding to the target pixel.

[0011] Based on digital elevation maps, surface height constraints are constructed. By iteratively approximating spatial points along the ray direction, the intersection points of the ray and the ground that satisfy the surface height constraints are determined, and the geographic coordinates corresponding to the target pixels are obtained.

[0012] A further technical solution, wherein the coordinate distortion correction is as follows:

[0013] The distortion coordinates of the target pixel back-projection points, transformed to the camera normalized plane coordinate system. , () is the initial coordinate point. The distance from the coordinate point to the coordinate center (0,0) of the camera's normalized plane coordinate system is calculated. Then, the current coordinate point is updated by combining the preset camera distortion parameters.

[0014] After multiple iterations of calculation and updates, until the deviation of the updated coordinate points is less than the set value, the coordinates of the target pixel back projection point without distortion in the camera coordinate system are obtained.

[0015] A further technical solution is to use the following formula to update the coordinate points:

[0016] ;

[0017] ;

[0018] ;

[0019] in, For the first k The coordinates of the next iteration. For the first k The distance calculated in the next iteration. 、 、 、 These are the preset camera distortion parameters.

[0020] A further technical solution involves providing the UAV attitude information, including the UAV's latitude and longitude, altitude, pitch angle, yaw angle, and roll angle. Based on the camera extrinsic parameters and the UAV attitude information, a normalized ray direction vector corresponding to the target pixel is calculated, including:

[0021] Based on the pre-calibrated fixed installation relationship between the camera and the UAV body, the transformation matrix from the camera coordinate system to the UAV body coordinate system is determined. Based on the transformation matrix, the coordinates of the back-projected points of the undistorted target pixels are transformed from the camera coordinate system to the UAV body coordinate system.

[0022] Using the location of the UAV as the origin of the UAV's coordinate system, the transformed coordinates are then transformed to the geographic inertial reference coordinate system to obtain the normalized ray direction vector corresponding to the target pixel. The geographic inertial reference coordinate system adopted is the NED coordinate system. 、 、 These represent the components of the ray direction vector along the north, east, and lower axes of the NED coordinate system.

[0023] A further technical solution involves determining the initial drone position based on its latitude, longitude, and altitude. Combining the normalized ray direction vector, the ray equation is constructed as follows:

[0024] ;

[0025] in, The length of the ray is represented by the following formula:

[0026] ;

[0027] in, This indicates the vertical distance between the drone and the target point. This represents the distance of the normalized ray direction vector on the lower axis.

[0028] A further technical solution involves constructing surface height constraints based on a digital elevation map. By iteratively approximating spatial points along the ray direction, the intersection points of the rays satisfying the surface height constraints and the ground are determined, yielding the geographic coordinates corresponding to the target pixels, including:

[0029] The initial relative height is the difference between the drone's altitude and the ground altitude directly below the drone in the digital elevation map. ;

[0030] Based on the initial relative height Perform iterative calculations, based on the... k Relative height of the next iteration perpendicular component of ray direction vector Calculate the length of the ray Combined with the drone's NED coordinates Ground candidate points are obtained by calculation using ray equations. ;

[0031] ground candidate points Convert from NED coordinates to geographic coordinates, query the corresponding ground elevation from the digital elevation map, and update the [database name missing]. k Relative height of +1 iteration ;

[0032] Repeat the relative height update process iteratively until the relative height converges. The ground candidate points obtained at this point are the real geographic coordinates corresponding to the target pixels.

[0033] Secondly, the present invention provides a single-frame UAV image pixel positioning system based on an elevation map.

[0034] A single-frame UAV image pixel localization system based on elevation maps, comprising:

[0035] The data acquisition module is used to acquire single-frame raw images captured by the drone, as well as drone attitude information, camera internal and external parameters, and digital elevation maps of the shooting area during the shooting process.

[0036] The pixel coordinate back projection and correction module is used to transform the pixel coordinates of the target pixel in the original image to the camera normalized plane coordinate system based on the camera intrinsic parameters, and obtain the coordinates of the back projection point of the target pixel after coordinate distortion correction.

[0037] The coordinate system transformation module is used to transform the coordinates of the back projection point of the target pixel into the camera-UAV body coordinate system and the body-geographic inertial reference coordinate system based on the camera extrinsic parameters and UAV attitude information, so as to obtain the normalized ray direction vector corresponding to the target pixel.

[0038] The iterative solution module is used to construct surface height constraints based on digital elevation maps. By iteratively approximating spatial points along the ray direction, it determines the intersection points of the ray and the ground that satisfy the surface height constraints, and obtains the geographic coordinates corresponding to the target pixel.

[0039] Thirdly, the present invention also provides an electronic device, comprising: a memory for storing executable instructions; and a processor for executing the executable instructions stored in the memory to implement the above-described single-frame UAV image pixel localization method based on elevation maps.

[0040] Fourthly, the present invention also provides a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-described single-frame UAV image pixel localization method based on an elevation map.

[0041] Fifthly, the present invention also provides a computer program product comprising executable instructions stored in a computer-readable storage medium; wherein, when the processor of an electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the above-mentioned single-frame UAV image pixel positioning method based on elevation map is implemented.

[0042] The above one or more technical solutions have the following beneficial effects:

[0043] This invention proposes a single-frame UAV image pixel localization method and system based on elevation maps. It does not rely on multi-frame images or feature point matching algorithms, nor on external positioning devices. It only relies on the attitude information collected by the UAV itself, camera parameters, and publicly available DEM data. Coordinate transformation can be achieved based on a single frame of UAV imagery, eliminating the need for additional external positioning equipment such as RTK, effectively reducing hardware costs. It also avoids the problems of existing technologies relying on multi-frame images and prone to misjudgment in feature point matching, making it applicable to a wider range of scenarios. Furthermore, it eliminates lens errors through camera distortion correction and combines iterative solutions based on the elevation constraints of the DEM, ensuring the accuracy of the pixel coordinate to geographic coordinate conversion. This can meet the high positioning accuracy requirements of scenarios such as surveying and precision agriculture. The calculation logic of each step is simple, with few iterations, typically converging in 5-10 iterations, enabling rapid coordinate transformation and making it suitable for real-time UAV operation scenarios.

[0044] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0045] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0046] Figure 1 This is an overall flowchart of the single-frame UAV image pixel localization method based on elevation map according to an embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram illustrating the convergence process of iteratively solving for the intersection of the target point direction ray and the ground in an embodiment of the present invention. Detailed Implementation

[0048] It should be noted that the following detailed descriptions are exemplary and are intended only to describe specific embodiments and to provide further explanation of the invention, and are not intended to limit the scope of exemplary embodiments of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0049] Example 1

[0050] To address the limitations of existing UAV image pixel localization methods, which rely on multi-frame imagery and feature point matching, resulting in restricted applicability and low coordinate positioning accuracy, this embodiment proposes a single-frame UAV image pixel localization method based on elevation maps. This method comprises five core steps: imagery and elevation data preparation, pixel coordinates to camera normalized plane back projection, coordinate distortion correction, camera coordinate system to geographic coordinate system transformation, and iterative solution of the intersection point of the target point's direction ray and the ground. Figure 1 As shown, the method specifically includes:

[0051] Step S1: Acquire a single frame of raw image captured by the drone, as well as the drone's attitude information, camera internal and external parameters, and a digital elevation map of the shooting area.

[0052] Specifically, during flight, the drone captures single-frame raw images of the captured area using onboard image sensors such as cameras. Simultaneously, it uses its onboard GNSS module to collect latitude, longitude, and altitude information, and its onboard IMU (Inertial Measurement Unit) to collect pitch, yaw, and roll angles, thus obtaining the drone's attitude information during capture. The single-frame raw images and attitude information are stored synchronously. Furthermore, the drone loads a digital elevation map (DEM) of the captured area from a geographic information system (such as a GIS platform). This DEM is used for subsequent terrain height compensation, providing altitude data support for various locations on the Earth's surface.

[0053] Step S2: Based on the camera intrinsic parameters, transform the pixel coordinates of the target pixel in the original image to the camera normalized plane coordinate system, and obtain the coordinates of the back projection point of the target pixel after coordinate distortion correction.

[0054] Specifically, each pixel in the single-frame original image obtained above has its own unique pixel coordinates. u, v ),in u Column number of pixels v This refers to the pixel row number. Since this coordinate represents the relative position on the two-dimensional plane of the image, it needs to be converted to normalized planar coordinates from the camera's perspective. The conversion process is as follows:

[0055] Camera Insider , , , These are inherent parameters of the camera, which can be obtained in advance through camera calibration. , This reflects the converted value of the lens focal length in pixels. , The pixel coordinates correspond to the center of the camera's imaging plane. Based on the camera's intrinsic parameters, the pixel coordinates are expressed using the following formula: u, v ) converted to distorted coordinates of the camera normalized plane ( , This coordinate initially reflects the spatial orientation of a pixel from the camera's perspective. The transformation formula to the camera's normalized planar coordinate system is:

[0056] ;

[0057] .

[0058] Furthermore, considering that camera lenses produce distortions (including radial and tangential distortions) during the imaging process, the normalized planar coordinates calculated above will be affected. , There is a deviation, therefore coordinate distortion correction is needed to obtain the true coordinate points. In this embodiment, an iterative method is used to eliminate distortion, that is: using the distorted coordinates of the target pixel back-projection point transformed to the camera normalized plane coordinate system (…). , Let be the initial coordinate point, which can be represented as:

[0059] ;

[0060] The distance from the coordinate point to the center (0,0) of the camera's normalized plane coordinate system can be calculated using the following formula. , is represented as:

[0061] ;

[0062] Combined with preset camera distortion parameters 、 、 、 ,in 、 The radial distortion coefficient is... 、 The tangential distortion coefficient, which can also be obtained through camera calibration, is updated using the following formula:

[0063] ;

[0064] ;

[0065] Since the distortion correction formula has no analytical solution, multiple iterations are required to eliminate the distortion effect. After multiple iterations of the above calculation and update process, the coordinate difference between two adjacent iterations gradually decreases until the deviation of the updated coordinate point is less than a set value (e.g., 0.001 pixels), at which point the iteration terminates. The coordinates obtained at this point are the distortion-free target pixel back-projection point coordinates in the camera coordinate system. The depth value of this coordinate is set by default. z =1, which only reflects spatial direction and does not include distance information.

[0066] Step S3: Based on the camera extrinsic parameters and UAV attitude information, transform the coordinates of the back-projection point of the target pixel into the camera-UAV body coordinate system and the body-geographic inertial reference coordinate system to obtain the normalized ray direction vector corresponding to the target pixel.

[0067] Specifically, this step requires two coordinate system transformations to map the view from the camera's perspective to the actual geographic view, including:

[0068] First, the camera and the drone body are fixedly installed, and their relative positions and attitudes are determined through pre-calibration. For example, the transformation matrix from the camera to the drone coordinate system can be obtained by Zhang Zhengyou's calibration method. Using this transformation matrix, the true coordinates of the back-projected points of the undistorted target pixels in the camera coordinate system can be transformed to the drone body coordinate system, so that the coordinate reference changes from the camera to the drone, and the coordinate points are represented in the drone system.

[0069] In this embodiment, the pixel is transformed from the camera coordinate system to the UAV body coordinate system, which is represented as:

[0070] ;

[0071] in, Let be the ray direction vector of the pixel in the camera coordinate system. This is the ray direction vector of the pixel in the UAV body coordinate system after pixel transformation. The rotation matrix from the calibrated camera to the body coordinate system. This is the translation matrix from the calibrated camera to the body coordinate system.

[0072] Secondly, the UAV's body coordinate system is a local coordinate system and needs to be further transformed to a universal geographic inertial reference coordinate system. This embodiment uses the Northeast-East (NED) coordinate system, which has the UAV's location as its origin, with the north, east, and down axes as the three positive directions, conforming to geographic positioning practices. Specifically, using the UAV's location as the origin of the UAV's body coordinate system, the transformed coordinates are then transformed back to the NED coordinate system to obtain the normalized ray direction vector corresponding to the target pixel. ,in, 、 、 These are the components of the ray direction vector on the north, east, and bottom axes of the NED coordinate system, respectively. This vector accurately describes the ray direction of the pixel in the real geographic space, such as "30° north of east and 10° down", but it does not yet contain distance information (i.e., it does not contain actual spatial depth).

[0073] In this embodiment, the pixels in the above-mentioned UAV body coordinate system are transformed to the NED coordinate system, and are represented as follows:

[0074] ;

[0075] ;

[0076] in, This represents the normalized ray direction vector of the pixel in the NED coordinate system. The transformation matrix is ​​used to convert the body coordinate system to the NED coordinate system, and is determined by the UAV's pitch, yaw, and roll angles.

[0077] Step S4: Construct surface height constraints based on digital elevation maps. By iteratively approximating spatial points along the ray direction, determine the intersection points of the ray and the ground that satisfy the surface height constraints, and obtain the geographic coordinates corresponding to the target pixel.

[0078] Since the spatial location of the actual target point depends on the intersection of the ray direction and the ground, the above steps only determine the spatial ray direction corresponding to the pixel point. The actual height difference between the current target point and the UAV is unknown, so the ray length cannot be directly determined. Only the height information of the UAV relative to the ground can be obtained. In order to find the intersection of the ray and the ground, that is, the actual ground position corresponding to the pixel point, considering the undulation of the ground terrain, this embodiment combines the elevation constraint of the DEM and determines the intersection point through iterative calculation. That is, based on the digital elevation model (DEM), the ground elevation constraint is constructed, and the spatial point is iteratively approximated along the ray direction to find the intersection point that satisfies the ground elevation condition.

[0079] Specifically, by scaling up the length along the normalized ray direction, candidate spatial points at different depths can be obtained. Based on the UAV's relative altitude to these points, the LLA coordinates of the point can be obtained. The relative altitude of the UAV to the target point is iteratively updated by obtaining the surface elevation values ​​corresponding to the latitude and longitude of these points from the DEM. The initial UAV NED position is... The normalized ray direction vector is Therefore, the ray equation can be expressed as:

[0080] ;

[0081] in, The length of the ray is represented by its perpendicular component:

[0082] ;

[0083] The relative height can be used to amplify the vertical component of the direction vector to the corresponding vertical distance, thereby obtaining the segment length along the ray. That is, the length of the ray. It can be calculated directly using the following formula:

[0084] ;

[0085] In the above formula, This indicates the vertical distance between the drone and the target point. This represents the distance of the normalized ray direction vector along the D-axis. Furthermore, the process for iteratively solving for the geographic coordinates of the target point based on the relative height between the UAV and the target point can be determined, as follows: Figure 2 The iterative convergence calculation process is shown, illustrating the process of obtaining points on the ray using the relative height between the UAV and the point. Points ①-④ are candidate ground points obtained sequentially during the iteration process, ultimately converging to the true intersection point ④. The calculation process for this iterative convergence is as follows:

[0086] Step S4.1: Determine the initial relative altitude. The UAV's altitude is known (from the GNSS module), i.e., the UAV's own coordinates. In the process, extract the altitude of the drone. Then, based on the drone's latitude and longitude, the elevation of the ground directly beneath the drone can be obtained by querying the DEM. The initial relative height is the difference between the drone's altitude and the ground altitude directly below the drone in the digital elevation map. This represents the initial estimated vertical distance between the drone and the ground. In this embodiment, this initial relative altitude... The 0 in parentheses serves as the starting point for the iteration, indicating the 0th iteration.

[0087] Step S4.2: Ray length calculation and ground candidate point calculation. Based on the initial relative height. Iterative calculations are performed due to the lower axis component of the ray direction vector. It reflects the direction of the ray in the vertical direction. If the value is greater than 0, it indicates that the ray extends downwards, based on the relative height of the k-th iteration. perpendicular component of ray direction vector The correspondence can be used to calculate the ray length using the following formula. The calculation formula is as follows:

[0088] .

[0089] Combined with the drone's NED coordinates Ground candidate points are obtained by calculation using ray equations. , is represented as:

[0090] .

[0091] Step S4.3: Relative Height Update. Update the ground candidate points. Convert from NED coordinates to LLA geographic coordinates, query the ground elevation corresponding to the geographic coordinates from the digital elevation map, and update the data based on the difference between the drone's altitude and the actual ground elevation. k Relative height of +1 iteration This makes the relative height closer to the actual value.

[0092] Step S4.4: Repeat the above relative height update process until the relative height converges. The ground candidate points obtained at this time are the real geographic coordinates corresponding to the target pixels.

[0093] Combination Figure 2 As shown, the above process is actually as follows: based on the calculated ray equation (which reflects the actual direction), the probe distance is calculated using the initial relative height. For example, if the drone flies in this actual direction, the vertical distance to the position reached (i.e., point ①) is the initial relative height. At this time, the ray length can be calculated using the above formula. (i.e., the test distance of the drone flight); then calculate the test point on the ground according to the test distance, and convert the coordinates of the test point into latitude, longitude and altitude; according to the latitude and longitude of the test point, look up the DEM altitude map to find the actual ground altitude of the point (the altitude of the location of point ②), and then recalculate the actual relative height between the drone and the point. For example, if the actual altitude of the test point is 120 meters and the drone altitude is 500 meters, the actual relative height is 380 meters; use this new actual relative height to recalculate the test distance, the test point and the actual ground altitude of the test point (such as calculating and determining the actual altitude corresponding to the latitude and longitude of test point ② in the next step), and then recalculate the actual relative height between the drone and the point to correct the relative height; through the above method, points ③ and ④ can be determined in turn. After multiple iterations, the deviation is gradually reduced, and the test point will converge to the actual intersection of the camera line of sight and the ground. The latitude, longitude and altitude of this point are the real geographic coordinates corresponding to the pixels in the drone image.

[0094] Example 2

[0095] This embodiment provides a single-frame UAV image pixel positioning system based on an elevation map, including:

[0096] The data acquisition module is used to acquire single-frame raw images captured by the drone, as well as drone attitude information, camera internal and external parameters, and digital elevation maps of the shooting area during the shooting process.

[0097] The pixel coordinate back projection and correction module is used to transform the pixel coordinates of the target pixel in the original image to the camera normalized plane coordinate system based on the camera intrinsic parameters, and obtain the coordinates of the back projection point of the target pixel after coordinate distortion correction.

[0098] The coordinate system transformation module is used to transform the coordinates of the back projection point of the target pixel into the camera-UAV body coordinate system and the body-geographic inertial reference coordinate system based on the camera extrinsic parameters and UAV attitude information, so as to obtain the normalized ray direction vector corresponding to the target pixel.

[0099] The iterative solution module is used to construct surface height constraints based on digital elevation maps. By iteratively approximating spatial points along the ray direction, it determines the intersection points of the ray and the ground that satisfy the surface height constraints, and obtains the geographic coordinates corresponding to the target pixel.

[0100] Example 3

[0101] This embodiment provides an electronic device, including: a memory for storing executable instructions; and a processor for executing the executable instructions stored in the memory to implement the method provided in this embodiment.

[0102] Example 4

[0103] This embodiment also provides a computer-readable storage medium storing executable instructions, which, when executed by a processor, will cause the processor to execute the method described above in this embodiment.

[0104] Example 5

[0105] This embodiment provides a computer program product including executable instructions, which are computer instructions; the executable instructions are stored in a computer-readable storage medium. When the processor of an electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the electronic device performs the method described in this embodiment.

[0106] The steps and methods involved in Embodiments 2 to 5 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0107] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0108] The above description is only a preferred embodiment of the present invention. Although the specific implementation of the present invention has been described in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that, based on the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without creative effort are still within the scope of protection of the present invention.

Claims

1. A pixel localization method for a single-frame UAV image based on an elevation map, characterized in that, include: Acquire single-frame raw images captured by the drone, as well as drone attitude information, camera internal and external parameters, and digital elevation maps of the shooting area during the shooting process; Based on the camera intrinsic parameters, the pixel coordinates of the target pixel in the original image are transformed into the camera normalized plane coordinate system. After coordinate distortion correction, the coordinates of the back projection point of the target pixel are obtained. Based on the camera extrinsic parameters and UAV attitude information, the coordinates of the back-projection points of the target pixels are transformed into the camera-UAV body coordinate system and the body-geographic inertial reference coordinate system to obtain the normalized ray direction vector corresponding to the target pixel. Based on digital elevation maps, surface height constraints are constructed. By iteratively approximating spatial points along the ray direction, the intersection points of the rays and the ground that satisfy the surface height constraints are determined, and the geographic coordinates corresponding to the target pixels are obtained. The coordinate distortion correction is as follows: The distortion coordinates of the target pixel back-projection points, transformed to the camera normalized plane coordinate system. , Let (0,0) be the initial coordinate point. Calculate the distance from this point to the center of the camera's normalized plane coordinate system (0,0), and then update the current coordinate point using preset camera distortion parameters. The update formula for the coordinate point is: ； ； ； in, For the first k The coordinates of the next iteration. For the first k The distance calculated in the next iteration. 、 、 、 These are preset camera distortion parameters; After multiple iterations of calculation and updates, until the deviation of the updated coordinate points is less than the set value, the coordinates of the target pixel back projection point without distortion in the camera coordinate system are obtained.

2. The single-frame UAV image pixel localization method based on elevation map as described in claim 1, characterized in that, The UAV attitude information includes the UAV's latitude and longitude, altitude, pitch angle, yaw angle, and roll angle. Based on the camera extrinsic parameters and the UAV attitude information, the normalized ray direction vector corresponding to the target pixel is calculated, including: Based on the pre-calibrated fixed installation relationship between the camera and the UAV body, the transformation matrix from the camera coordinate system to the UAV body coordinate system is determined. Based on the transformation matrix, the coordinates of the back-projected points of the undistorted target pixels are transformed from the camera coordinate system to the UAV body coordinate system. Using the location of the UAV as the origin of the UAV's coordinate system, the transformed coordinates are then transformed to the geographic inertial reference coordinate system to obtain the normalized ray direction vector corresponding to the target pixel. The geographic inertial reference coordinate system adopted is the NED coordinate system. 、 、 These represent the components of the ray direction vector along the north, east, and lower axes of the NED coordinate system.

3. The single-frame UAV image pixel localization method based on elevation map as described in claim 1, characterized in that, Determine the initial drone position based on the drone's latitude, longitude, and altitude. Combining the normalized ray direction vector, the ray equation is constructed as follows: ； in, This is the normalized ray direction vector corresponding to the target pixel. 、 、 These are the components of the ray direction vector along the north, east, and lower axes of the NED coordinate system, respectively. The length of the ray is represented by the following formula: ； in, This indicates the vertical distance between the drone and the target point. This represents the distance of the normalized ray direction vector on the lower axis.

4. The single-frame UAV image pixel localization method based on elevation map as described in claim 3, characterized in that, Based on digital elevation maps, surface height constraints are constructed. By iteratively approximating spatial points along the ray direction, the intersection points of the rays and the ground that satisfy the surface height constraints are determined, yielding the geographic coordinates corresponding to the target pixels, including: The initial relative height is the difference between the drone's altitude and the ground altitude directly below the drone in the digital elevation map. ; Based on the initial relative height Perform iterative calculations, based on the... k Relative height of the next iteration perpendicular component of ray direction vector Calculate the length of the ray Combined with the drone's NED coordinates Ground candidate points are obtained by calculation using ray equations. ; ground candidate points Convert from NED coordinates to geographic coordinates, query the corresponding ground elevation from the digital elevation map, and update the [database name missing]. k Relative height of +1 iteration ; Repeat the relative height update process iteratively until the relative height converges. The ground candidate points obtained at this point are the real geographic coordinates corresponding to the target pixels.

5. A single-frame UAV image pixel positioning system based on elevation maps, characterized in that, include: The data acquisition module is used to acquire single-frame raw images captured by the drone, as well as drone attitude information, camera internal and external parameters, and digital elevation maps of the shooting area during the shooting process. The pixel coordinate back projection and correction module is used to transform the pixel coordinates of the target pixel in the original image to the camera normalized plane coordinate system based on the camera intrinsic parameters, and obtain the coordinates of the target pixel back projection point after coordinate distortion correction. The coordinate system transformation module is used to transform the coordinates of the back projection point of the target pixel into the camera-UAV body coordinate system and the body-geographic inertial reference coordinate system based on the camera extrinsic parameters and UAV attitude information, so as to obtain the normalized ray direction vector corresponding to the target pixel. The iterative solution module is used to construct surface height constraints based on digital elevation maps. By iteratively approximating spatial points along the ray direction, it determines the intersection point of the ray and the ground that satisfies the surface height constraint conditions, and obtains the geographic coordinates corresponding to the target pixel. The coordinate distortion correction is as follows: The distortion coordinates of the target pixel back-projection points, transformed to the camera normalized plane coordinate system. , Let (0,0) be the initial coordinate point. Calculate the distance from this point to the center of the camera's normalized plane coordinate system (0,0), and then update the current coordinate point using preset camera distortion parameters. The update formula for the coordinate point is: ； ； ； in, For the first k The coordinates of the next iteration. For the first k The distance calculated in the next iteration. 、 、 、 These are preset camera distortion parameters; After multiple iterations of calculation and updates, until the deviation of the updated coordinate points is less than the set value, the coordinates of the target pixel back projection point without distortion in the camera coordinate system are obtained.

6. An electronic device, characterized in that, include: Memory, used to store executable instructions; The processor, when executing executable instructions stored in the memory, implements the single-frame UAV image pixel localization method based on elevation map as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The device stores executable instructions that, when executed by a processor, implement the single-frame UAV image pixel localization method based on an elevation map as described in any one of claims 1-4.

8. A computer program product, characterized in that, The computer program product includes executable instructions stored in a computer-readable storage medium; When the processor of the electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, it implements the single-frame UAV image pixel localization method based on elevation map as described in any one of claims 1-4.

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