UAV Mapping Method and System Based on 3D Point Cloud Data Analysis

Abstract

The present invention discloses a UAV mapping method and system based on three-dimensional point cloud data analysis, which relates to the technical field of UAV mapping. It uses GIS to obtain high-altitude geographical images of the UAV and delimits mapping blocks; determines the altitude of key position nodes and constructs an initial three-dimensional model; analyzes laser data to generate a ground point cloud model, which is optimized by scale adjustment, rotation, and translation, and filters adjustment strategies with a high initial matching degree; determines the secondary matching degree through the comparison between the ground image and the characteristics of the mapping block, selects the optimal point cloud model, and outputs data in combination with mapping marks. The present invention innovatively integrates GIS positioning, node modeling, point cloud optimization, and image calibration, improves the mapping accuracy of complex terrains, and is applicable to fields such as engineering planning.

Description

Technical Field

[0001] The present invention relates to the technical field of UAV mapping, and in particular to a UAV mapping method and system based on three-dimensional point cloud data analysis. Background Technique

[0002] With the rapid development of UAV technology, UAV mapping has been widely used in fields such as geographic information collection, terrain modeling, and urban planning. Traditional UAV mapping methods mainly rely on optical images or single lidar data to generate terrain models through two-dimensional image stitching or sparse point clouds. However, in complex terrain environments (such as mountainous areas, hilly areas, or urban fringe areas), due to factors such as large terrain undulations, complex edge features, and vegetation occlusion, traditional methods often face problems such as insufficient mapping accuracy, difficulty in identifying key feature points, and low model matching efficiency.

[0003] In recent years, three-dimensional point cloud technology has attracted much attention because it can directly reflect the spatial structure characteristics of the ground surface. Combining lidar-based point cloud data with a UAV platform can obtain high-density three-dimensional coordinate information, providing the possibility for constructing a refined terrain model. However, there are still several limitations in the existing technology in practical applications. First, it is difficult for single point cloud data to be effectively aligned with a geographic information system (GIS) or other reference models, resulting in a large model deviation. Second, in the identification of key position nodes, traditional methods mostly rely on manual annotation or simple threshold segmentation, which is difficult to adapt to terrain diversity and affects the accuracy of the initial model. Summary of the Invention

[0004] The purpose of the present invention is to provide a UAV mapping method and system that can more accurately map the ground.

[0005] The present invention discloses a UAV mapping method based on three-dimensional point cloud data analysis, including:

[0006] Step S100, using a GIS information system to obtain a geographic image at the same height as the UAV, and demarcating a mapping block that needs to be mapped on the geographic image;

[0007] Step S200, determining the altitude of key position nodes on the mapping block, and constructing an initial mapping three-dimensional model based on the altitude of the key position nodes;

[0008] Step S300, analyzing the laser detection data of the UAV, constructing a ground three-dimensional point cloud model, and performing several initial model adjustments on the ground three-dimensional point cloud model. Each initial model adjustment includes scale adjustment, rotation, and translation. After each initial model adjustment, calculate the initial matching degree between the ground three-dimensional point cloud model and the initial mapping three-dimensional model, and screen and save the initial model adjustment strategies with an initial matching degree greater than or equal to a preset value;

[0009] Step S400: Perform image feature comparison and analysis on the ground image captured by the drone and the surveying and mapping block. Based on the analysis results, determine the secondary matching degree between the ground three-dimensional point cloud model and the initial surveying and mapping three-dimensional model. Based on the secondary matching degree, select the ground three-dimensional point cloud model corresponding to the optimal initial model adjustment strategy, and determine the output of the three-dimensional point cloud model surveying and mapping data based on the surveying and mapping marks on the surveying and mapping block.

[0010] In some embodiments of the present invention, the method for determining the key position nodes on the surveying and mapping block includes:

[0011] Step S201: Perform edge detection on the surveying and mapping block, and combine with DEM data to determine the geographical height beside each edge.

[0012] Step S202: Determine the edge continuous length and edge tortuous feature of each edge, and determine the geographical height difference feature between the two sides of the edge.

[0013] Step S203: Based on the edge continuous length, edge tortuous feature, and geographical height difference feature between the two sides of the edge, determine the edge criticality of the edge. Identify the edge with edge criticality meeting the preset standard as the key edge. Analyze the geographical height change rate of the key edge, identify the edge section with the geographical height change rate greater than or equal to the preset value as the key edge section. Set several key position nodes at preset intervals for the key edge section, identify the edge section outside the key edge section as the non-key edge section, and select the center point of the non-key edge section as the key position node.

[0014] In some embodiments of the present invention, the method for determining the edge tortuous feature includes:

[0015] Step S2031: Uniformly set several first edge detection points on the edge, randomly select several first edge detection points, record them as random detection points, and randomly combine the random detection points in pairs to obtain several random edge detection point groups. Connect the random edge detection points in each random edge detection point group to obtain several random edge detection lines.

[0016] Step S2032: Randomly combine the random edge detection lines, determine the included angle between the random edge detection lines in the combination, and calculate the average value of all the included angles, denoted as the average included angle. Identify the average included angle as the edge tortuous parameter.

[0017] In some embodiments of the present invention, the method for determining the geographical height difference feature between the two sides of the edge includes:

[0018] Step S2033: Set a number of second edge detection points evenly on the edge, and set height detection points on both sides of each second edge detection point, and the connection line between the height detection points is perpendicular to the edge;

[0019] Step S2034: Calculate the height difference between the opposite height detection points, and screen out the height differences greater than or equal to the preset value, which are recorded as the height differences to be concerned about. Identify the second edge detection points corresponding to the height differences to be concerned about as the edge detection points to be concerned about. Calculate the proportion of the number of edge detection points to be concerned about among all second edge detection points, and identify this proportion as the geographical height difference parameter.

[0020] In some embodiments of the present invention, the method for determining the edge criticality of an edge based on the edge continuous length, edge tortuosity feature, and geographical height difference feature on both sides of the edge includes:

[0021] Step S2035: Perform parametric definition conversion on the edge tortuosity feature to obtain the edge tortuosity parameter, and perform parametric definition conversion on the geographical height difference feature on both sides of the edge to obtain the geographical height difference parameter;

[0022] Step S2036: Determine the edge critical degree of the edge based on the edge continuous length, edge tortuosity parameter, and edge height difference parameter of the edge.

[0023] In some embodiments of the present invention, the method for constructing the initial mapping three-dimensional model and the ground three-dimensional point cloud model includes:

[0024] Step S204: Construct a three-dimensional coordinate system, and map the key position nodes into the three-dimensional coordinate system based on the location and altitude of the key position nodes to form the initial mapping three-dimensional model;

[0025] Step S205: Analyze the laser detection data and construct a laser point cloud in the three-dimensional coordinate system to form the ground three-dimensional point cloud model.

[0026] In some embodiments of the present invention, the method for determining the initial matching degree between the ground three-dimensional point cloud model and the initial mapping three-dimensional model includes:

[0027] Step S301: Overlap the initial mapping three-dimensional model and the ground three-dimensional point cloud model, and determine the laser mapping point closest to each key position node in the ground three-dimensional point cloud model. Denote the combination of the two as the model mapping point group, and calculate the inter-point distance;

[0028] Step S302: Determine the initial matching degree based on the positional relationship between the key position nodes and the inter-point distances of the model mapping point groups corresponding to each key position node.

[0029] In some embodiments of the present invention, the method for determining the secondary matching degree between the ground three-dimensional point cloud model and the initial surveying and mapping three-dimensional model includes:

[0030] Step S401: Grayscale the ground image and the surveying and mapping block respectively to obtain a grayscale ground image and a grayscale surveying and mapping block image, and overlap the grayscale ground image and the grayscale surveying and mapping block image;

[0031] Step S402: Randomly select several first grayscale image blocks from the grayscale ground image, and determine the first average grayscale value and the first average grayscale change rate of the first grayscale image block;

[0032] Step S403: Perform dynamic block scanning on the grayscale surveying and mapping block image, and determine the second average grayscale value and the second average grayscale change rate within the dynamic block during each dynamic block stay analysis. If the second average grayscale value of the dynamic block is the same as the first average grayscale value, and the second average grayscale change rate is the same as the first average grayscale change rate, then the dynamic block at this time is identified as the second grayscale block, calculate the block distance between the first grayscale block and the second grayscale block, and retain the second grayscale block whose block distance is less than or equal to the preset value;

[0033] Step 404: Statistically analyze the block distances between the corresponding first grayscale blocks and second grayscale blocks, and determine the proximity parameter between the first grayscale block and the second grayscale block based on the preset block distance interval to which the block distance belongs. Statistically analyze the sum of the proximity parameters corresponding to all block distances, which is recorded as the secondary matching degree.

[0034] In some embodiments of the present invention, a drone surveying and mapping system based on three-dimensional point cloud data analysis is also disclosed, including:

[0035] The first module is used to obtain a geographical image at the same altitude of the drone by using a GIS information system, and demarcate a surveying and mapping block that needs to be surveyed and mapped on the geographical image;

[0036] The second module is used to determine the altitude of key position nodes on the surveying and mapping block, and construct an initial surveying and mapping three-dimensional model based on the altitude of the key position nodes;

[0037] The third module is used to analyze the laser detection data of the drone, construct a ground three-dimensional point cloud model, and perform several initial model adjustments on the ground three-dimensional point cloud model. Each initial model adjustment includes scale adjustment, rotation, and translation. After each initial model adjustment, calculate the initial matching degree between the ground three-dimensional point cloud model and the initial surveying and mapping three-dimensional model, and screen and save the initial model adjustment strategies whose initial matching degree is greater than or equal to the preset value;

[0038] The fourth module is used to perform image feature comparison and analysis on the ground images captured by the drone and the surveying and mapping block, and based on the analysis results, determine the secondary matching degree between the ground three-dimensional point cloud model and the initial surveying and mapping three-dimensional model. Based on the secondary matching degree, select the ground three-dimensional point cloud model corresponding to the optimal initial model adjustment strategy, and determine the output of the three-dimensional point cloud model surveying and mapping data based on the surveying and mapping marks on the surveying and mapping block.

[0039] The present invention discloses a drone surveying and mapping method and system based on three-dimensional point cloud data analysis, which relates to the technical field of drone surveying and mapping. It uses GIS to obtain high-altitude geographical images of the drone, delimits the surveying and mapping block; determines the altitude of key position nodes, constructs the initial three-dimensional model; analyzes the laser data, generates the ground point cloud model, optimizes it through scale adjustment, rotation, and translation, and screens the adjustment strategies with high initial matching degree; determines the secondary matching degree through the comparison of the ground image and the surveying and mapping block features, selects the optimal point cloud model, and outputs the data in combination with the surveying and mapping marks. The present invention innovatively integrates GIS positioning, node modeling, point cloud optimization, and image calibration, improves the surveying and mapping accuracy of complex terrains, and is applicable to fields such as engineering planning.

[0040] The technical solution of the present invention will be further described in detail below through the accompanying drawings and embodiments. Description of the Drawings

[0041] Figure 1 It is a method step diagram of the drone surveying and mapping method based on three-dimensional point cloud data analysis disclosed in the embodiment of the present invention. Detailed Embodiments

[0042] The technical solution of the present invention will be further described below through the accompanying drawings and embodiments.

[0043] The technical solution of the present invention will be clearly and completely described below in combination with the accompanying drawings and specific embodiments. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and cannot be construed as limiting the protection scope of the present invention. Those skilled in the art can make some non-essential improvements and adjustments based on the content of the present invention below. In the present invention, unless otherwise clearly defined and limited, the technical terms used in the present invention should have the ordinary meaning understood by those skilled in the art of the present invention.

[0044] Embodiment:

[0045] The present invention discloses a drone surveying and mapping method based on three-dimensional point cloud data analysis. Refer to Figure 1 , including:

[0046] Step S100, using the GIS information system, obtain the geographical images at the same altitude of the drone, and delimit the surveying and mapping block that needs to be surveyed on the geographical images.

[0047] The principle of step S100 is to use Geographic Information System (GIS) to obtain the geographical image corresponding to the flight altitude of the drone, and on this basis, delimit the block to be surveyed. The core of this process lies in converting the geographical data in the real world into operable digital information, laying the foundation for subsequent three-dimensional point cloud analysis. The GIS system can generate a two-dimensional geographical image consistent with the flight altitude of the drone by integrating satellite images, map data, and elevation information. This image not only contains surface features (such as rivers, mountains, buildings, etc.), but also can be corresponding to the actual geographical location through the coordinate system. Delimiting the surveyed block is to circle a specific area on the geographical image according to the task requirements, such as a mountainous area or a building complex area that needs to be surveyed for terrain. This process is similar to "selecting" the target area on a digital map, ensuring that the subsequent survey work focuses on the key areas. For example, assuming that the slope change of a hillside needs to be surveyed, the GIS system can provide the satellite image of the area and delimit the scope of the hillside in combination with the task requirements. Through this step, the system can clarify the boundary and scope of the survey, avoid redundant data collection, and at the same time provide a spatial reference for the identification of subsequent key nodes and model construction. The application of GIS improves the accuracy and efficiency of the survey because it not only provides visual geographical information, but also can preset the initial conditions for three-dimensional modeling through data layer superposition (such as elevation data). The essence of this process is to abstract the complex real geographical environment into a computable two-dimensional plane, providing a clear "starting line" for drone surveying.

[0048] Step S200: Determine the altitude of the key position nodes on the surveyed block, and construct an initial surveyed three-dimensional model based on the altitude of the key position nodes.

[0049] The principle of step S200 is to construct an initial 3D mapping model by determining the elevation of key position nodes within the mapping block, providing a reference benchmark for subsequent point cloud data analysis. The core of this process lies in extracting 3D information from 2D geographical images and using key position nodes as representative points of terrain features. The determination of key position nodes usually depends on the significance of terrain features, such as ridges, valleys, or surface mutations, which can reflect the main height changes in the mapping area. By combining Digital Elevation Model (DEM) data, accurate elevation values can be assigned to each key node. For example, when mapping a hilly area, the mountaintop, the foot of the mountain, and the slope turning points may be selected as key nodes, and the height data of these points (such as 500 meters for the mountaintop and 200 meters for the foot of the mountain) are obtained from the DEM. Subsequently, based on the elevation and geographical coordinates of these nodes, an initial mapping model is constructed in a 3D coordinate system. This model is essentially a rough 3D framework, similar to a terrain "skeleton" built with several key support points. Its function is to provide a comparison benchmark for subsequent laser point cloud data, avoiding deviations that may occur when directly modeling using the raw data collected by drones. For example, if directly modeling with point cloud data, terrain distortion may occur due to vegetation occlusion or equipment errors, while the initial model presets the overall contour of the terrain through key nodes. The key to this process lies in the representativeness of node selection and the accuracy of elevation data, which together determine whether the initial model can effectively guide subsequent adjustments. Through this step, the mapping system can establish a preliminary spatial awareness in complex terrains, laying a foundation for refined modeling.

[0050] In step S300, the laser detection data of the drone are analyzed to construct a ground 3D point cloud model, and several initial model adjustments are performed on the ground 3D point cloud model. Each initial model adjustment includes scale adjustment, rotation, and translation. After each initial model adjustment, the initial matching degree between the ground 3D point cloud model and the initial mapping 3D model is calculated, and the initial model adjustment strategies with an initial matching degree greater than or equal to the preset value are screened and saved.

[0051] The principle of step S300 is to construct a ground three-dimensional point cloud model by analyzing the laser detection data of the UAV, and make it aligned with the initial surveying and mapping model through multiple adjustments, thereby improving the surveying and mapping accuracy. The core of this process lies in converting the discrete point cloud data collected by the lidar into a continuous three-dimensional terrain representation, and realizing the fusion with the initial model through iterative optimization. The laser detection data contains a large number of three-dimensional coordinate points, reflecting the true shape of the ground surface, such as ground undulations, vegetation heights or building outlines. The system first maps these points into a three-dimensional coordinate system to form a ground point cloud model. However, since the initial model is only based on key nodes, its details and accuracy are limited. Therefore, it is necessary to adjust the point cloud model. The adjustments include scale scaling (matching the overall scale), rotation (correcting the direction deviation), and translation (aligning the spatial position). After each adjustment, the initial matching degree of the two models is calculated. The matching degree may be evaluated by the distance between points or the overlap rate. For example, if the average distance between the ground points in the point cloud model and the nodes of the initial model is less than 0.3 meters, the matching degree is relatively high. The system filters out the adjustment strategies with a matching degree greater than the preset value (such as 85%) and saves them. For example, when surveying a farmland, the initial model may only reflect the positions of the ridges, while the point cloud model contains details of crops and soil. By adjusting, the crop interference can be eliminated and the real ground can be approximated. The significance of this process is to use the high resolution of the point cloud data to make up for the roughness of the initial model, and at the same time reduce errors through multiple iterative optimizations. For example, if the initial model is deflected by 5 degrees, the rotation adjustment can correct it and improve the coincidence degree of the two models. This step is essentially the preliminary fusion of the point cloud data and the preset framework, providing a reliable intermediate model for subsequent image feature comparison, ensuring that the surveying and mapping results not only conform to the macroscopic terrain but also have microscopic accuracy.

[0052] Step S400: Perform image feature comparison and analysis on the ground images captured by the UAV and the surveying and mapping block. Based on the analysis results, determine the secondary matching degree between the ground three-dimensional point cloud model and the initial surveying and mapping three-dimensional model. Based on the secondary matching degree, select the ground three-dimensional point cloud model corresponding to the optimal initial model adjustment strategy, and determine the output of the three-dimensional point cloud model surveying and mapping data based on the surveying and mapping marks on the surveying and mapping block.

[0053] The principle of step S400 is to compare and analyze the features of the ground images captured by the drone with the surveying and mapping block, determine the secondary matching degree between the ground three-dimensional point cloud model and the initial surveying and mapping model, and based on this, select the ground three-dimensional point cloud model optimized by the optimal initial model adjustment strategy. Finally, the surveying and mapping data is output in combination with the surveying and mapping marks. The core of this process lies in using the image information to conduct secondary verification on the multiple adjusted point cloud models generated in step S300 to ensure their alignment accuracy with the actual terrain. The significance of this process is to introduce an additional calibration dimension through image features to make up for the possible local deviations in the laser point cloud (such as height errors caused by vegetation occlusion), so as to select the model that is closest to the real terrain. For example, in forest areas, image comparison can help distinguish the ground from the tree canopy and optimize the point cloud model. The finally output surveying and mapping data not only reflects the terrain height, but also is accurately aligned with the actual landform features, and is applicable to scenarios such as agricultural planning or disaster assessment.

[0054] In some embodiments of the present invention, the method for determining the key position nodes on the surveying and mapping block includes:

[0055] Step S201, perform edge detection on the surveying and mapping block, and in combination with DEM data, determine the geographical height beside each edge.

[0056] The principle of step S201 is to identify significant terrain boundaries within the surveyed area through edge detection technology, and assign precise geographical heights to both sides of these boundaries by combining Digital Elevation Model (DEM) data, providing basic data for the determination of subsequent key nodes. The core of this process lies in converting the two-dimensional geographical image of the surveyed area into a structured feature containing height information, using edges as the initial indicators of terrain changes. Edge detection typically employs image processing algorithms (such as the Canny or Sobel operators), which identify distinct dividing lines of surface features by analyzing pixel grayscale or color gradients, such as ridgelines, river edges, or abrupt slope changes. For example, when surveying a hilly area, edge detection may identify a ridgeline, which appears as a significant change in brightness or color on the geographical image. Combining DEM data, the system assigns height values to both sides of this edge. For instance, the elevation on the left side of the ridgeline is 450 meters, and on the right side is 400 meters. The introduction of this height information is crucial as it transforms the two-dimensional edge into a representative of three-dimensional terrain features, providing a spatial dimension for subsequent analysis. For example, assume the survey target is a coastal wetland. Edge detection may identify the boundary between the coastline and the inland marsh, and the DEM data shows that the coastline height is 2 meters and the marsh is 0.5 meters. The significance of this process is to screen out the key contours of the terrain through edge detection and supplement the details with the elevation accuracy of the DEM, avoiding height changes that may be overlooked by relying solely on image information. Determining the heights on the sides of the edge not only lays the foundation for quantifying terrain features but also provides data support for evaluating the importance of edges in subsequent steps. Through this step, the system can extract preliminary structured information from the complex geographical environment, laying a solid foundation for the positioning of key location nodes.

[0057] Step S202: Determine the continuous length of each edge and the edge tortuosity feature, and determine the geographical height difference feature on both sides of the edge.

[0058] The principle of step S202 is to conduct feature quantization analysis on each edge identified in step S201, determine its continuous length, tortuous feature, and the geographical height difference on both sides, so as to comprehensively describe the topographic significance of the edge and provide a basis for critical evaluation. The core of this process lies in extracting computable parameters from the morphological and height characteristics of the edge to ensure that the subsequent screening can reflect the actual changes in the terrain. The continuous length of the edge refers to the distance that the edge extends in the geographical space. For example, a ridge line may continuously extend for 3 kilometers, reflecting the size of its coverage. The tortuous feature describes the shape complexity of the edge and is quantified by analyzing the degree of curvature or the frequency of turning of the edge. For example, the edge of a smooth river may have a low tortuosity, while the edge of a serrated fault has a high tortuosity. The geographical height difference on both sides of the edge is calculated by comparing the height values on both sides of the edge in the DEM data. For example, one side of a ridge is 500 meters and the other side is 420 meters, with a difference of 80 meters. For example, when mapping a forest mountain area, the system may identify a valley edge with a continuous length of 1.5 kilometers, a tortuous feature manifested as multiple sharp turns (high tortuosity), and a height difference of 60 meters on both sides. The determination of these features requires the comprehensive processing of image analysis and height data to ensure that the results reflect both the geometric characteristics of the edge and its topographic significance. The significance of this process is to abstract the edge from a simple line into a topographic element with multi-dimensional attributes, providing a quantitative basis for subsequent critical judgments. For example, a short and straight edge may only represent local undulations, while a long, tortuous, and large-height-difference edge may indicate an important topographic boundary. Through this step, the system can refine the description of the edge and provide comprehensive data support for the screening of key nodes.

[0059] Step S203: Based on the continuous length of the edge, the tortuous feature of the edge, and the geographical height difference feature on both sides of the edge, determine the edge criticality of the edge, and identify the edge whose edge criticality meets the preset standard as a key edge. Analyze the geographical height change rate of the key edge, identify the edge section whose geographical height change rate is greater than or equal to the preset value as a key edge section, set several key position nodes at preset intervals for the key edge section, identify the edge section outside the key edge section as a non-key edge section, and select the center point of the non-key edge section as a key position node.

[0060] The principle of step S203 is to comprehensively evaluate the criticality of the edge based on the continuous length, tortuous characteristics, and height difference on both sides of the edge, screen out the critical edges, and further analyze the height change rate thereof, so as to determine the critical position nodes. The core of this process lies in identifying the most representative areas in the terrain through weighted analysis of multi-dimensional features and reasonably distributing critical nodes on this basis. First, the system calculates the criticality score of the edge according to preset criteria (such as a length greater than 1 km, a tortuosity higher than a certain threshold, and a height difference greater than 30 m), and determines the edges that meet the conditions as critical edges. For example, a ridge line 2 km long, with obvious tortuosity and a height difference of 50 m may be rated as a critical edge. Then, analyze the geographical height change rate of the critical edge, that is, the height change per unit distance. For example, the height changes 4 m every 10 m. If the change rate is greater than the preset value (such as 2 m / 10 m), then this section is determined as a critical edge section. In these sections, set critical nodes at preset intervals (such as 50 m) to ensure capturing the sharp change points of the terrain. For non-critical edge sections (such as gentle sections), take the center point thereof as the critical node to cover relatively flat areas. For example, when mapping the edge of a river, set a node every 50 m in the steep riverbank section (large height difference, high change rate), and take the midpoint in the gentle section (small height difference). The significance of this process is to accurately locate the terrain feature points through criticality screening and change rate analysis, avoiding over-dense distribution of nodes or omission of important areas. For example, in mountain mapping, the dense distribution of nodes on the steep slope can reflect the terrain details, while a single node on the gentle slope is sufficient to represent the overall trend. Through this step, the system transforms the edge features into a specific set of three-dimensional nodes, providing reliable support points for the construction of the initial mapping model and being applicable to the precise mapping of complex terrains.

[0061] In some embodiments of the present invention, the method for determining the tortuous characteristics of the edge includes:

[0062] Step S2031, uniformly set a plurality of first edge detection points on the edge, randomly select a plurality of first edge detection points, record them as random detection points, and randomly combine the random detection points in pairs to obtain a plurality of random edge detection point groups, and connect the random edge detection points in each random edge detection point group to obtain a plurality of random edge detection lines.

[0063] The principle of step S2031 is to generate a set of random edge detection lines by uniformly setting detection points on the edge and performing random combinations, providing a geometric basis for subsequent quantification of edge tortuosity features. The core of this process lies in discretizing the continuous form of the edge into a set of computable points and lines for analyzing its shape complexity. First, the system uniformly distributes a number of first edge detection points on the edge. For example, one point is set every 10 meters along a 1-kilometer-long ridge line, with a total of 100 detection points. The positions of these points are based on the actual path of the edge to ensure coverage of the entire edge range. Subsequently, a number of points are randomly selected from these detection points as random detection points. For example, 10 points are randomly selected and denoted as P1, P2, …, P10. Then, these random detection points are combined in pairs to form random edge detection point groups, such as (P1, P2), (P2, P3), (P5, P7), etc. A random edge detection line is generated by connecting the two points in each group. Assuming the coordinates of P1 are (0, 0), P2 are (10, 5), and P3 are (15, 15), then the connecting lines P1 - P2 and P2 - P3 are respectively a straight-line segment, and a total of dozens of such detection lines may be generated. The significance of this process is to decompose the overall shape of the edge into multiple local line segments through random sampling and connection, reflecting the possibility of its direction change. For example, when mapping the edge of a meandering river, the detection points may be distributed at the bends of the riverbank, and the detection lines generated after connection will show different inclination angles, while a straight edge may generate nearly parallel line segments.

[0064] In step S2032, the random edge detection lines are randomly combined, the inter-line angle between the random edge detection lines in the combination is determined, and the average value of all inter-line angles is calculated and denoted as the average inter-line angle. The average inter-line angle is regarded as the edge tortuosity parameter.

[0065] Step S2032 calculates the inter-line angles between the random edge detection lines generated in step S2031 and takes their average as the edge tortuosity parameter to quantify the degree of edge curvature. The core of this process is to utilize changes in geometric angles to reflect the tortuosity characteristics of the edge, providing an important basis for identifying key location nodes. First, the system randomly combines the random edge detection lines generated in step S2031. For example, several pairs are selected from the 10 detection lines, such as (P1-P2, P2-P3), (P3-P4, P5-P6). For each pair of detection lines, the inter-line angle is calculated. For example, if the direction vector of the P1-P2 line segment is (10, 5) and that of the P2-P3 line segment is (5, 10), the angle is calculated to be approximately 45 degrees using a vector angle formula (such as the law of cosines). Next, the system counts the inter-line angles of all combinations and calculates the average, which is recorded as the average inter-line angle. For example, if mapping a ridge edge, five pairs of detection lines are generated with angles of 30°, 60°, 45°, 20°, and 50°, respectively. The average interline angle is (30+60+45+20+50) / 5 = 41°, which is defined as the edge tortuosity parameter. The significance of this parameter lies in the fact that larger angles indicate more dramatic changes in the direction of the edge and a higher degree of tortuosity. For example, the edge of a straight highway may have an average angle close to 0°, indicating a low tortuosity parameter; whereas the edge of a winding mountain road may have an average angle of 60°, indicating a high tortuosity parameter, reflecting the complexity of the terrain. This process uses geometric analysis to convert the morphological characteristics of the edge into a single numerical value, which can be combined with other features (such as length and elevation difference) to assess edge criticality. For example, when mapping a forest boundary, if the average interline angle is 55°, it indicates that the boundary is tortuous and variable, likely containing multiple turning points, and requires special attention. Through this step, the system realizes the quantification of edge tortuosity characteristics, providing a scientific basis for subsequent key edge screening.

[0066] In some embodiments of the present invention, the method for determining the geographical height difference characteristics on both sides of the edge includes:

[0067] Step S2033: setting a plurality of second edge detection points evenly on the edge, and setting height detection points on both sides of each second edge detection point, with the line connecting the height detection points being perpendicular to the edge;

[0068] Step S2034, calculate the height difference between the relative height detection points, and filter out the height difference that is greater than or equal to the preset value, record it as the height difference that needs attention, and identify the second edge detection point corresponding to the height difference that needs attention as the edge detection point that needs attention, calculate the ratio of the number of edge detection points that need attention to all second edge detection points, and identify the number ratio as the geographical height difference parameter.

[0069] The principle of step S2034 is to calculate the height difference between height detection points, screen out the regions with significant differences, and quantify the geographical height difference characteristics on both sides of the edge in the form of quantity proportion, providing a basis for edge criticality assessment. The core of this process lies in extracting feature points with topographic significance from local height data and converting them into global parameters. First, the system calculates the height difference between the height detection points on both sides of each second edge detection point. For example, if the height on the left side of a detection point is 450 meters and the height on the right side is 420 meters, the difference is 30 meters. Then, a preset value (such as 20 meters) is set, and the height differences greater than or equal to this value are screened out and recorded as the height differences to be concerned. This detection point is identified as an edge detection point to be concerned. For example, among 50 detection points, if the height differences of 10 points exceed 20 meters (such as 30 meters, 25 meters, etc.), then these 10 points are the points to be concerned. Subsequently, the proportion of the edge detection points to be concerned among all the second edge detection points is calculated. For example, 10 / 50 = 20%, and this proportion is the geographical height difference parameter. The significance of this parameter is that it reflects the significant degree and distribution frequency of the height changes on both sides of the edge. For example, when mapping the edge of a gentle ridge, if only 2 points have height differences exceeding 20 meters, accounting for only 4%, it indicates that the height difference is not significant; while at the edge of a steep cliff, if 40 points have exceeded the standard height differences, accounting for 80%, it indicates a drastic topographic change. Through this process of screening and proportion calculation, the local height differences are transformed into a feature description of the overall edge. For example, in coastal cliff mapping, the detection points may show that the height difference between the cliff top (50 meters) and the bottom (5 meters) reaches 45 meters, and most points are marked as points to be concerned, with a high proportion, reflecting a strong topographic boundary. Through this step, the system quantifies the height difference characteristics on both sides of the edge, providing a scientific basis for the subsequent identification of key edges and being applicable to the analysis of complex terrains.

[0070] In some embodiments of the present invention, the method for determining the edge criticality of an edge based on the edge continuous length, edge tortuosity characteristics, and geographical height difference characteristics on both sides of the edge includes:

[0071] Step S2035, perform a parametric definition conversion on the edge tortuosity characteristics to obtain an edge tortuosity parameter, and perform a parametric definition conversion on the geographical height difference characteristics on both sides of the edge to obtain a geographical height difference parameter.

[0072] Step S2036, determine the edge critical degree of the edge based on the edge continuous length, edge tortuosity parameter, and edge height difference parameter of the edge.

[0073] Among them, the expression for calculating the edge critical degree is:

[0074] ;

[0075] Among them, G is the edge criticality of the edge, L is the edge continuous length, is the weight adjustment coefficient affected by the edge tortuosity parameter, is the weight adjustment coefficient affected by the edge height difference parameter, q is the edge tortuosity parameter, h is the edge height difference parameter, and b is the adjustment coefficient affected by the edge feature.

[0076] Among them, the edge continuous length L represents the extension scale of the edge in space (such as the continuous length of the mountain ridge line or the valley), and directly determines the benchmark magnitude of the criticality as a global multiplier; the edge tortuosity parameter q describes the bending complexity of the edge shape, usually calculated by the mean of the included angles of adjacent line segments or the curvature integral, and the larger the value, the more drastic the change in the edge direction (for example, the q of the serrated gully is significantly higher than that of the gentle edge); the edge height difference parameter h characterizes the elevation mutation of the terrain on both sides of the edge, and can be quantified by the absolute value of the height difference or the gradient change rate (for example, the height difference h of the cliff boundary is much larger than that of the gentle slope); the weight adjustment coefficients and respectively control the contribution ratios of the tortuosity and the height difference (for example = 0.7, = 0.3, more emphasizing the morphological complexity); the adjustment coefficient b is used as the bias term of the exponential term to compensate for the basic differences of different terrain categories (for example, b = -0.5 is set in the plain area to reduce the criticality of the edges in the flat area). The formula converts the linearly combined parameters into a non-linearly growing criticality value through exponential operations. For example, when an edge with a length of 800 m has q = 0.6, h = 1.0, and = 0.5, = 0.4, b = 0.2, it is calculated that G≈1968, and when it is significantly higher than the threshold, it is determined as a critical edge. This design can adaptively enhance the weights of the edges in complex terrains such as steep mountainous areas and dense gullies, while suppressing the redundant interference of the gentle terrain, providing an accurate feature screening basis for UAV mapping and 3D model reconstruction.

[0077] In some embodiments of the present invention, the method for constructing the initial mapping 3D model and the ground 3D point cloud model includes:

[0078] Step S204, constructing a 3D coordinate system, and mapping the key position nodes into the 3D coordinate system based on the belonging positions and altitudes of the key position nodes to form an initial mapping 3D model.

[0079] Step S205, analyzing the laser detection data, and constructing a laser point cloud in the 3D coordinate system to form a ground 3D point cloud model.

[0080] In some embodiments of the present invention, the method for determining the initial matching degree between the ground 3D point cloud model and the initial mapping 3D model includes:

[0081] Step S301: Overlap the initial surveyed 3D model and the ground 3D point cloud model, and determine the laser mapping points in the ground 3D point cloud model that are closest to each key position node. Denote the combination of the two as the model mapping point group, and calculate the distance between points.

[0082] Step S302: Determine the initial matching degree based on the positional relationship between key position nodes and the distance between points in the model mapping point group corresponding to each key position node.

[0083] Among them, the expression for calculating the initial matching degree is:

[0084] ;

[0085] Among them, is the initial matching degree, is the matching parameter determination function corresponding to the i-th key position node, Based on the preset distance interval to which the distance between points corresponding to the i-th key position node belongs, output the corresponding matching parameter. Among them, the smaller the distance between points, the larger the matching parameter. n is the total number of key position nodes, is the continuous matching judgment function corresponding to the i-th key position node. If the distance between the i-th key position node and the most adjacent key position node is less than or equal to the preset value, and the distances between points corresponding to both are less than or equal to the preset value, then output 1, otherwise output 0. R is the continuous matching influence adjustment coefficient, and c is the continuous matching influence adjustment constant.

[0086] The formula quantifies the spatial alignment quality between the ground 3D point cloud model and the reference model through the synergistic effect of discrete key point matching accuracy and continuous region matching consistency. Among them, the discrete matching term represents the sum of the matching parameters of all key position nodes (such as mountaintop points, valley points). Its core logic is that the smaller the distance between points, the larger the matching parameter is (for example, when the distance d ≤ 0.5m, = 1; when 0.5 < d ≤ 1m, = 0.5), which directly reflects the positioning accuracy of a single node. The continuous matching enhancement term )]]amplifies the contribution of the continuous matching region through an exponential function. Among them, the continuous matching judgment function needs to meet two conditions simultaneously: the spatial distance between the i-th node and the most adjacent node ≤ the preset value (such as 50m), and the distances between points of both are ≤ the preset threshold (such as 1m). When satisfied, = 1, otherwise 0. Its average value Characterize the spatial continuity ratio of successful adjacent node matching; the adjustment coefficient R controls the amplification intensity of the total score of continuous matching (for example, when R = 0.3, every 10% of the continuous matching rate can increase the total score by about 3.5%), and the adjustment constant c is used to set the basic amplification factor (for example, c = 0.5 can prevent the exponential term from approaching zero when the continuous matching rate is extremely low).

[0087] In some embodiments of the present invention, the method for determining the secondary matching degree between the ground three-dimensional point cloud model and the initial surveying and mapping three-dimensional model includes:

[0088] Step S401, grayscale the ground image and the surveying and mapping block respectively to obtain the ground image grayscale map and the surveying and mapping block grayscale map, and overlap the ground image grayscale map and the surveying and mapping block grayscale map;

[0089] Step S402, randomly select several first grayscale map blocks for the ground image grayscale map, and determine the first average grayscale value and the first average grayscale change rate of the first grayscale map block.

[0090] Step S403, perform dynamic block scanning on the surveying and mapping block grayscale map, and determine the second average grayscale value and the second average grayscale change rate within the dynamic block during each dynamic block stay analysis. If the second average grayscale value of the dynamic block is the same as the first average grayscale value, and the second average grayscale change rate is the same as the first average grayscale change rate, then the dynamic block at this time is recognized as the second grayscale block, calculate the block distance between the first grayscale block and the second grayscale block, and retain the second grayscale block whose block distance is less than or equal to the preset value.

[0091] Step 404, count the block distances between the corresponding first grayscale blocks and second grayscale blocks, and determine the proximity parameter between the first grayscale block and the second grayscale block based on the preset block distance interval to which the block distance belongs. Count the sum of the proximity parameters corresponding to all block distances, which is recorded as the secondary matching degree.

[0092] In some embodiments of the present invention, a drone surveying and mapping system based on three-dimensional point cloud data analysis is also disclosed, including:

[0093] The first module is used to obtain the geographical image at the same altitude of the drone by using the GIS information system, and demarcate the surveying and mapping block that needs to be surveyed on the geographical image;

[0094] The second module is used to determine the altitude of the key position nodes on the surveying and mapping block, and construct an initial surveying and mapping three-dimensional model based on the altitude of the key position nodes;

[0095] The third module is used to analyze the laser detection data of the drone, construct a ground three-dimensional point cloud model, and perform several initial model adjustments on the ground three-dimensional point cloud model. Each initial model adjustment includes scale adjustment, rotation, and translation. After each initial model adjustment, calculate the initial matching degree between the ground three-dimensional point cloud model and the initial surveying and mapping three-dimensional model, and screen and save the initial model adjustment strategies with the initial matching degree greater than or equal to the preset value;

[0096] The fourth module is used to perform image feature comparison and analysis on the ground images captured by the drone and the surveying and mapping block, and based on the analysis results, determine the secondary matching degree between the ground three-dimensional point cloud model and the initial surveying and mapping three-dimensional model. Based on the secondary matching degree, select the ground three-dimensional point cloud model corresponding to the optimal initial model adjustment strategy, and determine the output of the three-dimensional point cloud model surveying and mapping data based on the surveying and mapping marks on the surveying and mapping block.

[0097] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by hardware or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various implementation scenarios of the present invention.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A UAV mapping method based on three-dimensional point cloud data analysis, characterized in that, Including: Step S100: Using a GIS information system, obtain a geographical image at the same altitude as the drone, and demarcate a survey block on the geographical image that needs to be surveyed and mapped; Step S200: Determine the altitude of key position nodes on the survey block, and based on the altitude of the key position nodes, construct an initial survey 3D model; Step S300: Analyze the laser detection data of the drone, construct a ground 3D point cloud model, and perform several initial model adjustments on the ground 3D point cloud model. Each initial model adjustment includes scale adjustment, rotation, and translation. After each initial model adjustment, calculate the initial matching degree between the ground 3D point cloud model and the initial survey 3D model, and screen and save the initial model adjustment strategies with an initial matching degree greater than or equal to the preset value; Step S400: Perform image feature comparison and analysis on the ground image captured by the drone and the survey block. Based on the analysis results, determine the secondary matching degree between the ground 3D point cloud model and the initial survey 3D model. Based on the secondary matching degree, select the ground 3D point cloud model corresponding to the optimal initial model adjustment strategy, and determine the output of the 3D point cloud model survey data based on the survey marks on the survey block; The method for determining key position nodes on the survey block includes: Step S201: Perform edge detection on the survey block, and in combination with DEM data, determine the geographical height beside each edge; Step S202: Determine the edge continuous length and edge zigzag characteristics of each edge, and determine the geographical height difference characteristics on both sides of the edge; Step S203: Based on the edge continuous length, edge zigzag characteristics, and geographical height difference characteristics on both sides of the edge, determine the edge criticality of the edge. Identify the edge with edge criticality meeting the preset standard as the key edge. Analyze the geographical height change rate of the key edge, identify the edge section with a geographical height change rate greater than or equal to the preset value as the key edge section. Set several key position nodes at preset intervals for the key edge section, identify the edge section outside the key edge section as the non-key edge section, and select the center point of the non-key edge section as the key position node; The method for constructing the initial survey 3D model and the ground 3D point cloud model includes: Step S204: Construct a 3D coordinate system, and map the key position nodes into the 3D coordinate system based on the location and altitude of the key position nodes to form an initial survey 3D model; Step S205: Analyze the laser detection data and construct a laser point cloud in the 3D coordinate system to form a ground 3D point cloud model; The method for determining the secondary matching degree between the ground 3D point cloud model and the initial survey 3D model includes: Step S401: Grayscale the ground image and the survey block respectively to obtain a grayscale ground image and a grayscale survey block image, and overlap the grayscale ground image and the grayscale survey block image; Step S402: Randomly select a number of first grayscale map blocks from the ground image grayscale map, and determine the first average grayscale value and the first average grayscale change rate of the first grayscale map block; Step S403: Conduct a dynamic block scan on the surveyed block grayscale map, and determine the second average grayscale value and the second average grayscale change rate within the dynamic block during each dynamic block stay analysis. If the second average grayscale value of the dynamic block is the same as the first average grayscale value and the second average grayscale change rate is the same as the first average grayscale change rate, then recognize the dynamic block at this time as the second grayscale block, calculate the block distance between the first grayscale block and the second grayscale block, and retain the second grayscale block whose block distance is less than or equal to the preset value; Step 404: Statistically calculate the block distances between the corresponding first grayscale blocks and second grayscale blocks, and based on the preset block distance intervals to which the block distances belong, determine the proximity parameter between the first grayscale block and the second grayscale block, and statistically calculate the sum of the proximity parameters corresponding to all block distances, which is denoted as the secondary matching degree.

2. The UAV mapping method based on three-dimensional point cloud data analysis according to claim 1, characterized in that The method for determining the edge zigzag feature includes: Step S2031: Uniformly set a number of first edge detection points on the edge, randomly select a number of first edge detection points, denoted as random detection points, and randomly combine the random detection points in pairs to obtain a number of random edge detection point groups, and connect the random edge detection points in each random edge detection point group to obtain a number of random edge detection lines; Step S2032: Randomly combine the random edge detection lines, determine the included angle between the random edge detection lines in the combination, and calculate the average value of all included angles, denoted as the average included angle, and recognize the average included angle as the edge zigzag parameter.

3. The UAV mapping method based on three-dimensional point cloud data analysis according to claim 1, wherein The method for determining the geographical height difference feature on both sides of the edge includes: Step S2033: Uniformly set a number of second edge detection points on the edge, and set height detection points on both sides of each second edge detection point, and the connection line between the height detection points is perpendicular to the edge; Step S2034: Calculate the height difference amount between the opposite height detection points, screen out the height difference amounts greater than or equal to the preset value, denoted as the height difference amounts to be concerned, and recognize the second edge detection points corresponding to the height difference amounts to be concerned as the edge detection points to be concerned, calculate the proportion of the number of edge detection points to be concerned corresponding to the edge detection points to be concerned among all second edge detection points, and recognize the proportion as the geographical height difference parameter.

4. The UAV mapping method based on three-dimensional point cloud data analysis according to claim 1, characterized in that The method for determining the edge criticality of the edge based on the edge continuous length, edge zigzag feature, and geographical height difference feature on both sides of the edge includes: Step S2035: Conduct a parametric definition conversion on the edge zigzag feature to obtain the edge zigzag parameter, and conduct a parametric definition conversion on the geographical height difference feature on both sides of the edge to obtain the geographical height difference parameter; Step S2036: Based on the edge continuous length, edge zigzag parameter, and edge height difference parameter of the edge, determine the edge critical degree of the edge.

5. The UAV mapping method based on three-dimensional point cloud data analysis according to claim 1, characterized in that The method for determining the initial matching degree between the ground three-dimensional point cloud model and the initial surveyed three-dimensional model includes: Step S301: Overlap the initial surveying and mapping 3D model with the ground 3D point cloud model, determine the laser mapping point closest to each key position node in the ground 3D point cloud model, denote the combination of the two as the model mapping point group, and calculate the distance between points. Step S302: Determine the initial matching degree based on the positional relationship between the key position nodes and the distance between points of each model mapping point group corresponding to the key position nodes.

6. An unmanned aerial vehicle mapping system based on three-dimensional point cloud data analysis, characterized in that, The method for unmanned aerial vehicle surveying and mapping for implementing any one of claims 1 - 5 includes: The first module is used to obtain the geographical image at the same altitude of the unmanned aerial vehicle by using the GIS information system, and demarcate the surveying and mapping block that needs to be surveyed on the geographical image. The second module is used to determine the altitude of the key position nodes on the surveying and mapping block, and construct an initial surveying and mapping 3D model based on the altitude of the key position nodes. The third module is used to analyze the laser detection data of the unmanned aerial vehicle, construct a ground 3D point cloud model, and perform several initial model adjustments on the ground 3D point cloud model. Each initial model adjustment includes scale adjustment, rotation, and translation. After each initial model adjustment, calculate the initial matching degree between the ground 3D point cloud model and the initial surveying and mapping 3D model, and screen and save the initial model adjustment strategies with the initial matching degree greater than or equal to the preset value. The fourth module is used to perform image feature comparison and analysis on the ground image captured by the unmanned aerial vehicle and the surveying and mapping block, determine the secondary matching degree between the ground 3D point cloud model and the initial surveying and mapping 3D model based on the analysis result, select the ground 3D point cloud model corresponding to the optimal initial model adjustment strategy based on the secondary matching degree, and determine the output of the 3D point cloud model surveying and mapping data based on the surveying and mapping marks on the surveying and mapping block.

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