Road elevation information acquisition method and device based on unmanned aerial vehicle

Abstract

The invention discloses a road elevation information acquisition method and device based on an unmanned aerial vehicle, and relates to the field of photogrammetry, and the method comprises the steps: controlling the unmanned aerial vehicle to fly in a to-be-measured area according to a preset flight path, and collecting an image of the to-be-measured area through a camera carried by the unmanned aerial vehicle to obtain a first image set; calculating a horizontal route parameter and a vertical route parameter according to the first image set, and determining a corrected flight path according to the horizontal route parameter and the vertical route parameter; controlling the unmanned aerial vehicle to fly in the to-be-detected area according to the corrected flight path, and acquiring an image of the to-be-detected area through a camera carried by the unmanned aerial vehicle to obtain a second image set; and according to the first image set and the second image set, establishing an elevation digital model and an orthoimage of the to-be-detected area so as to determine elevation information of the road in the to-be-detected area. According to the invention, the collection precision and efficiency of the road elevation information are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of photogrammetry, in particular to a highway elevation information acquisition method and device based on a UAV. BACKGROUND

[0002] With the rapid development of economy, the transportation industry also develops rapidly, and a large range of highways are built and opened to traffic, but at the same time, problems such as highway detection and maintenance have appeared, especially the built and opened highway sections have obvious damage cracks, uneven road surface and other phenomena, which directly affect the service life of the highway and the driving safety of the vehicle. Therefore, it is necessary to detect, evaluate and analyze the indicators such as the road surface flatness and the road surface cracks of the highway.

[0003] At present, the detection methods of highway elevation mainly include: leveling, trigonometric leveling and barometric leveling. Among them, (1) leveling needs to manually set up a level or a total station instrument on the measured section, and the elevation of the road surface is measured by the level or the total station instrument, the measurement speed is slow, the labor cost is high, and it is generally difficult to measure the ground point elevation in mountainous areas or areas with large terrain undulations. (2) The trigonometric leveling method is a simple method to determine the height difference between two points, but due to the influence of atmospheric refraction, the measurement accuracy is low. (3) The barometric leveling method is a method for measuring the height difference between two points according to the change rule of atmospheric pressure with height, using a barometer to measure the pressure difference, but due to the influence of weather changes, the accuracy of barometric leveling is lower than that of leveling and trigonometric leveling.

[0004] In summary, a new highway elevation detection method is needed to improve the accuracy and efficiency of elevation detection. SUMMARY

[0005] The purpose of the present application is to provide a highway elevation information acquisition method and device based on a UAV, which can improve the measurement accuracy and efficiency of highway elevation information.

[0006] To achieve the above purpose, the present application provides the following solutions:

[0007] A highway elevation information acquisition method based on a UAV, comprising:

[0008] controlling the UAV to fly in the to-be-measured area according to a pre-set flight trajectory, and acquiring images of the to-be-measured area by a camera carried by the UAV to obtain a first image set;

[0009] calculating horizontal flight line parameters and vertical flight line parameters according to the first image set, and determining a corrected flight trajectory according to the horizontal flight line parameters and the vertical flight line parameters;

[0010] The unmanned aerial vehicle flies in the to-be-measured area according to the corrected flight track, and collects images of the to-be-measured area through a camera carried by the unmanned aerial vehicle to obtain a second image set;

[0011] According to the first image set and the second image set, a height digital model and an orthographic image of the to-be-measured area are established to determine the height information of the road in the to-be-measured area.

[0012] To achieve the above object, the present application further provides the following scheme:

[0013] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, the processor being connected with the unmanned aerial vehicle; the processor executes the computer program to realize the above-mentioned unmanned aerial vehicle-based road height information collection method.

[0014] According to the specific embodiments of the present application, the following technical effects are achieved: the present application collects images of the to-be-measured area through a camera carried by the unmanned aerial vehicle, without the need for manually setting up a level or a total station, thereby improving the measurement efficiency; the flight track is corrected according to the images collected in the first flight process of the unmanned aerial vehicle, so that the unmanned aerial vehicle collects images again according to the corrected flight track; the height digital model and the orthographic image of the to-be-measured area are established according to the images collected in the two flight processes of the unmanned aerial vehicle, which can reflect the height information of the to-be-measured area as a whole, thereby improving the collection accuracy and efficiency of the road height information. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0016] Figure 1 The flowchart of the unmanned aerial vehicle-based road height information collection method provided by the present application;

[0017] Figure 2 The schematic diagram of horizontal direction flight path planning;

[0018] Figure 3 The schematic diagram of vertical direction flight path planning (H0<H v );

[0019] Figure 4 The schematic diagram of vertical direction flight path planning (H0>H v );

[0020] Figure 5A schematic diagram for constructing a scale space for a scale invariant feature transform matching algorithm;

[0021] Figure 6 A schematic diagram for a feature point matching process of a scale invariant feature transform matching algorithm;

[0022] Figure 7 A schematic diagram for a process of collecting road elevation information according to the present application;

[0023] Figure 8 A schematic diagram for a process of constructing an elevation digital model and an orthographic image according to the present application. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0025] The present application aims to provide a road elevation information collection method and device based on a UAV, which collects images of a to-be-measured region based on a UAV, constructs an elevation digital model and an orthographic image of the to-be-measured region by using close-range photogrammetry technology, and improves the collection accuracy and efficiency of road elevation information.

[0026] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0027] Embodiment 1

[0028] As shown in the drawings, the present embodiment provides a road elevation information collection method based on a UAV, which comprises: Figure 1

[0029] Step 1: controlling a UAV to fly in a to-be-measured region according to a pre-set flight trajectory, and collecting images of the to-be-measured region by a camera carried by the UAV to obtain a first image set.

[0030] Step 2: calculating horizontal flight line parameters and vertical flight line parameters according to the first image set, and determining a corrected flight trajectory according to the horizontal flight line parameters and the vertical flight line parameters.

[0031] The present application adopts close-range photogrammetry technology, takes the "face" of an object as an object, acquires high-resolution images by close-range shooting, and extracts fine geographic information. The core of close-range photogrammetry lies in the planning of a three-dimensional flight line. The flight line planning plane is parallel to the surface of an object, and the form of the flight line is a "bow" shape, including a horizontal flight line and a vertical flight line.​

[0032] The horizontal flight line parameters include horizontal coordinates of the plurality of exposure points. The vertical flight line parameters include elevation values of the plurality of exposure points and a lens angle of the camera.

[0033] Further, step 2 specifically includes:

[0034] Step 21: determining a forward overlap and a lateral overlap of images in the first image set. To reconstruct the geometric relationship between images, it is necessary to ensure that the images have sufficient overlap. The overlap includes the forward overlap and the lateral overlap. The forward overlap refers to a ratio of an overlapping part of adjacent images in a flight line direction to a length of the image. The lateral overlap refers to a ratio of an overlapping edge length of adjacent images between adjacent flight lines to a width of the image.

[0035] Step 22: obtaining a horizontal field of view angle, a vertical field of view angle and a photographing distance of the camera. The field of view angle of the camera is a fixed value of the camera and determines a field of view range of the camera.

[0036] Step 23: determining a distance between two exposure points in a horizontal direction according to the horizontal field of view angle of the camera, the photographing distance of the camera and the forward overlap.

[0037] Specifically, according to the horizontal field of view angle of the camera and the photographing distance of the camera, a formula is used to calculate a coverage range in a horizontal direction of the image; wherein G x is the coverage range in the horizontal direction of the image, d is the photographing distance of the camera, fov x is the horizontal field of view angle of the camera.

[0038] According to the forward overlap and the coverage range in the horizontal direction of the image, a formula L x = P x × G x is used to calculate an overlapping edge length in the horizontal direction of the image; wherein L x is the overlapping edge length in the horizontal direction of the image, and P x is the forward overlap.

[0039] According to the coverage range in the horizontal direction of the image and the overlapping edge length in the horizontal direction of the image, a formula is used to calculate the distance between the two exposure points in the horizontal direction; wherein ΔS x is the distance between the two exposure points in the horizontal direction.

[0040] Step 24: determining horizontal coordinates of the plurality of exposure points according to the distance between the two exposure points in the horizontal direction in a trajectory planning plane.

[0041] Specifically, as Figure 2As shown, in the trajectory planning plane, along the A' to B' direction, the interval ΔS x , the horizontal coordinates of the exposure points are calculated in sequence.

[0042] Step 25: Determine the minimum safe flight height of the UAV in the to-be-tested area and the facade height of the to-be-tested area.

[0043] Unlike the horizontal route planning, in the vertical direction, it can be divided into two cases according to whether the safe distance of the UAV from the ground is greater than the facade height: one is that the minimum safe flight height of the UAV is less than the facade height; the other is that the minimum safe flight height of the UAV is greater than the facade height.

[0044] Step 26: According to the minimum safe flight height of the UAV in the to-be-tested area, the facade height of the to-be-tested area, the lateral overlap, the vertical field of view angle of the camera, and the photographing distance of the camera, determine the distance between two exposure points in the vertical direction and the lens angle of the camera.

[0045] Specifically, when the minimum safe flight height of the UAV in the to-be-tested area is less than the facade height of the to-be-tested area, according to the vertical field of view angle of the camera and the photographing distance of the camera, the formula is used to calculate the coverage range of the image in the vertical direction; wherein G y is the coverage range of the image in the vertical direction, d is the photographing distance of the camera, fov y is the vertical field of view angle of the camera.

[0046] According to the lateral overlap and the coverage range of the image in the vertical direction, the formula L y = P y × G y is used to calculate the overlapping side length of the image in the vertical direction; wherein L y is the overlapping side length of the image in the vertical direction, and P y is the lateral overlap.

[0047] According to the coverage range of the image in the vertical direction and the overlapping side length of the image in the vertical direction, the formula is used to calculate the distance between two exposure points in the vertical direction; wherein ΔS y is the distance between two exposure points in the horizontal direction.

[0048] As Figure 3 shown, when the minimum safe flight height H0 of the UAV in the to-be-tested area is less than the facade height H v of the to-be-tested area, similar to the horizontal planning, starting from H0, the flight height of the aircraft is increased by ΔS y, the coverage of the camera is calculated until the coverage exceeds the height of the facade, and the lens of the camera is always perpendicular to the facade.

[0049] As shown in Figure 4 , when the minimum safe flight height H0 of the UAV in the area to be measured is greater than the height H of the facade of the area to be measured, v , the lens angle of the camera is determined according to the minimum safe flight height and the height of the facade of the area to be measured. In order to ensure that the shooting range can cover the bottom of the object, the lens direction needs to be adjusted. At this time, the lens direction of the camera is no longer perpendicular to the facade, but forms an acute angle a0 or a1 with the facade.

[0050] Step 27: In the trajectory planning plane, the elevation values of the plurality of exposure points are determined according to the distance between the two exposure points in the vertical direction.

[0051] Specifically, in the trajectory planning plane, the elevation values of the exposure points are calculated in the vertical direction at intervals of AS y .

[0052] Step 28: The corrected flight trajectory is determined according to the horizontal coordinates of the plurality of exposure points, the elevation values of the plurality of exposure points, and the lens angle of the camera.

[0053] The horizontal coordinates of the plurality of exposure points, the elevation values of the plurality of exposure points, the lens angle of the camera, and the orientation of the UAV body are integrated to obtain the final flight path planning result.

[0054] Step 3: Control the UAV to fly in the area to be measured according to the corrected flight trajectory, and collect the images of the area to be measured through the camera carried by the UAV to obtain a second image set.

[0055] Step 4: According to the first image set and the second image set, a digital elevation model and an orthographic image of the area to be measured are established to determine the elevation information of the road in the area to be measured.

[0056] Further, step 4 specifically includes:

[0057] Step 41: Image matching is performed on the first image set and the second image set to obtain a plurality of pairs of matching feature points.

[0058] Image matching, i.e. the same name image point extraction and matching, obtains accurate ground object image point coordinates through the matching of the same ground object on different images. Image matching is the basis for generating a digital surface model, and its accuracy directly affects the subsequent process and the final result quality. The application adopts a Scale Invariant Feature Transform (SIFT) matching algorithm to perform image matching on the first image set and the second image set.

[0059] The specific method of SIFT feature point matching is as follows:

[0060] (1) Construct a scale space: first, perform scale transformation on the image: for a given image, obtain an image set of different scales of the image, and construct an image pyramid through Gaussian convolution, as shown in Figure 5 .

[0061] (2) Detect the extreme points of the scale space: the maximum and minimum values are obtained in the 26 fields of the current layer and the upper and lower two layers of the Gaussian difference (Difference of Gaussian, DOG) scale space, and then the edge response points of the key points with low contrast and unstable points are removed.

[0062] (3) Confirm the key points: in order to further refine the position of the extreme points in the image scale space, the scale space function needs to be curve-fitted, so as to improve the positioning accuracy of the key points, and improve the stability and anti-noise ability of the matching.

[0063] (4) After the key point positioning and the main direction determination, each key point has the information of scale, position and direction. In the scale space where the key point is located, take the field image of 8x8 size centered on the key point, and evenly divide it into 4 4x4 small blocks, and count the gradient histogram of 8 directions of each block. Then, the gradient histogram of 8 directions of 4x4 blocks is sorted according to the position to form a 128-dimensional feature vector. Finally, the length of the feature vector is normalized to further eliminate the influence of light and improve the stability of the feature vector.

[0064] (5) SIFT matching: the Euclidean distance between the two feature points to be matched is calculated as the matching measure. As shown in Figure 6 , first, a scale space is established, then feature point positioning is performed, then the main direction of the feature point is determined, and finally a feature descriptor is generated.

[0065] Step 42: according to the multiple pairs of matching feature points and the pre-acquired ground control point coordinates, perform aerial triangulation to obtain a preliminary ground image. The preliminary ground image includes automatically connected points with ground object texture information.

[0066] Aerial triangulation is a key step in the data solving process, and its principle is to solve the connection points obtained by matching image features, and then match the ground control point coordinates actually measured, to include all image areas into the ground coordinate system, and to obtain the exterior orientation elements of each image. The accuracy of aerial triangulation directly determines the quality of the final results. The strip method, independent model method and beam method are commonly used adjustment models for aerial triangulation, but in the actual production process, the beam adjustment model is more rigorous in theory and has higher encryption accuracy, so the present application adopts the beam method as the adjustment model for aerial triangulation.

[0067] After the aerial triangulation process is completed, the automatic connection points with ground feature texture information are generated. At the same time, the image control points are provided in the aerial triangulation process, and the image control points provide accurate coordinate information of the ground points. By matching and measuring the feature points in the image, the positioning parameters of the camera and the geometric correction parameters of the image can be determined, the accurate positioning and measurement of other ground features in the image can be realized, and the measurement accuracy and accuracy can be improved.

[0068] Step 43: According to the preliminary ground image, a relevant dense matching algorithm is used for multi-view image dense matching to obtain point cloud data of the to-be-measured region.

[0069] Multi-view image dense matching is based on the automatic connection points formed by aerial triangulation and the construction of ultra-high density point cloud according to the relevant dense matching algorithm.

[0070] Specifically, the relevant dense matching algorithm includes a structure from motion algorithm, a clustered multi-view stereo algorithm and a patch-based dense matching algorithm.

[0071] Firstly, the structure from motion algorithm (SFM) is used to restore the exterior orientation elements of the preliminary ground image to obtain the ground image with exterior orientation elements. Then, the clustered multi-view stereo algorithm (CMVS) is used to cluster and classify the ground image with exterior orientation elements to obtain clustered images. Then, the patch-based dense matching algorithm (PMVS) is used for dense matching of the clustered images to obtain the point cloud data of the to-be-measured region.

[0072] Step 44: According to the point cloud data of the to-be-measured region, a digital elevation model (DEM) of the to-be-measured region is generated.

[0073] Specifically, before processing, the point cloud data usually needs to be preprocessed, including removing outliers, reducing sampling density, noise filtering, etc., to ensure the quality and reliability of the point cloud data.

[0074] Ground extraction is performed according to the point cloud data of the to-be-measured region, and a plurality of ground points are obtained. Ground extraction is a key step for generating DEM. Common methods include height threshold-based method, plane cutting method, ground smoothing method, etc. These methods identify and extract ground points according to height information in the point cloud, and delete non-ground points to ensure that only ground points participate in DEM generation.

[0075] The plurality of ground points are connected and interpolated to generate an elevation digital model of the to-be-measured region. After the ground points are extracted, the ground points need to be connected and interpolated to generate a continuous elevation model. The commonly used method is to use a triangular mesh to connect the ground points into a seamless triangular mesh structure. Then, through an interpolation algorithm (such as triangular mesh interpolation, inverse distance weighted interpolation, etc.), the elevation values of the non-ground regions in the mesh are estimated according to the height values of the ground points.

[0076] Step 45: registering the preliminary ground image and the elevation digital model to obtain a registered image. Specifically, the original image and the DEM need to be accurately registered and aligned in space. An image registration algorithm is used to correct the image according to the geometric information of the DEM, eliminating the tilt and deformation of the ground.

[0077] Step 46: geometrically correcting the registered image according to the terrain information of the elevation digital model to obtain a preliminary orthographic image. Orthographic images require all pixels to have uniform resolution on the viewing plane, which means that each pixel should have a vertical viewing angle. Geometric correction of the image can adjust the position and shape of the pixels according to the terrain information of the DEM, so that the orthographic image maintains accurate proportions and shapes in space.

[0078] Step 47: color correcting the preliminary orthographic image to obtain an orthographic image of the to-be-measured region. After generating the preliminary orthographic image, color correction may be needed to ensure color balance and consistency of the image. This involves adjusting brightness, contrast, correcting lighting and shadows, etc. to make the orthographic image more realistic and consistent in appearance.

[0079] As shown in Figure 7 The present application involves rough collection and fine collection in the process of collecting highway elevation information. In the process of rough collection, the unmanned aerial vehicle collects low-resolution images by conventional flight, and performs photogrammetry processing on the low-resolution images; in the process of fine collection, the initial terrain is obtained according to the processing results of the low-resolution images, and then three-dimensional flight planning is performed to enable the unmanned aerial vehicle to fly intelligently close to the ground, collect high-resolution images, and perform photogrammetry processing on the high-resolution images. Finally, an elevation digital model and an orthographic image are constructed to realize the collection of highway elevation information in the to-be-measured region.

[0080] Further as Figure 8 As shown, first, a task is proposed, then a route is designed, the unmanned aerial vehicle goes out to collect, the quality of the image is checked, if the image is unqualified, it is re-collected, if the image is qualified, image processing is performed to generate an elevation digital model and an orthographic image.

[0081] The present application carries a camera to collect the image of the to-be-measured region by the unmanned aerial vehicle, without manually erecting a level or a total station, thereby improving the measurement efficiency, adopting the close-to-photogrammetry technology to construct the elevation digital model and the orthographic image of the to-be-measured region, which can overall reflect the elevation information of the to-be-measured region, thereby improving the collection accuracy and efficiency of the highway elevation information.

[0082] Embodiment 2

[0083] A computer device comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the processor is connected with the unmanned aerial vehicle, and the processor executes the computer program to realize the unmanned aerial vehicle-based highway elevation information collection method in embodiment 1.

[0084] Embodiment 3

[0085] A computer readable storage medium, which stores a computer program, the computer program is executed by a processor to realize the unmanned aerial vehicle-based highway elevation information collection method in embodiment 1.

[0086] Embodiment 4

[0087] A computer program product comprises a computer program, the computer program is executed by a processor to realize the unmanned aerial vehicle-based highway elevation information collection method in embodiment 1.

[0088] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0089] In the present application, all actions of obtaining signals, information or data are carried out under the premise of complying with the corresponding data protection regulations and policies of the country where the device is located, and under the premise of obtaining authorization from the corresponding device owner.

[0090] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0091] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0092] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

[0093] The principles and implementation modes of the present application are described by applying specific examples herein, and the above-mentioned embodiments are only used to help understand the method and its core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range can be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A method for collecting highway elevation information based on unmanned aerial vehicles (UAVs), characterized in that, The method for collecting highway elevation information based on unmanned aerial vehicles (UAVs) includes: The drone is controlled to fly within the test area according to a pre-set flight trajectory, and images of the test area are collected by the camera carried by the drone to obtain a first image set; Based on the first image set, calculate the horizontal and vertical flight path parameters, and determine the corrected flight trajectory based on the horizontal and vertical flight path parameters. The drone is controlled to fly within the test area according to the corrected flight trajectory, and images of the test area are collected by the camera carried by the drone to obtain a second image set; Based on the first image set and the second image set, an elevation digital model and orthophoto of the area to be measured are established to determine the elevation information of the highways in the area to be measured.

2. The method for collecting highway elevation information based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The horizontal flight path parameters include the horizontal coordinates of multiple exposure points; the vertical flight path parameters include the elevation values ​​of multiple exposure points and the lens angle of the camera. Based on the first image set, horizontal and vertical flight path parameters are calculated, and the corrected flight trajectory is determined based on the horizontal and vertical flight path parameters, specifically including: Determine the forward overlap and lateral overlap of the images in the first image set; Obtain the horizontal field of view, vertical field of view, and shooting distance of the camera; The distance between two exposure points in the horizontal direction is determined based on the horizontal field of view of the camera, the shooting distance of the camera, and the heading overlap. Within the trajectory planning plane, the horizontal coordinates of multiple exposure points are determined based on the distance between two exposure points in the horizontal direction; Determine the minimum safe flight altitude of the UAV within the area to be measured and the elevation of the area to be measured; Based on the minimum safe flight altitude of the UAV in the area to be tested, the elevation of the area to be tested, the lateral overlap, the vertical field of view of the camera, and the shooting distance of the camera, the distance between two exposure points in the vertical direction and the lens angle of the camera are determined. Within the trajectory planning plane, the elevation values ​​of multiple exposure points are determined based on the distance between two exposure points in the vertical direction; The corrected flight trajectory is determined based on the horizontal coordinates of multiple exposure points, the elevation values ​​of multiple exposure points, and the lens angle of the camera.

3. The method for collecting highway elevation information based on unmanned aerial vehicles (UAVs) according to claim 2, characterized in that, Based on the camera's horizontal field of view, the camera's shooting distance, and the heading overlap, the distance between two exposure points in the horizontal direction is determined, specifically including: Based on the horizontal field of view of the camera and the shooting distance of the camera, the formula is used. Calculate the horizontal coverage area of ​​the image; where G x d represents the horizontal coverage area of ​​the image, fov represents the shooting distance of the camera, and fov represents the horizontal coverage area of ​​the image. x The horizontal field of view of the camera; Based on the heading overlap and the horizontal coverage of the image, formula L is used. x =P x ×G x Calculate the horizontal overlap length of the image; where L x P is the horizontal overlap length of the image. x For heading overlap; Based on the horizontal coverage area of ​​the image and the horizontal overlap length of the image, the formula ΔS is used. x =G x -L x Calculate the distance between two exposure points in the horizontal direction; where ΔS x This represents the distance between two exposure points in the horizontal direction.

4. The method for collecting highway elevation information based on unmanned aerial vehicles (UAVs) according to claim 2, characterized in that, Based on the minimum safe flight altitude of the UAV within the test area, the elevation of the test area, the lateral overlap, the vertical field of view of the camera, and the camera's shooting distance, the distance between two exposure points in the vertical direction and the lens angle of the camera are determined, specifically including: When the minimum safe flight altitude of the UAV within the test area is less than the elevation of the test area, the formula is used based on the vertical field of view of the camera and the camera's shooting distance. Calculate the vertical coverage area of ​​the image; where G y d represents the vertical coverage area of ​​the image, fov represents the shooting distance of the camera, and fov represents the vertical coverage area of ​​the image. y The vertical field of view of the camera; Based on the lateral overlap and the vertical coverage of the image, formula L is used. y =P y ×G y Calculate the vertical overlap length of the image; where L y P is the length of the overlapping side in the vertical direction of the image. y Lateral overlap; Based on the vertical coverage area of ​​the image and the vertical overlap length of the image, the formula ΔS is used. y =G y -L y Calculate the distance between two exposure points in the vertical direction; where ΔS y This is the distance between two exposure points in the horizontal direction; When the minimum safe flight altitude of the UAV in the area to be measured is greater than the elevation of the area to be measured, the camera lens angle is determined based on the minimum safe flight altitude and the elevation of the area to be measured.

5. The method for collecting highway elevation information based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, Based on the first image set and the second image set, an elevation digital model and orthophoto of the area to be measured are established, specifically including: Image matching is performed on the first image set and the second image set to obtain multiple pairs of matching feature points; Based on multiple pairs of matching feature points and pre-collected ground control point coordinates, aerial triangulation is performed to obtain preliminary ground images; the preliminary ground images include automatically connected points with ground feature texture information; Based on the preliminary ground imagery, a correlation dense matching algorithm is used to perform multi-view image dense matching to obtain point cloud data of the area to be tested. Based on the point cloud data of the area to be measured, generate an elevation digital model of the area to be measured; The preliminary ground image is registered with the digital elevation model to obtain a registered image; Based on the terrain information of the elevation digital model, the registered image is geometrically corrected to obtain a preliminary orthophoto image; Color correction is performed on the preliminary orthophoto to obtain the orthophoto of the area to be tested.

6. The method for collecting highway elevation information based on unmanned aerial vehicles according to claim 5, characterized in that, The scale-invariant feature transformation matching algorithm is used to perform image matching on the first image set and the second image set.

7. The method for collecting highway elevation information based on unmanned aerial vehicles (UAVs) according to claim 5, characterized in that, Aerial triangulation was performed using the bundle method.

8. The method for collecting highway elevation information based on unmanned aerial vehicles (UAVs) according to claim 5, characterized in that, The relevant dense matching algorithms include motion recovery structure algorithm, multi-view clustering algorithm and patch-based dense matching algorithm; Based on the preliminary ground imagery, a correlation-based dense matching algorithm is used to perform multi-view image dense matching to obtain point cloud data of the area to be measured, specifically including: The exterior orientation elements of the preliminary ground image are recovered using the structure-reconstruction-motion algorithm to obtain a ground image with exterior orientation elements; A multi-view clustering algorithm is used to classify ground images with exterior orientation elements into clustered images. A patch-based dense matching algorithm is used to perform dense matching on the clustered image to obtain point cloud data of the region to be tested.

9. The method for collecting highway elevation information based on unmanned aerial vehicles (UAVs) according to claim 5, characterized in that, Based on the point cloud data of the area to be measured, an elevation digital model of the area to be measured is generated, specifically including: Ground points are extracted based on the point cloud data of the area to be tested, resulting in multiple ground points. Multiple ground points are connected and interpolated to generate an elevation digital model of the area to be measured.

10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor is connected to a drone; the processor executes the computer program to implement the drone-based highway elevation information acquisition method according to any one of claims 1-9.

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