Unmanned aerial vehicle surveying and mapping method and system for geographic information acquisition

Abstract

The invention provides an unmanned aerial vehicle surveying and mapping method and system for geographic information collection, and belongs to the technical field of surveying and mapping. The method comprises the following steps: determining a first route based on first digital elevation data of a target area; the target area comprises a plurality of target sub-areas; and executing the route adjustment operation of the unmanned aerial vehicle for many times until the terminal point of the first route is reached to realize geographic information surveying and mapping of the target area. According to the unmanned aerial vehicle surveying and mapping method and system for geographic information collection, the surveying and mapping accuracy of the unmanned aerial vehicle can be improved.

Description

Technical Field

[0001] This application belongs to the field of surveying and mapping technology, and more specifically, relates to an unmanned aerial vehicle (UAV) surveying and mapping method and system for geographic information collection. Background Technology

[0002] With the development of drone technology, drones are being used more and more widely in the field of surveying and mapping. Equipped with devices such as lidar and high-resolution cameras, drones can quickly acquire large-scale terrain and feature data. Combined with AI algorithms, they can achieve automated data processing and analysis, providing accurate geographic information support for fields such as urban planning, engineering construction, and disaster monitoring.

[0003] In existing UAV surveying, geographic information is collected based on pre-set fixed routes. When geographic information changes, the fixed route surveying method cannot fully and accurately obtain the real-time changes in geographic information, which affects the accuracy of surveying. Summary of the Invention

[0004] The purpose of this application is to provide a UAV mapping method and system for geographic information collection, so as to improve the mapping accuracy of UAVs.

[0005] A first aspect of this application provides an unmanned aerial vehicle (UAV) mapping method for geographic information collection, comprising: A first flight path is determined based on the first digital elevation data of the target area; the target area includes multiple target sub-regions. The drone's flight path is adjusted multiple times until it reaches the end of the first flight path, in order to achieve geographic information mapping of the target area. Any single route adjustment operation includes: Flight control of the UAV is based on the first flight path; In response to the detection that the UAV has flown to any target sub-region, a second digital elevation data of the target sub-region is determined based on the real-time detection data of the target sub-region; if the similarity between the second digital elevation data of the target sub-region and the corresponding first digital elevation data is less than a similarity threshold, a second flight path of the target sub-region is determined based on the second digital elevation data of the target sub-region; and the first flight path is adjusted based on the second flight path.

[0006] A second aspect of this application provides an unmanned aerial vehicle (UAV) mapping system for geographic information acquisition, comprising: The route determination module is used to determine a first route based on the first digital elevation data of a target area; the target area includes multiple target sub-regions. The flight path adjustment module is used to perform multiple flight path adjustment operations on the UAV until it reaches the end of the first flight path, so as to achieve geographic information mapping of the target area. Any single route adjustment operation includes: Flight control of the UAV is based on the first flight path; In response to the detection that the UAV has flown to any target sub-region, a second digital elevation data of the target sub-region is determined based on the real-time detection data of the target sub-region; if the similarity between the second digital elevation data of the target sub-region and the corresponding first digital elevation data is less than a similarity threshold, a second flight path of the target sub-region is determined based on the second digital elevation data of the target sub-region; and the first flight path is adjusted based on the second flight path.

[0007] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described UAV mapping method for geographic information collection.

[0008] In a fourth aspect, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described UAV mapping method for geographic information collection.

[0009] The beneficial effects of the UAV mapping method and system for geographic information collection provided in this application are as follows: This application embodiment determines a first flight path suitable for the entire target area based on the first digital elevation data of the target area, which can ensure that the UAV covers all target sub-areas within the target area. During the flight of the UAV, the second digital elevation data of each target sub-area is generated in real time and compared with the corresponding first elevation data, which can promptly detect real-time changes in the geographic information of the target sub-area. When the geographic information of the target sub-area changes significantly, a second flight path is determined based on the second elevation data of the target sub-area, and the first flight path is adjusted based on the second flight path. The adjusted first flight path can ensure that the flight altitude and path of the UAV in the target sub-area are more in line with the actual terrain of the target sub-area, thereby improving the mapping accuracy of the UAV in the target sub-area. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart illustrating an unmanned aerial vehicle (UAV) mapping method for geographic information collection, provided as an embodiment of this application; Figure 2 A structural block diagram of an unmanned aerial vehicle (UAV) mapping system for geographic information collection is provided in one embodiment of this application; Figure 3 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0013] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0014] Please refer to Figure 1 , Figure 1 A flowchart illustrating a UAV mapping method for geographic information collection, provided in one embodiment of this application, can be executed by an electronic device. The method may include: S101: Determine the first route based on the first digital elevation data of the target area; the target area includes multiple target sub-areas.

[0015] In this embodiment, the first digital elevation data of the target area is a digital representation of the terrain undulation of the target area. It consists of the three-dimensional coordinates (X, Y, Z) of a large number of discrete points within the target area, where Z is the elevation, usually calculated from the mean sea level or a specified reference surface, and X and Y are planar coordinates (such as Gauss-Kruger coordinates or WGS84 latitude and longitude). The first digital elevation data reflects the altitude information of surface features (such as vegetation, buildings, etc.) through discrete elevation points and serves as the basic reference data for UAV flight path planning.

[0016] Specifically, the first digital elevation data of the target area can be obtained by pre-scanning flight of a UAV. The pre-scanning flight is usually planned according to the orthophoto route, which can quickly cover the target area and acquire images. Then, the images are subjected to aerial triangulation and dense matching by photogrammetry software (such as Pix4D and ContextCapture) to finally generate a digital surface model (DSM) or a digital elevation model (DEM). The elevation data in the digital surface model (DSM) or the digital elevation model (DEM) can be used as the first digital elevation data of the target area.

[0017] Multiple target sub-regions within a target area are areas that require focused attention. For example, in urban surveying, target sub-regions can be urban core areas, historical and cultural sites, resource-rich areas, etc. The spatial range of each target sub-region can be defined by coordinate boundaries (such as longitude range, latitude range, or X / Y axis coordinate range).

[0018] The first flight path can be obtained based on the first elevation data of the target area. The first flight path is the global flight path of the UAV in the target area. Specifically, the start and end points of the first flight path can be preset, and the contour reference lines of the first flight path can be generated using the first elevation data. Then, according to the preset flight path generation rules (such as parallel strip flight path or grid flight path), the first flight path is generated in parallel along the contour reference lines and covers the target sub-area. The fact that the first flight path is distributed in parallel along the contour reference lines can ensure that the flight altitude is always higher than the terrain or ground features.

[0019] S102: Performs multiple flight path adjustments for the UAV until it reaches the end of the first flight path, in order to achieve geographic information mapping of the target area.

[0020] Any single route adjustment operation includes: Flight control of the UAV is based on the first flight path; In response to the detection that the UAV has flown to any target sub-region, the second digital elevation data of the target sub-region is determined based on the real-time detection data of the target sub-region; if the similarity between the second digital elevation data of the target sub-region and the corresponding first digital elevation data is less than the similarity threshold, the second flight path of the target sub-region is determined based on the second digital elevation data of the target sub-region; the first flight path is adjusted based on the second flight path.

[0021] In this embodiment, during the flight of the UAV along the first route, the position of the UAV can be monitored in real time. If the position of the UAV falls within the coordinate range of a certain target sub-region, it is determined that the UAV has entered the target sub-region. At this time, the second digital elevation data can be obtained based on the real-time detection data (such as high-resolution images and laser point clouds) collected by the UAV in the target sub-region.

[0022] By extracting digital elevation data at the same location from the first digital elevation data based on the location coordinates of the second digital elevation data, the corresponding first digital elevation data can be obtained. By calculating the similarity between the second digital elevation data and the corresponding first digital elevation data, the changes in the geographic information of the target sub-region can be determined.

[0023] If the similarity between the second digital elevation data and the corresponding first digital elevation data is greater than or equal to the similarity threshold, it indicates that the geographical information of the target sub-region has not changed significantly. At this time, the UAV can continue to be controlled according to the first flight path.

[0024] If the similarity between the second digital elevation data and the corresponding first digital elevation data is less than the similarity threshold, it indicates that the geographic information of the target sub-region has changed significantly. In this case, a more refined path that fits the real-time terrain of the target sub-region can be replanned based on the second digital elevation data, which is the second flight path.

[0025] By replacing the corresponding target sub-region segment in the first route with the second route, the adjusted first route is obtained. Controlling the UAV to continue flying along the adjusted first route can improve the mapping accuracy of the UAV in the target sub-region.

[0026] Using the above method, the first flight path is continuously adjusted based on the second elevation data of each target sub-region during the UAV flight until the end of the first flight path is reached, thus completing the mapping of the entire target area.

[0027] As can be seen from the above, this embodiment determines a first flight path suitable for the entire target area based on the first digital elevation data of the target area, which can ensure that the UAV covers all target sub-areas within the target area. During the flight of the UAV, the second digital elevation data of each target sub-area is generated in real time and compared with the corresponding first elevation data, which can promptly detect real-time changes in the geographic information of the target sub-area. When the geographic information of the target sub-area changes significantly, a second flight path is determined based on the second elevation data of the target sub-area, and the first flight path is adjusted based on the second flight path. The adjusted first flight path can ensure that the flight altitude and path of the UAV in the target sub-area are more in line with the actual terrain of the target sub-area, thereby improving the mapping accuracy of the UAV in the target sub-area.

[0028] Meanwhile, adjusting the first flight path only when the geographic information of the target sub-region changes can reduce the frequency of adjustments to the first flight path, thereby reducing the amount of computation in the UAV mapping process.

[0029] In one embodiment of this application, for any target sub-region, the real-time detection data of the target sub-region includes laser point cloud data, and determining the second digital elevation data of the target sub-region based on the real-time detection data of the target sub-region includes: The characteristics of point cloud echo intensity, scanning angle, and elevation distribution were determined based on laser point cloud data. Based on the distribution characteristics of point cloud echo intensity, scanning angle, and elevation, laser point cloud data are classified to obtain multiple groups of point cloud group data, with each group of point cloud group data corresponding to a land cover category. Interpolation is performed on multiple groups of point cloud data to obtain the second digital elevation data of the target sub-region.

[0030] In this embodiment, surveying drones are typically equipped with sensors such as LiDAR to acquire laser point cloud data. LiDAR emits laser beams and receives reflected echoes. The energy intensity of the reflected laser beam echoes is directly related to the material and surface roughness of the terrain. Therefore, different terrain features can be distinguished by detecting the intensity of the point cloud echoes. At the same time, the laser scanning angle (or the incident angle between the laser beam and the ground) affects the distribution density and accuracy of the point cloud in the terrain. In steep slope areas, the scanning angle is large, and the point cloud is sparsely distributed along the slope direction. In flat areas, the scanning angle is small, and the point cloud is evenly distributed. Therefore, different terrains can be distinguished by detecting the scanning angle. Elevation distribution characteristics refer to the statistical characteristics of the point cloud in the vertical direction (Z-axis), including the maximum, minimum, standard deviation, and cluster centers of the point cloud within the region. Elevation distribution characteristics can reflect terrain undulations or differences in the height of terrain features. For example, the elevation distribution in building cluster areas shows discrete peaks (corresponding to different building heights), while the elevation distribution in plain areas is concentrated and has a small standard deviation.

[0031] Based on the above characteristics, machine learning models (such as random forests and SVMs) or rule-based matching methods can be used to divide laser point cloud data into multiple groups of point cloud data, such as noisy point cloud data, ground point cloud data, vegetation point cloud data, and building point cloud data. Based on this, noisy point cloud data in laser point cloud data can be removed, and point cloud group data corresponding to multiple land cover categories can be obtained.

[0032] Based on obtaining multiple sets of point cloud group data, interpolation can be performed on the land cover category corresponding to each set of point cloud group data to construct a continuous elevation model (second digital elevation data) to compensate for the discreteness and sparsity of point cloud data.

[0033] As can be seen from the above, this embodiment can clarify the elevation attributes of different land cover categories by classifying laser point cloud data, avoid elevation confusion caused by edge blurring between different land cover categories, and thus enable the final second elevation data to accurately reflect the real scene features such as building height, vegetation canopy and ground undulation.

[0034] In one embodiment of this application, the multiple point cloud group data include ground point cloud data, building point cloud data, and vegetation point cloud data; For any target sub-region, interpolation is performed on multiple sets of point cloud grouped data to obtain the second digital elevation data of that target sub-region, including: For the ground point cloud data in multiple point cloud group data, the ground digital elevation data of the target sub-region is obtained by interpolating the point cloud group data based on the Kriging interpolation method. For building point cloud data in multiple point cloud grouping data, interpolation is performed on the point cloud grouping data based on the inverse distance weighted interpolation method to obtain the building digital elevation data of the target sub-region; For vegetation point cloud data in multiple point cloud grouping data, the point cloud grouping data is interpolated based on the natural neighborhood interpolation method to obtain the vegetation digital elevation data of the target sub-region. The second digital elevation data of the target sub-region is determined based on ground digital elevation data, building digital elevation data, and vegetation digital elevation data.

[0035] In this embodiment, considering that the characteristics of each group of point cloud data are different, different interpolation methods can be used to interpolate different group of point cloud data to improve the interpolation effect.

[0036] Taking the most core and common types of point cloud data—ground point cloud data, building point cloud data, and vegetation point cloud data—as examples, the elevation changes in ground point cloud data follow the continuity of the terrain (such as the gradual change in slope and aspect), exhibiting strong spatial correlation. Kriging interpolation, by calculating the spatial variability function of sample points, quantifies the continuity characteristics of the terrain. During interpolation, it prioritizes referencing the elevation correlation of adjacent points to avoid abrupt jumps. Therefore, interpolating ground point cloud data using the Kriging interpolation method can yield a smooth elevation surface that conforms to the natural terrain trend. Building point cloud data, on the other hand, is characterized by "blocky concentration and steep edges." The characteristics of vegetation point cloud data (such as the vertical drop between the roof plane and the ground) mean that in the inverse distance weighted interpolation process, the weight of a point is inversely proportional to its distance. Nearby points (such as building edge points) have a greater impact on the interpolation results and can enhance the elevation sharpness of the edges. Therefore, interpolating building point cloud data based on the inverse distance weighted interpolation method can avoid blurring of building outlines due to excessive smoothing and accurately preserve artificial structural features such as right-angle turns of walls and roofs. Vegetation point cloud data (such as tree canopies) are discretely distributed and have significant height differences (such as the varying heights of treetops and branches). Natural neighborhood interpolation is based on the Voronoi diagram of the point set and calculates the interpolation through the natural connectivity of adjacent points. Therefore, interpolating vegetation point cloud data based on natural neighborhood interpolation can preserve local details in the original point cloud (such as the peak height of the canopy of a single tree), avoid the loss of vertical stratification features of vegetation due to averaging, and more realistically reflect the spatial distribution of vegetation.

[0037] By fusing the ground digital elevation data, building digital elevation data, and vegetation digital elevation data of each target sub-region, the second digital elevation data of that target sub-region can be obtained.

[0038] It should be noted that if multiple sets of point cloud group data also include other point cloud group data, such as rivers or roads, the corresponding interpolation methods can be used (for example, Kriging interpolation can be used for river point cloud data, and inverse distance weighted interpolation can be used for road point cloud data) to obtain the corresponding elevation data. Then, the river elevation data or road elevation data can be fused with ground digital elevation data, building digital elevation data, and vegetation digital elevation data to obtain the second digital elevation data of the target sub-region.

[0039] As can be seen from the above, this embodiment selects an appropriate interpolation method based on the different characteristics of each group of point cloud group data, which is beneficial to obtaining accurate second digital elevation data in the end.

[0040] In one embodiment of this application, for any target sub-region, determining the second digital elevation data of the target sub-region based on ground digital elevation data, building digital elevation data, and vegetation digital elevation data includes: Extracting ground region contours from ground point cloud data; Extracting building region contours from building point cloud data; Extracting vegetation region contours from vegetation point cloud data; The first buffer zone is determined based on the building area outline; the first buffer zone is a ring-shaped area surrounding the building area outline. The second buffer zone is determined based on the vegetation area outline; the second buffer zone is a ring-shaped area surrounding the vegetation area outline. Different masks are assigned to the building area outline, vegetation area outline, ground area outline, first buffer zone and second buffer zone respectively; Based on the masks corresponding to the building area outline, vegetation area outline, ground area outline, first buffer and second buffer respectively, the preset mapping relationship is found to obtain the fusion rule; Based on the fusion rules, ground digital elevation data, building digital elevation data, and vegetation digital elevation data are fused to obtain the second digital elevation data of the target sub-region.

[0041] In this embodiment, when fusing elevation data corresponding to multiple sets of point cloud data, contour extraction can be performed on each set of point cloud data to obtain multiple regional contours. Then, a unique mask is assigned to each regional contour, and different fusion rules are selected based on the corresponding mask to fuse the elevation data corresponding to the multiple sets of point cloud data.

[0042] Taking ground point cloud data, building point cloud data, and vegetation point cloud data as examples, the continuous boundaries of the ground surface can be extracted from the ground point cloud data to obtain the outline of the ground area; the outline boundaries of man-made buildings (such as houses and towers) can be extracted from the building point cloud data to obtain the outline of the building area; and the outline boundaries of vegetation-covered areas (such as trees and shrubs) can be extracted from the vegetation point cloud data to obtain the outline of the vegetation area.

[0043] Furthermore, to avoid elevation data conflicts at the edges of different land feature areas, transition buffers can be set for the outlines of building areas and vegetation areas. Specifically, based on the outline of the building area, a ring-shaped transition area is generated around its edge to obtain the first buffer, which is used to balance the elevation connection between the building and the ground (e.g., the slope transition between the building edge and the ground); based on the outline of the vegetation area, a ring-shaped transition area is generated around its edge to obtain the second buffer, which is used to handle the elevation fusion between vegetation and the ground, or between vegetation and buildings (e.g., the natural connection between the base of trees and the ground).

[0044] Based on this, unique masks can be assigned to the building area outline, vegetation area outline, ground area outline, first buffer zone and second buffer zone respectively. For example, building mask = 1, vegetation mask = 2, ground mask = 3, first buffer zone mask = 4, second buffer zone mask = 5.

[0045] Based on the above mask lookup and preset mapping relationship, the elevation data fusion rules for different regions can be obtained. For example, the preset mapping relationship can be shown in Table 1 below: Table 1 - Preset Mapping Relationships

[0046] By fusing ground digital elevation data, building digital elevation data, and vegetation digital elevation data according to the selected fusion rules, the second digital elevation data of the target sub-region can be obtained.

[0047] Specifically, for the first buffer zone, if the actual point cloud distribution of the first buffer zone indicates that a segment of the buffer zone is a transitional area between building areas and ground areas, then a fusion rule of weighted average of building elevation data and ground elevation data is adopted; if the actual point cloud distribution of the first buffer zone indicates that a segment of the buffer zone is a transitional area between building areas and vegetation areas, then a fusion rule of weighted average of building elevation data and vegetation elevation data is adopted. Similarly, for the second buffer zone, a fusion rule of weighted average of vegetation elevation data and ground elevation data, or a fusion rule of weighted average of vegetation elevation data and building elevation data, can also be adopted based on the actual point cloud distribution of the buffer zone.

[0048] As can be seen from the above, this embodiment solves the problem of abrupt elevation changes at the edges of different land features by using buffer design and weighted fusion rules, making the final elevation data spatially continuous and smooth, which conforms to the physical distribution law of real land features.

[0049] In one embodiment of this application, the method for calculating the similarity between the second digital elevation data and the corresponding first digital elevation data of any target sub-region includes: Feature extraction is performed on the second digital elevation data and the corresponding first digital elevation data to obtain the first feature vector and the second feature vector; Calculate the similarity between the first feature vector and the second feature vector to obtain the similarity between the second digital elevation data and the corresponding first digital elevation data of the target sub-region.

[0050] In this embodiment, core feature data such as elevation standard deviation, slope mean and variance, terrain curvature, and feature point density can be extracted from the second digital elevation data and the corresponding first digital elevation data. These feature data are then combined to form a first feature vector corresponding to the second digital elevation data and a second feature vector corresponding to the first digital elevation data. Then, similarity calculation methods such as Euclidean distance and cosine similarity are used to obtain the similarity between the first feature vector and the second feature vector. This similarity is used as the similarity between the second digital elevation data and the corresponding first digital elevation data.

[0051] Specifically, taking the second digital elevation data as an example, when calculating the elevation standard deviation, you can first calculate the average elevation of all pixels in the target sub-region, then calculate the sum of squares of the deviations of each pixel's elevation from the average value, take the square root, and obtain the elevation standard deviation.

[0052] When calculating the mean and variance of slope, the elevation change rates (dx, dy) in the x (east-west) and y (north-south) directions can be calculated first using the Sobel operator, and the slope can then be calculated accordingly. : ; The slope at multiple locations within the target sub-region is calculated using the above method, and then the mean and variance of the slope are calculated based on the slope at multiple locations.

[0053] When calculating terrain curvature, the second derivative (dx, dy) can be calculated first based on the elevation gradient (dx, dy). 2 x, d 2 Then, the terrain curvature is calculated based on the Zevenbergen-Thorne terrain classification model.

[0054] When calculating the feature point density, points where the elevation difference between adjacent pixels exceeds a first threshold (e.g., 5 meters) can be counted and recorded as elevation change points. At the same time, points where the slope difference between adjacent pixels exceeds a second threshold (e.g., 30°) can be counted and recorded as slope change points. Then, the total number of elevation change points and slope change points in the target sub-region is counted and divided by the area of ​​the target sub-region to obtain the feature point density.

[0055] As can be seen from the above, by extracting features from the second digital elevation data and the corresponding first digital elevation data respectively, the obtained feature data can cover multi-dimensional information, more comprehensively reflect the overall consistency and detailed differences between the two types of elevation data, and accurately determine the changes in geographic information of the target sub-region.

[0056] In one embodiment of this application, determining a second flight path for any target sub-region based on the second digital elevation data of the target sub-region includes: Determine the first origin and first destination of the first route within the target sub-region; Contour reference lines are determined based on the second digital elevation data of the target sub-region; The initial route is determined based on the first starting point, the first ending point, the contour reference line, and the preset route generation rules; Based on the positional relationship between the initial flight path and the obstacle area in the target sub-region, the initial flight path is adjusted to obtain the second flight path; the obstacle area is determined based on the second digital elevation data of the target sub-region.

[0057] In this embodiment, for any target sub-region, the starting point of the second route is the first starting point of the first route within that target sub-region, and the ending point of the second route is the first ending point of the first route within that target sub-region.

[0058] Based on this, contour reference lines can be obtained from the second digital elevation data of the target sub-region. Taking the first starting point and the first ending point as the endpoints of the flight path, and the contour reference lines as the basis for terrain adaptation, combined with the preset flight path generation rules (such as the threshold for the angle between the flight path and the contour lines, the flight path spacing, the minimum turning radius, etc.), the initial flight path can be obtained.

[0059] Furthermore, based on the second digital elevation data, obstacle areas (such as buildings above the safety threshold, steep mountains, etc.) within the target sub-region can be identified. The positional relationship between the initial route and the obstacle areas (such as whether they are crossed or whether the distance is less than the safety threshold) can be analyzed. The initial route can be corrected by path optimization algorithms (such as A* algorithm and Dijkstra algorithm) to avoid obstacle areas and obtain the final second route.

[0060] As can be seen from the above, this embodiment uses the start and end points of the first route as constraints to generate the second route for the target sub-region, which can ensure the spatial continuity between the sub-region route and the overall route.

[0061] In one embodiment of this application, adjusting the first route based on the second route for any target sub-region includes: Replace the route on the first route between the first origin and the first destination with the second route for the target sub-region.

[0062] In this embodiment, the original route segments within the first starting point and the first ending point of the first route are replaced with a second route targeting the target sub-region. Route optimization is achieved through local replacement, which ensures the stability of the global route while accurately solving the route problems in the local area. There is no need to replan the entire first route. This not only solves the problems of terrain adaptation and obstacle avoidance in the region, but also avoids the redundant calculations and resource consumption caused by global replanning.

[0063] Corresponding to the above embodiment, a UAV mapping method for geographic information collection, Figure 2 This is a structural block diagram of an unmanned aerial vehicle (UAV) mapping system for geographic information acquisition, provided as an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 2 The UAV mapping system 20 for geographic information collection includes: a route determination module 21 and a route adjustment module 22. Among them, the route determination module 21 is used to determine the first route based on the first digital elevation data of the target area; the target area includes multiple target sub-areas; The flight path adjustment module 22 is used to perform multiple flight path adjustment operations of the UAV until it reaches the end of the first flight path, so as to achieve geographic information mapping of the target area. Any single route adjustment operation includes: Flight control of the UAV is based on the first flight path; In response to the detection that the UAV has flown to any target sub-region, the second digital elevation data of the target sub-region is determined based on the real-time detection data of the target sub-region; if the similarity between the second digital elevation data of the target sub-region and the corresponding first digital elevation data is less than the similarity threshold, the second flight path of the target sub-region is determined based on the second digital elevation data of the target sub-region; the first flight path is adjusted based on the second flight path.

[0064] In one embodiment of this application, for any target sub-region, the real-time detection data of the target sub-region includes laser point cloud data, and the flight path adjustment module 22 is specifically used for: The characteristics of point cloud echo intensity, scanning angle, and elevation distribution were determined based on laser point cloud data. Based on the distribution characteristics of point cloud echo intensity, scanning angle, and elevation, laser point cloud data are classified to obtain multiple groups of point cloud group data, with each group of point cloud group data corresponding to a land cover category. Interpolation is performed on multiple groups of point cloud data to obtain the second digital elevation data of the target sub-region.

[0065] In one embodiment of this application, for any target sub-region, the route adjustment module 22 is further configured to: For any target sub-region, interpolation is performed on multiple sets of point cloud grouped data to obtain the second digital elevation data of that target sub-region, including: For the ground point cloud data in multiple point cloud group data, the ground digital elevation data of the target sub-region is obtained by interpolating the point cloud group data based on the Kriging interpolation method. For building point cloud data in multiple point cloud grouping data, interpolation is performed on the point cloud grouping data based on the inverse distance weighted interpolation method to obtain the building digital elevation data of the target sub-region; For vegetation point cloud data in multiple point cloud grouping data, the point cloud grouping data is interpolated based on the natural neighborhood interpolation method to obtain the vegetation digital elevation data of the target sub-region. The second digital elevation data of the target sub-region is determined based on ground digital elevation data, building digital elevation data, and vegetation digital elevation data.

[0066] In one embodiment of this application, for any target sub-region, the route adjustment module 22 is specifically used for: Extracting ground region contours from ground point cloud data; Extracting building region contours from building point cloud data; Extracting vegetation region contours from vegetation point cloud data; The first buffer zone is determined based on the building area outline; the first buffer zone is a ring-shaped area surrounding the building area outline. The second buffer zone is determined based on the vegetation area outline; the second buffer zone is a ring-shaped area surrounding the vegetation area outline. Different masks are assigned to the building area outline, vegetation area outline, ground area outline, first buffer zone and second buffer zone respectively; Based on the masks corresponding to the building area outline, vegetation area outline, ground area outline, first buffer and second buffer respectively, the preset mapping relationship is found to obtain the fusion rule; Based on the fusion rules, ground digital elevation data, building digital elevation data, and vegetation digital elevation data are fused to obtain the second digital elevation data of the target sub-region.

[0067] In one embodiment of this application, for any target sub-region, the route adjustment module 22 is specifically used for: Feature extraction is performed on the second digital elevation data and the corresponding first digital elevation data to obtain the first feature vector and the second feature vector; Calculate the similarity between the first feature vector and the second feature vector to obtain the similarity between the second digital elevation data and the corresponding first digital elevation data of the target sub-region.

[0068] In one embodiment of this application, for any target sub-region, the route adjustment module 22 is specifically used for: Determine the first origin and first destination of the first route within the target sub-region; Contour reference lines are determined based on the second digital elevation data of the target sub-region; The initial route is determined based on the first starting point, the first ending point, the contour reference line, and the preset route generation rules; Based on the positional relationship between the initial flight path and the obstacle area in the target sub-region, the initial flight path is adjusted to obtain the second flight path; the obstacle area is determined based on the second digital elevation data of the target sub-region.

[0069] In one embodiment of this application, for any target sub-region, the route adjustment module 22 is further configured to: Replace the route on the first route between the first origin and the first destination with the second route for the target sub-region.

[0070] See Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 3 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of each module / unit in the above-described device embodiments, for example... Figure 2 The functions of the route determination module 21 and the route adjustment module 22 are shown.

[0071] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0072] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0073] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store preset constants such as a similarity threshold.

[0074] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation method described in the UAV mapping method for geographic information collection provided in the embodiments of this application, or they can execute the implementation method of the electronic device described in the embodiments of this application, which will not be repeated here.

[0075] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0076] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0077] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0078] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connections shown or discussed may be indirect coupling or communication connections through some interfaces or units, or they may be electrical, mechanical, or other forms of connection.

[0080] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0081] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0082] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A UAV mapping method for geographic information collection, characterized in that, include: The first flight path is determined based on the first digital elevation data of the target area; The target region includes multiple target sub-regions; The drone's flight path is adjusted multiple times until it reaches the end of the first flight path, in order to achieve geographic information mapping of the target area. Any single route adjustment operation includes: Flight control of the UAV is based on the first flight path; In response to the detection that the UAV has flown to any target sub-region, a second digital elevation data of the target sub-region is determined based on the real-time detection data of the target sub-region; if the similarity between the second digital elevation data of the target sub-region and the corresponding first digital elevation data is less than a similarity threshold, a second flight path of the target sub-region is determined based on the second digital elevation data of the target sub-region; and the first flight path is adjusted based on the second flight path.

2. The UAV mapping method for geographic information collection as described in claim 1, characterized in that, For any target sub-region, the real-time detection data for that sub-region includes laser point cloud data. Based on the real-time detection data for that sub-region, the second digital elevation data for that sub-region is determined, including: Based on the laser point cloud data, the point cloud echo intensity, scanning angle, and elevation distribution characteristics are determined. The laser point cloud data is classified based on the point cloud echo intensity, scanning angle and elevation distribution characteristics to obtain multiple groups of point cloud group data, and each group of point cloud group data corresponds to a land cover category. Interpolate multiple groups of point cloud data to obtain the second digital elevation data of the target sub-region.

3. The UAV mapping method for geographic information collection as described in claim 2, characterized in that, The multiple point cloud group data include ground point cloud data, building point cloud data, and vegetation point cloud data; For any target sub-region, interpolation is performed on multiple sets of point cloud grouped data to obtain the second digital elevation data of the target sub-region, including: For the ground point cloud data in the multiple groups of point cloud group data, the point cloud group data is interpolated based on the Kriging interpolation method to obtain the ground digital elevation data of the target sub-region; For the building point cloud data in the multiple groups of point cloud group data, the point cloud group data is interpolated based on the inverse distance weighted interpolation method to obtain the building digital elevation data of the target sub-region; For the vegetation point cloud data in the multiple groups of point cloud data, the point cloud data is interpolated based on the natural neighborhood interpolation method to obtain the vegetation digital elevation data of the target sub-region. The second digital elevation data of the target sub-region is determined based on the ground digital elevation data, the building digital elevation data, and the vegetation digital elevation data.

4. The UAV mapping method for geographic information collection as described in claim 3, characterized in that, For any target sub-region, a second digital elevation data for that target sub-region is determined based on the ground digital elevation data, the building digital elevation data, and the vegetation digital elevation data, including: Extract the ground region contour based on the ground point cloud data; Extract the building region outline based on the building point cloud data; Extract the vegetation region outline based on the vegetation point cloud data; A first buffer zone is determined based on the outline of the building area; the first buffer zone is a ring-shaped area surrounding the outline of the building area. A second buffer zone is determined based on the outline of the vegetation area; the second buffer zone is a ring-shaped area surrounding the outline of the vegetation area. Different masks are assigned to the building area outline, the vegetation area outline, the ground area outline, the first buffer zone, and the second buffer zone, respectively; Based on the building area outline, the vegetation area outline, the ground area outline, the masks corresponding to the first buffer and the second buffer respectively, a preset mapping relationship is found to obtain the fusion rule; Based on the fusion rules, the ground digital elevation data, the building digital elevation data, and the vegetation digital elevation data are fused to obtain the second digital elevation data of the target sub-region.

5. The UAV mapping method for geographic information collection as described in claim 1, characterized in that, For any target sub-region, the method for calculating the similarity between the second digital elevation data of that sub-region and the corresponding first digital elevation data includes: Feature extraction is performed on the second digital elevation data and the corresponding first digital elevation data to obtain a first feature vector and a second feature vector; Calculate the similarity between the first feature vector and the second feature vector to obtain the similarity between the second digital elevation data and the corresponding first digital elevation data of the target sub-region.

6. The UAV mapping method for geographic information collection as described in claim 1, characterized in that, For any target sub-region, a second flight path is determined based on the second digital elevation data of that target sub-region, including: Determine the first starting point and the first ending point of the first route in the target sub-region; Contour reference lines are determined based on the second digital elevation data of the target sub-region; The initial route is determined based on the first starting point, the first ending point, the contour reference line, and the preset route generation rules; Based on the positional relationship between the initial route and the obstacle area in the target sub-region, the initial route is adjusted to obtain the second route; the obstacle area is determined based on the second digital elevation data of the target sub-region.

7. The UAV mapping method for geographic information collection as described in claim 6, characterized in that, For any target sub-region, the first route is adjusted based on the second route, including: Replace the route on the first route that is located between the first origin and the first destination with the second route of the target sub-region.

8. A UAV mapping system for geographic information acquisition, characterized in that, include: The route determination module is used to determine the first route based on the first digital elevation data of the target area. The target region includes multiple target sub-regions; The flight path adjustment module is used to perform multiple flight path adjustment operations on the UAV until it reaches the end of the first flight path, so as to achieve geographic information mapping of the target area. Any single route adjustment operation includes: Flight control of the UAV is based on the first flight path; In response to the detection that the UAV has flown to any target sub-region, a second digital elevation data of the target sub-region is determined based on the real-time detection data of the target sub-region; if the similarity between the second digital elevation data of the target sub-region and the corresponding first digital elevation data is less than a similarity threshold, a second flight path of the target sub-region is determined based on the second digital elevation data of the target sub-region; and the first flight path is adjusted based on the second flight path.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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