A method and system for unmanned aerial vehicle surveying for geographic information collection

Abstract

The application provides a kind of unmanned aerial vehicle surveying and mapping method and system for geographic information collection, belong to surveying and mapping technical field, this method includes: based on the first digital elevation data of target area determines first route;Target area includes multiple target sub-regions;Multiple times execute the route adjustment operation of unmanned aerial vehicle, until to the end point of first route, to realize the geographic information surveying and mapping of target area.The unmanned aerial vehicle surveying and mapping method and system for geographic information collection provided in the application can improve the surveying and mapping accuracy of unmanned aerial vehicle.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of surveying and mapping, and more particularly relates to a method and system for unmanned aerial vehicle surveying and mapping for geographic information collection. BACKGROUND

[0002] With the development of unmanned aerial vehicle technology, unmanned aerial vehicles are increasingly widely used in the field of surveying and mapping. They can quickly obtain large-scale terrain and feature data by carrying devices such as laser radars and high-resolution cameras, and can realize automatic data processing and analysis by combining AI algorithms, thereby providing accurate geographic information support for urban planning, engineering construction, disaster monitoring, and other fields.

[0003] In the prior art, when an unmanned aerial vehicle surveys, geographic information is collected based on a pre-set fixed flight path. When the geographic information changes, the fixed flight path surveying method cannot comprehensively and accurately obtain real-time changes in geographic information, which affects the surveying accuracy. SUMMARY

[0004] The application aims to provide a method and system for unmanned aerial vehicle surveying and mapping for geographic information collection to improve the surveying accuracy of unmanned aerial vehicles.

[0005] In a first aspect, the application provides a method for unmanned aerial vehicle surveying and mapping for geographic information collection, comprising:

[0006] determining a first flight path based on first digital elevation data of a target region; the target region comprises a plurality of target sub-regions;

[0007] performing a flight path adjustment operation of the unmanned aerial vehicle multiple times until reaching an end point of the first flight path to realize geographic information surveying of the target region;

[0008] wherein each flight path adjustment operation comprises:

[0009] controlling the flight of the unmanned aerial vehicle based on the first flight path;

[0010] in response to detecting that the unmanned aerial vehicle flies to any target sub-region, determining second digital elevation data of the target sub-region based on 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, determining a second flight path of the target sub-region based on the second digital elevation data of the target sub-region; and adjusting the first flight path based on the second flight path.

[0011] In a second aspect, the application provides a system for unmanned aerial vehicle surveying and mapping for geographic information collection, comprising:

[0012] The route determining module is configured to determine a first route based on first digital elevation data of the target region, wherein the target region comprises a plurality of target sub-regions;

[0013] The route adjusting module is configured to perform a plurality of route adjusting operations of the UAV until reaching an end point of the first route, so as to realize geographic information mapping of the target region.

[0014] Each of the route adjusting operations comprises:

[0015] The flight control of the UAV is performed based on the first route.

[0016] In response to detecting that the UAV flies to any target sub-region, second digital elevation data of the target sub-region is determined based on real-time detection data of the target sub-region; if a similarity between the second digital elevation data of the target sub-region and corresponding first digital elevation data is less than a similarity threshold, a second route of the target sub-region is determined based on the second digital elevation data of the target sub-region; and the first route is adjusted based on the second route.

[0017] In a third aspect, an electronic device is provided, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the method for geographic information collection of the UAV mapping method when running the computer program.

[0018] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program implements the steps of the method for geographic information collection of the UAV mapping method when executed by a processor.

[0019] The method and system for geographic information collection of the UAV mapping method provided by the embodiments of the present application have the following advantages:

[0020] The embodiments of the present application determine the first route suitable for the target region based on the first digital elevation data of the target region, which can ensure full coverage of the UAV to each target sub-region in the target region. In the flight process of the UAV, the second digital elevation data of each target sub-region is generated in real time and compared with the corresponding first elevation data, so that real-time changes in geographic information of the target sub-region can be found in time. When the geographic information of the target sub-region changes greatly, the second route is determined based on the second elevation data of the target sub-region, and the first route is adjusted based on the second route. The adjusted first route can ensure that the flight height and path of the UAV in the target sub-region are more suitable for the actual terrain of the target sub-region, thereby improving the mapping accuracy of the UAV in the target sub-region. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0022] Figure 1 A flowchart of a UAV surveying and mapping method for geographic information collection provided by an embodiment of the present application;

[0023] Figure 2 A structural block diagram of a UAV surveying and mapping system for geographic information collection provided by an embodiment of the present application;

[0024] Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0025] In the following description, specific details such as specific system structures, techniques, etc. are presented in order to thoroughly understand the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits and methods are omitted to avoid unnecessary details that hinder the description of the present application.

[0026] In order to make the purpose, technical solutions and advantages of the present application clearer, specific embodiments will be described below with reference to the drawings.

[0027] Reference will be made to Figure 1 , Figure 1 A flowchart of a UAV surveying and mapping method for geographic information collection provided by an embodiment of the present application can be executed by an electronic device, and the method can include:

[0028] S101: determining a first flight route based on first digital elevation data of a target area; the target area includes a plurality of target sub-areas.

[0029] In the present embodiment, the first digital elevation data of the target area is a digital expression of the topographic relief state of the target area, which is composed of three-dimensional coordinates (X, Y, Z) of a large number of discrete points in the target area, wherein the Z value is the elevation, and the X and Y are plane coordinates (such as Gauss-Kruger coordinates or WGS84 latitude and longitude) with the average sea level or a specified reference surface as the starting point. The first digital elevation data reflects the elevation information of the ground objects (such as vegetation, buildings, etc.) through discrete elevation points, and is the basic reference data for UAV flight planning.

[0030] Specifically, the first digital elevation data of the target region can be obtained by a pre-scan flight of the UAV, the pre-scan flight is usually planned according to the ortho route, can quickly cover the target region and obtain images, and then the aerial triangulation and dense matching are performed on the images by using photogrammetry software (such as Pix4D and ContextCapture), and finally a digital surface model (DSM) or a digital elevation model (DEM) is generated, and 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 region.

[0031] The plurality of target sub-regions in the target region are regions that need to be focused on in the target region, for example, in urban mapping, the target sub-region can be a city core area, a historical and cultural site, a resource-rich area, etc., and the spatial range of each target sub-region can be defined by a coordinate boundary (such as a longitude range, a latitude range, or an X / Y axis coordinate range).

[0032] Based on the first elevation data of the target region, a first route can be obtained, the first route is a global route of the UAV in the target region, specifically, the starting point and the ending point of the first route can be preset, and the contour reference line of the first route is generated by using the first elevation data, and then the first route is generated according to the preset route generation rule (such as parallel strip route or grid route), which is distributed in parallel along the contour reference line and covers the target sub-region, wherein the first route is distributed in parallel along the contour reference line, which can ensure that the flight height is always higher than the terrain or the ground object.

[0033] S102: The route adjustment operation of the UAV is performed multiple times until the ending point of the first route is reached, so as to realize the geographic information mapping of the target region.

[0034] In the route adjustment operation, the UAV is controlled to fly along the first route, and the UAV flies to any target sub-region.

[0035] The flight control of the UAV is performed based on the first route.

[0036] In response to detecting that the UAV flies 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 a similarity threshold, the second route of the target sub-region is determined based on the second digital elevation data of the target sub-region; and the first route is adjusted based on the second route.

[0037] 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 judged that the UAV enters the target sub-region, and 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.

[0038] The digital elevation data of the same position in the first digital elevation data can be obtained based on the position coordinates of the second digital elevation data, and the similarity between the second digital elevation data and the corresponding first digital elevation data can be calculated to determine the change of the geographical information of the target sub-region.

[0039] 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, and the flight control of the unmanned aerial vehicle can be continued according to the first route.

[0040] 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 geographical information of the target sub-region has changed significantly, and a refined path that is more suitable for the real-time terrain of the target sub-region, i.e., the second route, can be planned based on the second digital elevation data.

[0041] The section corresponding to the target sub-region in the first route is replaced by the second route to obtain an adjusted first route, and the unmanned aerial vehicle continues to fly according to the adjusted first route, which can improve the mapping accuracy of the unmanned aerial vehicle in the target sub-region.

[0042] By using the above method, the first route is adjusted based on the second elevation data of each target sub-region during the flight of the unmanned aerial vehicle until the end point of the first route is reached, and the entire target region is mapped.

[0043] From the above, it can be concluded that the first route suitable for the entire target region is determined based on the first digital elevation data of the target region, which can ensure that the unmanned aerial vehicle fully covers each target sub-region in the target region. The second digital elevation data of each target sub-region is generated in real time during the flight of the unmanned aerial vehicle and compared with the corresponding first elevation data, which can timely discover the real-time change of the geographical information of the target sub-region. When the geographical information of the target sub-region changes significantly, the second route is determined based on the second elevation data of the target sub-region, and the first route is adjusted based on the second route. The adjusted first route can ensure that the flight height and path of the unmanned aerial vehicle in the target sub-region are more suitable for the actual terrain of the target sub-region, thereby improving the mapping accuracy of the unmanned aerial vehicle in the target sub-region.

[0044] At the same time, the adjustment of the first route is only performed when the geographical information of the target sub-region changes, which can reduce the frequent adjustment of the first route and thus reduce the computational load during the mapping process of the unmanned aerial vehicle.

[0045] In an embodiment of the present application, for any target sub-region, the real-time detection data of the target sub-region includes laser point cloud data, the second digital elevation data of the target sub-region is determined based on the real-time detection data of the target sub-region, including:

[0046] The point cloud echo intensity, scanning angle and elevation distribution characteristics are determined based on the laser point cloud data;

[0047] The laser point cloud data is classified based on the point cloud echo intensity, scanning angle and elevation distribution characteristics, and a plurality of groups of point cloud grouping data are obtained, one group of point cloud grouping data corresponding to one ground object category;

[0048] Interpolation is performed on the plurality of groups of point cloud grouping data to obtain the second digital elevation data of the target sub-region.

[0049] In the present embodiment, the surveying and mapping unmanned aerial vehicle usually carries sensors such as laser radar to obtain laser point cloud data. The laser radar (LiDAR) emits a laser beam and receives a reflected echo. The energy intensity of the reflected echo of the laser beam is directly related to the ground object material and surface roughness. Therefore, different ground objects can be distinguished by detecting the point cloud echo intensity. At the same time, the laser scanning angle (or the incident angle of the laser beam to the ground) will affect the distribution density and accuracy of the point cloud in the terrain. The scanning angle is large in steep slope area, and the point cloud is sparsely distributed along the slope direction. The scanning angle is small in flat area, and the point cloud is uniformly distributed. Therefore, different terrains can be distinguished by detecting the scanning angle. The elevation distribution characteristics refer to the statistical characteristics of the point cloud in the vertical direction (Z axis), including the maximum value, minimum value, standard deviation and cluster center of the point cloud in the region, etc. The elevation distribution characteristics can reflect the terrain undulation or the height difference of the ground object. For example, the elevation distribution of the building group area presents discrete peaks (corresponding to different building heights), and the elevation distribution of the plain area is concentrated, and the standard deviation is small.

[0050] According to the above characteristics, the laser point cloud data can be divided into a plurality of groups of point cloud grouping data such as noise point cloud data, ground point cloud data, vegetation point cloud data and building point cloud data by using a machine learning model (such as random forest, SVM) or a rule base matching method. Accordingly, the noise point cloud data in the laser point cloud data can be removed, and a plurality of groups of point cloud grouping data corresponding to different ground object categories can be obtained.

[0051] On the basis of obtaining the plurality of groups of point cloud grouping data, interpolation is performed on the ground object category corresponding to each group of point cloud grouping data, and a continuous elevation model (second digital elevation data) can be constructed to compensate for the discreteness and sparseness of the point cloud data.

[0052] From the above, it can be concluded that by classifying the laser point cloud data, the elevation attributes of different ground object categories can be determined, and the elevation confusion caused by the edge blur between different ground objects can be avoided, so that the second elevation data obtained finally can accurately reflect the real scene characteristics such as building height, vegetation canopy and ground undulation.

[0053] In an embodiment of the present application, the plurality of point cloud grouping data includes ground point cloud data, building point cloud data and vegetation point cloud data.

[0054] For any target sub-region, the plurality of point cloud grouping data is interpolated to obtain second digital elevation data of the target sub-region, including:

[0055] For the ground point cloud data in the plurality of point cloud grouping data, the point cloud grouping data is interpolated based on the Kriging interpolation method to obtain the ground digital elevation data of the target sub-region.

[0056] For the building point cloud data in the plurality of point cloud grouping data, the point cloud grouping data is interpolated based on the inverse distance weighted interpolation method to obtain the building digital elevation data of the target sub-region.

[0057] For the vegetation point cloud data in the plurality of 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.

[0058] Based on the ground digital elevation data, the building digital elevation data and the vegetation digital elevation data, the second digital elevation data of the target sub-region is determined.

[0059] In the present embodiment, considering that the characteristics of each group of point cloud grouping data are different, different point cloud grouping data can be interpolated based on different interpolation methods to improve the interpolation effect.

[0060] Taking the most core and most common ground point cloud data, building point cloud data and vegetation point cloud data as examples, the elevation change of the ground point cloud data follows the terrain continuity (such as the gradual change rule of slope and slope direction), and the spatial correlation is strong. The Kriging interpolation method quantifies the continuity characteristics of the terrain by calculating the spatial variation function of the sample points, and preferentially refers to the elevation correlation of the adjacent points during the interpolation to avoid abrupt jumps. Therefore, the Kriging interpolation method is used to interpolate the ground point cloud data, and a smooth elevation surface that conforms to the natural terrain trend can be obtained. The building point cloud data has the characteristics of "block concentration and steep edge" (such as the vertical drop of the roof plane and the ground). During the inverse distance weighted interpolation process, the weight of a point is inversely proportional to its distance, and the nearby points (such as the building edge points) have a greater impact on the interpolation results, which can strengthen the elevation sharpness of the edge. Therefore, the inverse distance weighted interpolation method is used to interpolate the building point cloud data, which can avoid the blurring of the building contour caused by excessive smoothing and accurately retain the right-angle turning features of the wall and roof. The vegetation point cloud data (such as tree crowns) is distributed discretely and has significant height differences (such as the height difference between tree branches and tree trunks). The natural neighborhood interpolation is based on the Voronoi diagram of the point set, and the interpolation is calculated through the natural connection relationship between adjacent points. Therefore, the natural neighborhood interpolation is used to interpolate the vegetation point cloud data, which can retain the local details (such as the crown height peak value of a single tree) in the original point cloud, avoid the loss of the vertical stratification features of the vegetation due to the averaging process, and more truly reflect the spatial distribution of the vegetation.

[0061] The ground digital elevation data, the building digital elevation data and the vegetation digital elevation data of each target sub-region are fused to obtain the second digital elevation data of the target sub-region.

[0062] It should be noted that if the multiple groups of point cloud grouping data further include other point cloud grouping data, such as rivers or roads, corresponding interpolation methods (for example, the Kriging interpolation method can be used for river point cloud data, and the inverse distance weighted interpolation method can be used for road point cloud data) can be used to obtain corresponding elevation data, and then the river elevation data or road elevation data is fused with the ground digital elevation data, the building digital elevation data and the vegetation digital elevation data to obtain the second digital elevation data of the target sub-region.

[0063] From the above, it can be concluded that the embodiment selects an adaptive interpolation method based on the different characteristics of each group of point cloud grouping data, which is beneficial to finally obtain accurate second digital elevation data.

[0064] In an embodiment of the present application, for any 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, comprising:

[0065] extracting a ground region contour based on the ground point cloud data;

[0066] extracting a building region contour based on the building point cloud data;

[0067] extracting a vegetation region contour based on the vegetation point cloud data;

[0068] determining a first buffer zone based on the building region contour; the first buffer zone is an annular region surrounding the building region contour;

[0069] determining a second buffer zone based on the vegetation region contour; the second buffer zone is an annular region surrounding the vegetation region contour;

[0070] assigning different masks to the building region contour, the vegetation region contour, the ground region contour, the first buffer zone and the second buffer zone respectively;

[0071] finding a preset mapping relationship based on the masks corresponding to the building region contour, the vegetation region contour, the ground region contour, the first buffer zone and the second buffer zone respectively, to obtain a fusion rule;

[0072] fusing the ground digital elevation data, the building digital elevation data and the vegetation digital elevation data based on the fusion rule, to obtain second digital elevation data of the target sub-region.

[0073] In this embodiment, when fusing the elevation data corresponding to multiple sets of point cloud data, the contour extraction can be performed based on the multiple sets of point cloud data respectively, to obtain multiple region contours, and then a unique mask is assigned to each region contour, and different fusion rules are selected based on the corresponding masks to fuse the elevation data corresponding to the multiple sets of point cloud data.

[0074] Taking the ground point cloud data, the building point cloud data and the vegetation point cloud data as examples, the continuous boundary of the ground surface can be extracted from the ground point cloud data, to obtain the ground region contour; the contour boundary of artificial buildings (such as houses and towers) can be extracted from the building point cloud data, to obtain the building region contour; and the contour boundary of vegetation coverage regions (such as trees and shrubs) can be extracted from the vegetation point cloud data, to obtain the vegetation region contour.

[0075] Further, to avoid the conflict of the elevation data of the edges of different ground object regions, a transition buffer zone can be set for the building region contour and the vegetation region contour. Specifically, an annular transition region surrounding the edge of the building region contour is generated based on the building region contour, to obtain the first buffer zone, which is used to balance the elevation connection of the building and the ground (for example, the slope transition of the edge of the building and the ground); and an annular transition region surrounding the edge of the vegetation region contour is generated based on the vegetation region contour, to obtain the second buffer zone, which is used to process the elevation fusion of the vegetation and the ground or the vegetation and the building (for example, the natural connection of the bottom of the tree and the ground).

[0076] On this basis, the building area contour, the vegetation area contour, the ground area contour, the first buffer zone and the second buffer zone can be respectively given a unique mask, for example, building mask = 1, vegetation mask = 2, ground mask = 3, first buffer zone mask = 4, and second buffer zone mask = 5.

[0077] Based on the above mask, the preset mapping relationship can be found, and the elevation data fusion rule of different areas can be obtained, for example, the preset mapping relationship can be as shown in Table 1:

[0078] Table 1 - Preset mapping relationship

[0079]

[0080] Based on the selected fusion rule, the ground digital elevation data, the building digital elevation data and the vegetation digital elevation data are fused, and the second digital elevation data of the target sub-area can be obtained.

[0081] Among them, for the first buffer zone, if the actual point cloud distribution of the first buffer zone indicates that a certain section of the buffer zone is a transition area of the building area and the ground area, the fusion rule of weighted average of the building elevation data and the ground elevation data is adopted; if the actual point cloud distribution of the first buffer zone indicates that a certain section of the buffer zone is a transition area of the building area and the vegetation area, the fusion rule of weighted average of the building elevation data and the vegetation elevation data is adopted. Similarly, for the second buffer zone, the fusion rule of weighted average of the vegetation elevation data and the ground elevation data, or the fusion rule of weighted average of the vegetation elevation data and the building elevation data can also be adopted according to the actual point cloud distribution of the buffer zone.

[0082] From the above, it can be concluded that the embodiment solves the elevation mutation problem of different ground object edges by buffer zone design and weighted fusion rule, so that the final elevation data is continuous and smooth in space, and conforms to the physical distribution law of real ground objects.

[0083] In an embodiment of the present application, for any target sub-area, the similarity between the second digital elevation data of the target sub-area and the corresponding first digital elevation data is calculated in the following manner:

[0084] The second digital elevation data and the corresponding first digital elevation data are respectively subjected to feature extraction to obtain a first feature vector and a second feature vector;

[0085] The similarity between the first feature vector and the second feature vector is calculated to obtain the similarity between the second digital elevation data of the target sub-area and the corresponding first digital elevation data.

[0086] In the embodiment, the core feature data of the second digital elevation data and the corresponding first digital elevation data can be extracted respectively, such as the elevation standard deviation, the slope mean and variance, the terrain curvature, the feature point density, etc. The above feature data are combined into the first feature vector corresponding to the second digital elevation data and the second feature vector corresponding to the first digital elevation data respectively. Then, the similarity between the first feature vector and the second feature vector can be obtained by using the Euclidean distance, the cosine similarity and other similarity calculation methods, and the similarity is taken as the similarity of the second digital elevation data and the corresponding first digital elevation data.

[0087] Specifically, taking the second digital elevation data as an example, when calculating the elevation standard deviation, the elevation average of all pixels in the target sub-region can be calculated first, and then the deviation square sum of the elevation of each pixel and the average value is calculated, and the square root is taken to obtain the elevation standard deviation.

[0088] When calculating the slope mean and variance, the elevation change rates (dx, dy) in the x (east-west) and y (north-south) directions can be calculated by the Sobel operator, and the slope :

[0089] ;

[0090] The slope of multiple positions in the target sub-region is calculated by using the above method, and then the slope mean and variance are calculated according to the slopes of the multiple positions.

[0091] When calculating the terrain curvature, the second derivatives (d 2 x, d 2 y) can be calculated based on the elevation gradient (dx, dy), and then the terrain curvature is calculated based on the Zevenbergen-Thorne terrain classification model.

[0092] When calculating the feature point density, the points with the elevation difference of adjacent pixels exceeding a first threshold (such as 5 meters) are counted as the elevation mutation points, and the points with the slope difference of adjacent pixels exceeding a second threshold (such as 30°) are counted as the slope mutation points. Then, the number of all the elevation mutation points and the slope mutation points in the target sub-region is counted, and then the feature point density is obtained by dividing the area of the target sub-region.

[0093] As can be seen from the above, by extracting the features of the second digital elevation data and the corresponding first digital elevation data respectively, the feature data obtained can cover multi-dimensional information, and can more comprehensively reflect the overall consistency and the detail difference of the two types of elevation data, so that the geographical information change of the target sub-region can be accurately judged.

[0094] In an embodiment of the present application, for any target sub-region, the second route of the target sub-region is determined based on the second digital elevation data of the target sub-region, including:

[0095] determining a first starting point and a first ending point of the first route in the target sub-region;

[0096] determining an isohypse reference line based on the second digital elevation data of the target sub-region;

[0097] determining an initial route based on the first starting point, the first ending point, the isohypse reference line, and a preset route generation rule;

[0098] adjusting the initial route based on the position relationship between the initial route and an obstacle region in the target sub-region to obtain the second route; the obstacle region is determined based on the second digital elevation data of the target sub-region.

[0099] In the embodiment, for any target sub-region, the starting point of the second route is the first starting point of the first route in the target sub-region, and the ending point of the second route is the first ending point of the first route in the target sub-region.

[0100] On this basis, the isohypse reference line can be obtained based on the second digital elevation data of the target sub-region, the initial route can be obtained with the first starting point and the first ending point as the route endpoints, the isohypse reference line as the terrain adaptation basis, and in combination with the preset route generation rule (such as route direction and isohypse crossing angle threshold, route spacing, minimum turning radius, etc.).

[0101] Further, the obstacle region (such as a building higher than a safety threshold, a steep mountain, etc.) in the target sub-region can be identified based on the second digital elevation data, the position relationship (such as whether to cross, whether the distance is less than a safety threshold) between the initial route and the obstacle region is analyzed, and the initial route is corrected by a path optimization algorithm (such as A* algorithm, Dijkstra algorithm) to avoid the obstacle region, thereby obtaining the final second route.

[0102] As can be seen from the above, the embodiment takes the starting point and the ending point of the first route as constraints to generate the second route of the target sub-region, which can ensure the spatial continuity of the sub-region route and the overall route.

[0103] In an embodiment of the present application, for any target sub-region, the first route is adjusted based on the second route, including:

[0104] replacing the route between the first starting point and the first ending point on the first route with the second route of the target sub-region.

[0105] In the embodiment, the original route segment in the first starting point and the first end point in the first route is replaced by the second route for the target sub-region, the route optimization is realized by local replacement, the route problem of the local region is accurately solved while the global route stability is ensured, the whole first route does not need to be replanned, the problems such as terrain adaptation and obstacle avoidance of the region are solved, and redundant calculation and resource consumption caused by global replanning are avoided.

[0106] A method for unmanned aerial vehicle surveying and mapping for geographic information collection, Figure 2 A structural block diagram of an unmanned aerial vehicle surveying and mapping system for geographic information collection is provided in an embodiment of the present application. For ease of illustration, only parts related to the embodiments of the present application are shown. For reference Figure 2 The unmanned aerial vehicle surveying and mapping system 20 includes a route determination module 21 and a route adjustment module 22.

[0107] The route determination module 21 is configured to determine a first route based on first digital elevation data of a target region; the target region includes a plurality of target sub-regions.

[0108] The route adjustment module 22 is configured to perform a route adjustment operation of the unmanned aerial vehicle multiple times until reaching an end point of the first route, so as to realize geographic information mapping of the target region.

[0109] Each route adjustment operation includes:

[0110] Flight control of the unmanned aerial vehicle is performed based on the first route;

[0111] In response to detecting that the unmanned aerial vehicle flies to any target sub-region, second digital elevation data of the target sub-region is determined based on real-time detection data of the target sub-region; if a similarity between the second digital elevation data of the target sub-region and corresponding first digital elevation data is less than a similarity threshold, a second route of the target sub-region is determined based on the second digital elevation data of the target sub-region; and the first route is adjusted based on the second route.

[0112] In an embodiment of the present application, for any target sub-region, the real-time detection data of the target sub-region includes laser point cloud data, and the route adjustment module 22 is specifically configured to:

[0113] Point cloud echo intensity, scanning angle and elevation distribution characteristics are determined based on the laser point cloud data;

[0114] The laser point cloud data is classified based on the point cloud echo intensity, the scanning angle and the elevation distribution characteristics to obtain a plurality of groups of point cloud grouping data; one group of point cloud grouping data corresponds to one ground object category.

[0115] Interpolating the plurality of groups of point cloud grouping data to obtain second digital elevation data of the target sub-region.

[0116] In an embodiment of the present application, for any target sub-region, the flight path adjustment module 22 is specifically configured to:

[0117] For any target sub-region, interpolating the plurality of groups of point cloud grouping data to obtain second digital elevation data of the target sub-region, comprising:

[0118] For ground point cloud data in the plurality of groups of point cloud grouping data, interpolating the point cloud grouping data based on the Kriging interpolation method to obtain ground digital elevation data of the target sub-region;

[0119] For building point cloud data in the plurality of groups of point cloud grouping data, interpolating the point cloud grouping data based on the inverse distance weighted interpolation method to obtain building digital elevation data of the target sub-region;

[0120] For vegetation point cloud data in the plurality of groups of point cloud grouping data, interpolating the point cloud grouping data based on the natural neighborhood interpolation method to obtain vegetation digital elevation data of the target sub-region;

[0121] Based on the ground digital elevation data, the building digital elevation data and the vegetation digital elevation data, determining the second digital elevation data of the target sub-region.

[0122] In an embodiment of the present application, for any target sub-region, the flight path adjustment module 22 is specifically configured to:

[0123] Extracting a ground area contour based on the ground point cloud data;

[0124] Extracting a building area contour based on the building point cloud data;

[0125] Extracting a vegetation area contour based on the vegetation point cloud data;

[0126] Determining a first buffer zone based on the building area contour; the first buffer zone is an annular region surrounding the building area contour;

[0127] Determining a second buffer zone based on the vegetation area contour; the second buffer zone is an annular region surrounding the vegetation area contour;

[0128] Assigning different masks to the building area contour, the vegetation area contour, the ground area contour, the first buffer zone and the second buffer zone, respectively;

[0129] Looking up a preset mapping relationship based on the masks corresponding to the building area contour, the vegetation area contour, the ground area contour, the first buffer zone and the second buffer zone, respectively, to obtain a fusion rule;

[0130] Fuse the ground digital elevation data, the building digital elevation data and the vegetation digital elevation data based on the fusion rule to obtain the second digital elevation data of the target sub-region.

[0131] In an embodiment of the present application, for any target sub-region, the flight path adjustment module 22 is specifically configured to:

[0132] Perform feature extraction on the second digital elevation data and the corresponding first digital elevation data respectively to obtain a first feature vector and a second feature vector.

[0133] Calculate the similarity between the first feature vector and the second feature vector to obtain the similarity of the second digital elevation data of the target sub-region and the corresponding first digital elevation data.

[0134] In an embodiment of the present application, for any target sub-region, the flight path adjustment module 22 is specifically configured to:

[0135] Determine a first starting point and a first ending point of the first flight path in the target sub-region;

[0136] Determine an isohypse reference line based on the second digital elevation data of the target sub-region;

[0137] Determine an initial flight path based on the first starting point, the first ending point, the isohypse reference line and a preset flight path generation rule;

[0138] Adjust the initial flight path based on the position relationship between the initial flight path and an obstacle region in the target sub-region to obtain a second flight path; the obstacle region is determined based on the second digital elevation data of the target sub-region.

[0139] In an embodiment of the present application, for any target sub-region, the flight path adjustment module 22 is specifically configured to:

[0140] Replace the flight path between the first starting point and the first ending point on the first flight path with the second flight path of the target sub-region.

[0141] Referring to Figure 3 , Figure 3 The schematic block diagram of the electronic device provided in an embodiment of the present application is shown in FIG. 1. As shown in FIG. 1, the electronic device comprises a flight path adjustment module 22. Figure 3The electronic device 300 in the embodiment shown can 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 above-mentioned processors 301, input devices 302, output devices 303, and memories 304 complete communication with each other through a communication bus 305. The memory 304 is configured to store a computer program, and the computer program includes program instructions. The processor 301 is configured to execute the program instructions stored in the memory 304. Specifically, the processor 301 is configured to invoke the program instructions to execute the functions of each module / unit in the above-mentioned device embodiments, for example Figure 2 The functions of the route determination module 21 and the route adjustment module 22 shown.

[0142] It should be understood that, in the embodiments of the present application, the processor 301 can be a central processing unit (CPU), and the processor can 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 gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0143] The input device 302 can include a touchpad, a fingerprint collection sensor (used to collect fingerprint information and direction information of a fingerprint of a user), a microphone, etc., and the output device 303 can include a display (LCD, etc.), a speaker, etc.

[0144] The memory 304 can include read-only memory and random access memory, and provide instructions and data for the processor 301. A portion of the memory 304 can also include non-volatile random access memory. For example, the memory 304 can also store a preset constant such as a similarity threshold.

[0145] In specific implementations, the processor 301, the input device 302, and the output device 303 described in the embodiments of the present application can execute the implementation manners described in the unmanned aerial vehicle surveying and mapping method for geographic information collection provided by the embodiments of the present application, and can also execute the implementation manners of the electronic device described in the embodiments of the present application, which will not be described here.

[0146] In another embodiment of the present application, a computer readable storage medium is provided, which stores a computer program. The computer program includes program instructions, which, when executed by a processor, implement all or part of the processes of the above-mentioned embodiment methods. The computer program can also instruct related hardware to complete the implementation. The computer program can be stored in a computer readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate form. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0147] The computer readable storage medium can be an internal storage unit of the electronic device of any of the preceding embodiments, such as a hard disk or a 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, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the computer readable storage medium can include both the internal storage unit and the external storage device of the electronic device. The computer readable storage medium is used to store the computer program 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.

[0148] Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized in electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in general terms in the above description. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0149] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic device and the units described above can refer to the corresponding processes in the above-mentioned method embodiments, which will not be described here.

[0150] In several embodiments provided in the present application, it should be understood that the disclosed electronic device and method can be implemented in other manners. For example, the embodiments of the apparatus described above are merely illustrative, and the unit division is merely logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, or can be in electrical, mechanical or other forms.

[0151] The unit described as a separate component can or can not be physically separate, and the component shown as a unit can or can not be a physical unit, that is, can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0152] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The integrated module can be realized in the form of hardware or in the form of a software functional module.

[0153] The above is merely specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any skilled person in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for unmanned aerial vehicle surveying for geographic information acquisition, characterized in that, The method comprises the following steps: determining a first flight path based on first digital elevation data of a target region; the target region comprises a plurality of target sub-regions; performing a plurality of flight path adjustment operations of the UAV until reaching the end point of the first flight path to achieve geographic information mapping of the target region; each flight path adjustment operation comprises: controlling the flight of the UAV based on the first flight path; in response to detecting that the UAV has flown into any target sub-region, determining second digital elevation data of the target sub-region based on 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, determining a second flight path of the target sub-region based on the second digital elevation data of the target sub-region; and adjusting the first flight path based on the second flight path.

2. The unmanned aerial surveying method for geographic information collection of claim 1, wherein, For any target sub-region, the real-time detection data of the target sub-region comprises laser point cloud data, and the second digital elevation data of the target sub-region is determined based on the real-time detection data of the target sub-region, comprising: determining point cloud echo intensity, scanning angle and elevation distribution characteristics based on the laser point cloud data; classifying the laser point cloud data based on the point cloud echo intensity, scanning angle and elevation distribution characteristics to obtain a plurality of point cloud grouping data, and one group of point cloud grouping data corresponds to one ground object category; interpolating the plurality of point cloud grouping data to obtain the second digital elevation data of the target sub-region.

3. The unmanned aerial surveying method for geographic information collection of claim 2, wherein, The plurality of point cloud grouping data comprises ground point cloud data, building point cloud data and vegetation point cloud data. For any target sub-region, the interpolation of the plurality of point cloud grouping data to obtain the second digital elevation data of the target sub-region comprises: for the ground point cloud data in the plurality of point cloud grouping data, interpolating the point cloud grouping data 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 plurality of point cloud grouping data, interpolating 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 the vegetation point cloud data in the plurality of point cloud grouping data, interpolating the point cloud grouping data based on the natural neighborhood interpolation method to obtain the vegetation digital elevation data of the target sub-region; determining the second digital elevation data of the target sub-region based on the ground digital elevation data, the building digital elevation data and the vegetation digital elevation data.

4. The unmanned aerial surveying method for geographic information collection of claim 3, wherein, For any target sub-region, determining the second digital elevation data of the target sub-region based on the ground digital elevation data, the building digital elevation data and the vegetation digital elevation data comprises: extracting a ground region contour based on the ground point cloud data; extracting a building region contour based on the building point cloud data; extracting a vegetation region contour based on the vegetation point cloud data; determining a first buffer zone based on the building region contour; the first buffer zone is an annular region surrounding the building region contour; determining a second buffer zone based on the vegetation region contour; the second buffer zone is an annular region surrounding the vegetation region contour; different masks are respectively given to the building area contour, the vegetation area contour, the ground area contour, the first buffer area and the second buffer area; a preset mapping relationship is looked up based on the masks corresponding to the building area contour, the vegetation area contour, the ground area contour, the first buffer area and the second buffer area, to obtain a fusion rule; the ground digital elevation data, the building digital elevation data and the vegetation digital elevation data are fused based on the fusion rule, to obtain second digital elevation data of the target sub-region.

5. The unmanned aerial surveying method for geographic information collection of claim 1, wherein, For any target sub-region, the calculation manner of the similarity between the second digital elevation data and the corresponding first digital elevation data includes: feature extraction is respectively 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; the similarity between the first feature vector and the second feature vector is calculated, to obtain the similarity between the second digital elevation data and the corresponding first digital elevation data of the target sub-region.

6. The unmanned aerial surveying method for geographic information collection of claim 1, wherein, For any target sub-region, the second flight route of the target sub-region is determined based on the second digital elevation data of the target sub-region, and includes: a first starting point and a first ending point of the first flight route in the target sub-region are determined; an isohypse reference line is determined based on the second digital elevation data of the target sub-region; an initial flight route is determined based on the first starting point, the first ending point, the isohypse reference line and a preset flight route generation rule; the initial flight route is adjusted based on the position relationship between the initial flight route and an obstacle region in the target sub-region, to obtain the second flight route; the obstacle region is determined based on the second digital elevation data of the target sub-region.

7. The unmanned aerial surveying method for geographic information collection of claim 6, wherein, For any target sub-region, the first flight route is adjusted based on the second flight route, and includes: the flight route between the first starting point and the first ending point on the first flight route is replaced by the second flight route of the target sub-region.

8. A UAV surveying system for geographic information collection, characterized in that, includes: a flight route determination module configured to determine a first flight route based on first digital elevation data of a target region; the target region includes a plurality of target sub-regions; a flight route adjustment module configured to perform a flight route adjustment operation of a UAV multiple times until reaching an ending point of the first flight route, to realize geographic information mapping of the target region; wherein, any one flight route adjustment operation includes: flight control of the UAV based on the first flight route; in response to detecting that the UAV flies to any target sub-region, determining second digital elevation data of the target sub-region based on 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, determining a second flight route of the target sub-region based on the second digital elevation data of the target sub-region; and adjusting the first flight route based on the second flight route.

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, the processor executes the computer program to implement the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. the computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 7.

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