UAV variable speed aerial photography method, device and medium for multi-category terrain elevation

Abstract

The present invention discloses a method, device, and medium for variable-speed drone aerial photography of multi-category terrain elevations, relating to the technical field of photogrammetry. The method primarily includes the following steps: generating initial route data based on the information collection requirements of the various azimuth textures of static targets; controlling the drone to fly according to the initial route data and capturing a sequence of images at a preset forward-looking angle; integrating digital surface model data with the sequence of images and extracting an elevation dataset of each static target within the sequence of images; analyzing the changes in the elevation distribution ratio of static targets within adjacent sequence images based on the elevation datasets and adjusting the speed accordingly; generating final route data based on the speed adjustment results, and controlling the drone to perform variable-speed aerial photography based on the final route data. The present invention dynamically adjusts the drone's flight speed, ensuring the dense acquisition of high-rise building images while avoiding redundant data in low-rise areas.

Description

Technical Field

[0001] The present invention relates to the technical field of photogrammetry, and in particular to a method, device and medium for variable-speed aerial photography of multiple types of terrain features by an unmanned aerial vehicle (UAV). Background Art

[0002] As an important tool for modern surveying and 3D modeling, drone oblique photography technology has been widely used in urban planning, disaster emergency response, and real-world 3D reconstruction. Traditional aerial photography methods are typically based on a fixed altitude, uniform flight speed, and a single route design, combined with vertical or fixed-angle shooting, and are suitable for flat terrain or low-density building areas. However, when faced with densely populated areas with high-rise buildings, complex landforms, or scenes with significant terrain undulations, existing technologies often use a constant flight speed and lack a dynamic adaptation mechanism to the height distribution of landforms. In areas with high-rise buildings, the speed needs to be reduced to extend the exposure time, increase the density of image acquisition, and ensure the integrity of complex facade textures; while in low or open areas, maintaining high-speed flight can improve operational efficiency. However, traditional methods are unable to adjust the speed based on real-time elevation data, resulting in blurred images or matching failures in high-rise areas, and increased storage and processing costs due to redundant data in low-rise areas. Summary of the Invention

[0003] To solve the above problems, the present invention proposes a method for variable-speed aerial photography of multi-category terrain features using a drone, comprising the following steps:

[0004] S1: Based on the parameters of the aerial survey instrument, image overlap requirements, and terrain profile, the static target's azimuth texture is used as information collection requirements to generate mutually perpendicular cross-route routes in the horizontal plane as the initial route data;

[0005] S2: Control the drone to fly according to the initial route data and take a sequence of images at a preset forward-looking angle;

[0006] S3: Integrate digital surface model data and sequence images, and extract the elevation dataset of each static target in the sequence images;

[0007] S4: Analyze the changes in the elevation distribution ratio of static targets in adjacent sequence images based on the elevation dataset, and adjust the speed according to the changes in the ratio;

[0008] S5: Generate final route data according to the speed adjustment result, and control the UAV to perform variable speed aerial photography according to the final route data.

[0009] Furthermore, in step S1, the aerial survey instrument parameters include pixel size ,focal length , Hanggao , equivalent image width And the equivalent image height , the image overlap includes the heading overlap p and the lateral overlap q.

[0010] Furthermore, the equivalent image width and equivalent image height Obtained through the following formula:

[0011]

[0012] Where W is the number of horizontal pixels of the aerial survey instrument sensor, and H is the number of vertical pixels of the aerial survey instrument sensor.

[0013] Furthermore, in the step S3, the elevation dataset is extracted by:

[0014] The initial three-dimensional point cloud data is reconstructed from the sparse point cloud of the sequence images through a multi-view algorithm. After the initial three-dimensional point cloud data is rasterized, it is raster-superimposed with the digital surface model data. Based on the superposition result, the elevation value of the corresponding geographic coordinate is extracted pixel by pixel to form an elevation dataset with the sequence images as units.

[0015] Furthermore, before the step S4, the following steps are further included:

[0016] S40: filtering out the elevation data of static targets with a ratio lower than a preset ratio according to the ratio between the elevation data of each static target and the flight altitude in the sequence of images.

[0017] Furthermore, in step S4, analyzing the change in the elevation distribution ratio of static objects in adjacent sequence images based on the elevation dataset is specifically as follows:

[0018] The maximum elevation data in the current sequence image elevation dataset is taken as the first extreme value, the preset ratio threshold of the maximum elevation data is taken as the second extreme value, and the first extreme value and the second extreme value form the judgment interval. According to the proportion of the judgment interval at the elevation data in the current sequence image elevation dataset, the changes in the elevation distribution proportion of static targets in adjacent sequence images are analyzed.

[0019] Furthermore, in the step S4, the speed adjustment according to the change of the proportion is specifically as follows:

[0020] When the proportion of the elevation data of the previous sequence of images is in the first proportion range: if the proportion of the elevation data of the current sequence of images is in the first proportion range, adjust to the first speed; if the proportion of the elevation data of the current sequence of images is in the second proportion range, adjust to the second speed; if the proportion of the elevation data of the current sequence of images is in the third proportion range, adjust to the third speed;

[0021] When the proportion of the elevation data of the previous sequence of images is in the second proportion range: if the proportion of the elevation data of the current sequence of images is in the first proportion range, adjust to the first speed; if the proportion of the elevation data of the current sequence of images is in the second proportion range, adjust to the second speed; if the proportion of the elevation data of the current sequence of images is in the third proportion range, adjust to the third speed;

[0022] When the proportion of the elevation data of the previous sequence of images is in the third proportion interval: if the proportion of the elevation data of the current sequence of images is in the first proportion interval, adjust to the first speed; if the proportion of the elevation data of the current sequence of images is in the second proportion interval, adjust to the second speed; if the proportion of the elevation data of the current sequence of images is in the third proportion interval, adjust to the third speed;

[0023] The first speed is greater than the second speed, and the second speed is greater than the third speed; the numerical ranges of the first proportion interval, the second proportion interval, and the third proportion interval are adjacent to each other and do not overlap.

[0024] Furthermore, the speed adjustment according to the change of the proportion is expressed as the following formula:

[0025]

[0026] Where, is the adjusted speed, is the first speed, The second speed, The third speed, is the proportion of elevation data in the previous sequence of images, is the proportion of elevation data in the current sequence image, is the first proportion interval, For the second proportion interval, It is the third proportion interval.

[0027] The present invention also includes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for variable-speed aerial photography of multi-category terrain features by a drone.

[0028] Also included is a device for processing data, comprising:

[0029] a memory having a computer program stored thereon;

[0030] The processor is used to execute the computer program in the memory to implement the steps of the method for variable-speed aerial photography of multi-category terrain features by unmanned aerial vehicles.

[0031] Compared with the prior art, the present invention has at least the following beneficial effects:

[0032] (1) The present invention proposes a variable-speed drone aerial photography method for multi-category terrain elevation. By designing mutually perpendicular cross-route routes and combining forward-looking oblique shooting, the method systematically covers the four facades of a building (east, south, west, and north), thus solving the problem of texture loss caused by building occlusion in traditional aerial photography.

[0033] (2) Based on the pixel-level integration of digital surface models and images, local elevation distribution features are dynamically extracted to achieve accurate matching of ship speed and ground feature height;

[0034] (3) Based on the real-time analysis of the elevation distribution ratio, the UAV flight speed is dynamically adjusted, which not only ensures the intensive collection of high-rise building images, but also avoids redundant data in low-lying areas, significantly improving the aerial photography efficiency and image matching success rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The figure shows the steps of a UAV variable speed aerial photography method for multi-category terrain elevation. DETAILED DESCRIPTION

[0036] The following are specific embodiments of the present invention and the accompanying drawings to further describe the technical solutions of the present invention, but the present invention is not limited to these embodiments.

[0037] In the process of building smart cities, it is necessary to combine drone oblique photography, computers and high-precision positioning technology to produce remote sensing big data with high structural accuracy, high geographic accuracy and strong timeliness. In the aerial photography area where the types of land objects are relatively simple, the distribution of buildings is relatively scattered, and the elevation range is normal, the image matching degree is well controlled, and the drone aerial photography results basically meet the requirements of subsequent technical production and results utilization. When the aerial photography area involves densely populated areas and high-rise buildings with complex structures, further requirements are required for the drone aerial photography flight matching degree and the number of images. To this end, if Figure 1 As shown, the present invention proposes a method for variable-speed aerial photography of multi-category terrain features by using a drone, comprising the following steps:

[0038] S1: Based on the parameters of the aerial survey instrument, image overlap requirements, and terrain profile, the static target's azimuth texture is used as information collection requirements to generate mutually perpendicular cross-route routes in the horizontal plane as the initial route data;

[0039] S2: Control the drone to fly according to the initial route data and take a sequence of images at a preset forward-looking angle;

[0040] S3: Integrate digital surface model data and sequence images, and extract the elevation dataset of each static target in the sequence images;

[0041] S4: Analyze the changes in the elevation distribution ratio of static targets in adjacent sequence images based on the elevation dataset, and adjust the speed according to the changes in the ratio;

[0042] S5: Generate final route data according to the speed adjustment result, and control the UAV to perform variable speed aerial photography according to the final route data.

[0043] In the process of generating the initial route data, the core parameters of the aerial survey instrument must be accurately calculated, including the pixel size. ,focal length , flight altitude , equivalent image width and equivalent image height Among them, the equivalent image width and image height are determined by the ratio of the number of horizontal pixels W of the sensor to the number of vertical pixels H at altitude. The calculation formula is: To ensure that the image ground sampling distance (GSD) meets the preset accuracy requirements The image overlap is dynamically adjusted based on the heading overlap p (set to 80%) and the lateral overlap q (set to 80%), using the formula Computational photography baselines, and Determine the route interval, where , thereby ensuring the continuity and coverage density of the image.

[0044] Based on this, and taking into account the terrain and surveying requirements, a network of mutually perpendicular intersecting flight paths was designed. Specifically, an OXY rectangular coordinate system was constructed, with the center of the survey area as the origin, and the X-axis corresponding to the east-west and Y-axis corresponding to the north-south directions, respectively. By controlling the drone along the intersecting flight paths and capturing images at a preset forward tilt angle (e.g., 45°), all-round texture acquisition was achieved for the east, west, south, and north facades of the target in the survey area. For example, during an east-west flight path, the camera's forward tilt angle covered the west building facade, while during reverse flight, the camera covered the east facade. Similarly, north-south flight paths covered both the north and south directions, minimizing blind spots in densely populated areas with high-rise buildings. Furthermore, flight path layout took into account the undulating terrain. In steep slopes, the flight altitude was adjusted or a tethered flight path (perpendicular to the main flight path) was added to ensure overlap and avoid image matching failures. The resulting initial flight path data not only met the requirements for multi-angle texture acquisition but also laid the foundation for subsequent elevation data extraction and dynamic variable-speed aerial photography.

[0045] When the drone is flying according to the initial route data, the flight attitude and shooting parameters need to be precisely controlled to ensure the quality of image acquisition. First, based on the cross-route network generated by S1, the drone uses the RTK positioning system and the inertial navigation unit (IMU) to achieve centimeter-level accuracy in route tracking. The flight altitude is kept within the preset value range, and the speed is stable according to the initial design value. The camera controls the forward tilt angle through the three-axis gimbal, and combines the focal length Dynamic adjustment of exposure time and ISO ensures image clarity and contrast under varying lighting conditions. During flight, the drone triggers the shutter at fixed time intervals or distance intervals, generating a sequence of images continuously covering the survey area. Each image is embedded with GPS coordinates, altitude, attitude angles (pitch, roll, yaw), and timestamp information, forming a complete metadata record.

[0046] After filming is complete, the image sequences are combined with high-precision digital surface model (DSM) data for multi-source data overlaying using cross-platform geo-planning software (such as Omap). Specifically, a sparse point cloud is reconstructed from the image sequences using multi-view geometry algorithms (such as structure from motion, SFM) to generate initial 3D point cloud data, which is then spatially matched with the DSM data. During the matching process, corrections are performed based on control points (such as ground landmarks or points of known elevation). Subsequently, the point cloud data is overlaid with the DSM grid through rasterization, and the elevation values corresponding to the geographic coordinates are extracted pixel by pixel. This creates a rasterized elevation dataset based on the image units and is sorted according to the sequence of points in the initial route data.

[0047] Furthermore, the elevation data of each sequence of images is refined. To address dynamic interference (such as moving vehicles and temporary facilities) in the sequence of images, a time series analysis method combined with morphological filtering (such as opening operations) is used to remove noise data. For example, a variance analysis is performed on the elevation values of multiple consecutive images of the same geographical area. If the fluctuation of the elevation value of a pixel exceeds a certain threshold, it is determined to be a dynamic target and marked as invalid data. At the same time, for abnormal elevation values in the DSM data caused by vegetation or shadows (such as the height of trees mistakenly identified as buildings), the edge detection algorithm combined with texture analysis is used to distinguish the types of ground objects, and only the elevation information of static targets such as buildings and roads is retained.

[0048] Finally, the processed elevation data is arranged in the order of the initial flight path to construct an elevation database that strictly corresponds to the image sequence. Each sequence image's elevation dataset contains the target area's grid height value, feature classification label, and confidence score, providing a quantitative basis for subsequent dynamic speed change strategies.

[0049] By analyzing the constructed elevation database, dynamic speed-changing rules are generated, providing a basis for speed adjustment for subsequent aerial photography. First, the extracted elevation data requires certain preprocessing: a threshold for the flight altitude ratio (e.g., 20% of the flight altitude) is set, and static objects (such as low vegetation and temporary facilities) below this threshold are filtered out. For example, at an altitude of 120 meters, only buildings with elevations ≥ 24 meters are retained to reduce noise interference and focus on key features. This process is implemented using a spatial filtering algorithm, ensuring that the dataset only contains elevation information that significantly influences speed decisions.

[0050] Then extreme value analysis and proportion statistics are performed. Specifically, the maximum elevation value in the current sequence image is taken as the first extreme value (e.g. ), and set the second extreme value to (like , the second extreme is 80 meters). Count the proportion of buildings with elevation values between 80 and 100 meters in the current image. , while recording the proportion of adjacent images , resulting in a change in proportion.

[0051] Here, this embodiment divides the proportion range into three intervals, such as 0-30% (first proportion interval), 30%-60% (second proportion interval), and 60%-100% (third proportion interval), and establishes a speed adjustment mapping relationship:

[0052] When the proportion of the elevation data of the previous sequence of images is in the first proportion range: if the proportion of the elevation data of the current sequence of images is in the first proportion range, adjust to the first speed; if the proportion of the elevation data of the current sequence of images is in the second proportion range, adjust to the second speed; if the proportion of the elevation data of the current sequence of images is in the third proportion range, adjust to the third speed;

[0053] When the proportion of the elevation data of the previous sequence of images is in the second proportion range: if the proportion of the elevation data of the current sequence of images is in the first proportion range, adjust to the first speed; if the proportion of the elevation data of the current sequence of images is in the second proportion range, adjust to the second speed; if the proportion of the elevation data of the current sequence of images is in the third proportion range, adjust to the third speed;

[0054] When the proportion of the elevation data of the previous sequence of images is in the third proportion interval: if the proportion of the elevation data of the current sequence of images is in the first proportion interval, adjust to the first speed; if the proportion of the elevation data of the current sequence of images is in the second proportion interval, adjust to the second speed; if the proportion of the elevation data of the current sequence of images is in the third proportion interval, adjust to the third speed;

[0055] Among them, the first speed is greater than the second speed, and the second speed is greater than the third speed; the numerical intervals of the first proportion interval, the second proportion interval and the third proportion interval are adjacent in sequence and do not overlap with each other.

[0056] The formula is as follows:

[0057]

[0058] in, is the adjusted speed, is the first speed, The second speed, The third speed, is the first proportion interval, For the second proportion interval, It is the third proportion interval.

[0059] The above rule is quantified through this mathematical formula and combined with an acceleration threshold (e.g., ≤2.5 m / s²) to constrain the speed change rate, ensuring that the generated speed data is smooth and continuous.

[0060] The speed-changing route data generated based on this rule includes waypoint coordinates, target speed, and acceleration parameters. As expected, based on this rule design, the system can proactively predict changes in terrain elevation and generate an adaptive speed-changing strategy. For example, it can reduce speed before entering high-rise, densely populated areas to ensure image acquisition density; and it can efficiently cruise in low-lying areas to reduce inefficient flight time. This process is completely data-driven, requiring no human intervention, significantly improving aerial photography efficiency and model accuracy in complex scenarios.

[0061] After analyzing the final route data, the UAV flight control system combines real-time positioning (RTK / PPK) and attitude sensor data to dynamically adjust the flight speed. Specifically, before entering a high-rise dense area, the system predicts the elevation distribution ratio of the current flight segment (such as ), start the deceleration program in advance and reduce the speed from the initial speed Smooth down to , while controlling the acceleration not to exceed (2.5m / s 2 ), to avoid image blur or positioning drift caused by sudden deceleration. In low areas, if , then gradually return to the initial speed , and automatically optimize flight paths to bypass redundant areas to improve efficiency.

[0062] During aerial photography, the system monitors image overlap and speed matching in real time. An onboard computing unit (such as an embedded GPU module) performs real-time quality analysis of captured images. If overlap in critical areas falls below a preset threshold (e.g., heading overlap p < 75%), dynamic course corrections are immediately triggered: additional waypoints are inserted or partial detours are performed to ensure complete image coverage. Simultaneously, the flight control system continuously feeds execution data (such as actual speed, acceleration, and overlap) back to the ground station, creating a closed-loop control system.

[0063] Ultimately, all aerial data (images, metadata, and flight logs) is archived by mission number, with the effectiveness of the speed change strategy (such as the number of speed adjustments and the overlap rate) noted to provide data support for subsequent mission optimization. This process not only enables refined acquisition of high-rise areas and efficient coverage of low-rise areas, but also significantly improves the robustness of aerial photography and model reconstruction accuracy in complex scenarios through a closed-loop feedback mechanism.

[0064] The present invention also includes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for variable-speed aerial photography of multi-category terrain features by a drone.

[0065] Also included is a device for processing data, comprising:

[0066] a memory having a computer program stored thereon;

[0067] The processor is used to execute the computer program in the memory to implement the steps of a method for variable-speed aerial photography of multi-category terrain features by a drone.

[0068] In summary, the present invention proposes a variable-speed drone aerial photography method for the elevation of multiple types of terrain features. By designing mutually perpendicular cross-route routes combined with forward-looking oblique shooting, it systematically covers the four facades of the building in the east, south, west and north, solving the problem of texture loss caused by building obstruction in traditional aerial photography.

[0069] Based on pixel-level integration of digital surface models and imagery, local elevation distribution features are dynamically extracted to achieve precise matching of flight speed and ground feature height. Furthermore, based on real-time analysis of elevation distribution, the drone's flight speed is dynamically adjusted, ensuring intensive image collection of high-rise buildings while avoiding redundant data in low-rise areas, significantly improving aerial photography efficiency and image matching success rates.

[0070] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0071] In addition, in the present invention, descriptions such as "first," "second," and "one" are for descriptive purposes only and should not be understood to indicate or imply their relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0072] In the present invention, unless otherwise specified or limited, the terms "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can mean fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will be able to understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0073] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

Claims

1. A method for variable-speed aerial photography of multiple types of terrain features by using a drone, characterized in that: Including steps: S1: Based on the parameters of the aerial survey instrument, image overlap requirements, and terrain profile, the static target's azimuth texture is used as information collection requirements to generate mutually perpendicular cross-route routes in the horizontal plane as the initial route data; S2: Control the drone to fly according to the initial route data and take a sequence of images at a preset forward-looking angle; S3: Integrate digital surface model data and sequence images, and extract elevation data of each static target in the sequence images; S4: Analyze the change trend of the elevation distribution ratio of static targets in adjacent sequence images based on the elevation data, and adjust the speed according to the change trend of the ratio; S5: Generate final route data according to the speed adjustment result, and control the UAV to perform variable speed aerial photography according to the final route data; In step S4, the maximum elevation data in the current sequence of image elevation data is taken as the first extreme value, and the value lower than the preset threshold of the first extreme value is taken as the second extreme value. According to the proportion of elevation data in the current sequence of image elevation data within the range of the first extreme value and the second extreme value, the change trend of the elevation distribution proportion of static objects in adjacent sequence images is analyzed; In step S4, the speed adjustment according to the change trend of the proportion is specifically as follows: When the proportion of the elevation data of the previous sequence of images is within the first proportion range: if the elevation data of the current sequence of images is within the first proportion range, adjust to the first speed; if the elevation data of the current sequence of images is within the second proportion range, adjust to the second speed; if the elevation data of the current sequence of images is within the third proportion range, adjust to the third speed; When the elevation data ratio of the previous sequence of images is within the second ratio range: if the elevation data ratio of the current sequence of images is within the first ratio range, adjust to the first speed; if the elevation data ratio of the current sequence of images is within the second ratio range, adjust to the second speed; if the elevation data ratio of the current sequence of images is within the third ratio range, adjust to the third speed; When the proportion of the elevation data of the previous sequence of images is within the third proportion range: if the elevation data of the current sequence of images is within the first proportion range, adjust to the first speed; if the elevation data of the current sequence of images is within the second proportion range, adjust to the second speed; if the elevation data of the current sequence of images is within the third proportion range, adjust to the third speed; The first speed is greater than the second speed, and the second speed is greater than the third speed; the first proportion range is smaller than the second proportion range, and the second proportion range is smaller than the third proportion range.

2. The method for variable-speed aerial photography of multiple types of terrain features by using an unmanned aerial vehicle according to claim 1, wherein: In the step S1, the aerial survey instrument parameters include pixel size ,focal length , Hanggao , equivalent image width And the equivalent image height , the image overlap includes the heading overlap p and the lateral overlap q.

3. The method for variable-speed aerial photography of multiple types of terrain features by using an unmanned aerial vehicle according to claim 2, wherein: The equivalent image width and equivalent image height are obtained by the following formula: Where W is the number of horizontal pixels of the aerial survey instrument's sensor, and H is the number of vertical pixels of the aerial survey instrument's sensor.

4. The method for variable speed aerial photography of multiple types of terrain features by using an unmanned aerial vehicle according to claim 1, wherein: In step S3, the elevation data is obtained by: By overlaying the sequence images with the digital surface model data, extracting the grid values of the digital surface model data, matching the elevation database based on the sequence images, and arranging the height value set of the overlay projection of the digital surface model data according to the sequence points of the initial heading data, the elevation data of each static target in the sequence images are obtained.

5. The method for variable speed aerial photography of multiple types of terrain features by using an unmanned aerial vehicle according to claim 2, wherein: Before the S4 step, the following steps are also included: S40: filtering out the elevation data of static targets below a preset ratio according to the ratio between the elevation data of each static target and the flight altitude in the sequence of images.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of a method for variable-speed aerial photography of multi-category terrain features by a drone as described in any one of claims 1 to 5 are implemented.

7. A device for processing data, characterized in that: include: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of a method for variable-speed drone aerial photography of multi-category terrain elevations as described in any one of claims 1 to 5.

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