Terrain surveying and mapping system and method based on unmanned aerial vehicle

Abstract

The invention belongs to the technical field of topographic mapping, and discloses a topographic mapping system and method based on an unmanned aerial vehicle, and the method comprises the steps: a data collection module collects map geological data; the route planning module plans an optimal driving route; the data acquisition module acquires topographic images and point cloud data; the environment parameter monitoring module monitors flight environment parameters in real time; the image data analysis module is used for preprocessing and analyzing the image data; the radar data analysis module preprocesses and analyzes the radar data; the feature extraction and matching module performs feature extraction and matching on the image and the point cloud data; the terrain model construction module constructs a terrain three-dimensional model; and the terrain analysis module performs quantitative analysis on the terrain. According to the method, key feature extraction is performed on images and point cloud data, and image splicing and point cloud registration are realized; the terrain model construction module constructs a three-dimensional terrain model according to feature extraction and matching results, and the terrain analysis module obtains accurate terrain information.

Description

Technical Field

[0001] The present application relates to the technical field of topographic surveying and mapping, and more specifically, to a topographic surveying and mapping system and method based on an unmanned aerial vehicle (UAV). Background Art

[0002] When surveying and mapping terrain, UAVs are often used. By combining a surveying instrument with a UAV and using the UAV to measure the area to be surveyed, not only can the workload of operators be reduced, the accuracy of surveying and mapping data be improved, but also during the surveying and mapping work, operators do not need to set foot in dangerous areas, ensuring the personal safety of the operators.

[0003] The prior art document with the publication number CN118759533A provides a topographic surveying and mapping method and system based on UAV lidar, which relates to the field of topographic surveying and mapping technology. The method includes obtaining the surveying and mapping state of the UAV; obtaining the regional surveying and mapping effect diagram when the UAV surveying and mapping state is consistent with the completed surveying and mapping state; analyzing the regional surveying and mapping effect diagram to determine the surveying missing points and the normal surveying points, and demarcating the surveying missing areas according to each surveying missing point; demarcating the regional center point in the surveying missing area, and processing the surveying missing area according to the regional center point and the magnification factor to determine the detection area; determining the vegetation entry point and the vegetation exit point among all the normal surveying points in the detection area, and demarcating the missing surveying path according to the vegetation entry point, the vegetation exit point and the surveying missing area, and determining the regional detection order according to each surveying missing area; controlling the UAV to move along the missing surveying path. It has the effect of reducing the influence of dense trees on the UAV topographic surveying and mapping operation.

[0004] Although the above prior art solutions can achieve relevant beneficial effects through the structures of the prior art, there are still the following defects: 1. Environmental factor interference: Environmental factors such as temperature, humidity, and atmospheric refraction will affect the performance of sensors, and thus affect the accuracy of measurement data. For example, temperature changes may cause changes in the calibration parameters of sensors, and high humidity may affect the imaging quality of optical sensors. 2. Modeling errors: In the process of terrain modeling, there are various error sources, such as registration errors and modeling algorithm errors. These errors may lead to geometric distortion, detail loss or errors in the three-dimensional model, thus affecting the quality of topographic surveying and mapping.

[0005] In view of this, we propose a topographic surveying and mapping system and method based on a UAV. Summary of the Invention

[0006] 1. Technical Problems to be Solved

[0007] The purpose of this application is to provide a terrain mapping system and method based on drones, which solves the technical problems proposed in the above background technology, realizes the efficient extraction of key features of pre-processed images and point cloud data by using machine learning and deep learning algorithms, accurately stitches images and registers point clouds through feature matching algorithms, and improves the accuracy and integrity of terrain mapping; the terrain model construction module constructs a three-dimensional terrain model based on the feature extraction and matching results, visually presenting the overall terrain and spatial structure; the terrain analysis module uses technologies such as DEM and DSM to quantitatively analyze the terrain, obtain accurate information such as elevation, slope, and aspect, and provide a decision-making basis for fields such as land use and engineering construction.

[0008] 2. Technical solution

[0009] The technical solution of this application provides a terrain mapping system based on drones, including:

[0010] Data collection module: Collect maps, geological data, and vegetation and building data of the area that needs terrain mapping; annotate the data.

[0011] Route planning module: According to the map, geological data, and monitoring requirements of the mapping area, plan the optimal flight route of the drone; according to the scope, terrain characteristics, and accuracy requirements of the mapping area, use Geographic Information System (GIS) technology to plan flight tasks. Generate detailed flight route, altitude, speed and other parameters, and the task parameters can be adjusted in real time to meet different mapping requirements.

[0012] Data acquisition module: Includes drones, high-definition cameras, and lidar sensors; real-time acquisition of terrain image and point cloud data.

[0013] Environmental parameter monitoring module: Includes auxiliary sensors such as barometers, light, thermometers, and hygrometers, which can monitor flight environmental parameters in real time and provide references for sensor calibration and data processing.

[0014] Image data analysis module: Pre-process and analyze the collected image data; consider factors such as environmental temperature, light, humidity, and wind speed and direction.

[0015] Radar data analysis module: Pre-process and analyze the collected radar data; consider factors such as environmental temperature, humidity, and wind speed and direction.

[0016] Feature extraction and matching module: Use algorithms based on machine learning and deep learning to extract and match features of pre-processed image and point cloud data.

[0017] Terrain model construction module: According to the results of feature extraction and matching, use three-dimensional reconstruction algorithms to construct a three-dimensional model of the terrain.

[0018] Terrain Analysis Module: Using technologies such as Digital Elevation Model (DEM) and Digital Surface Model (DSM), it quantitatively analyzes the terrain to obtain information such as elevation, slope, and aspect of the terrain.

[0019] Result Output Module: Outputs the results of terrain mapping in various formats, such as maps, reports, 3D models, etc., to meet the needs of different users. At the same time, it provides a data sharing interface to facilitate integration and application with other geographic information systems.

[0020] PLC Control Module: Network-connected to the result output module, terrain analysis module, terrain model construction module, data collection module, route planning module, data acquisition module, environmental parameter monitoring module, image data analysis module, radar data analysis module, and feature extraction and matching module.

[0021] Furthermore, the terrain model construction module constructs a 3D model of the terrain using a 3D reconstruction algorithm based on the results of feature extraction and matching. It includes the following steps:

[0022] 1. Data Preparation and Inspection: Collect the results output by the feature extraction and matching module, including the paired image feature points and the corresponding point cloud data matching relationships. Carefully check the integrity and accuracy of the data to ensure there are no missing or incorrect matching information. At the same time, confirm whether the coordinate systems of the data are unified. If not, perform necessary coordinate conversions.

[0023] 2. Parameter Setting: According to the specific requirements of the project, data characteristics, and available computing resources, select a suitable 3D reconstruction algorithm, such as a method based on multi-view geometry or a method based on point cloud processing. Set the corresponding parameters for the selected algorithm. For example, for the Poisson reconstruction algorithm, parameters such as the accuracy and smoothness of surface reconstruction need to be set; for the method based on multi-view geometry, initial values of camera parameters, thresholds of reprojection errors, etc. need to be set.

[0024] 3. 3D Information Extraction Based on Images: Using the paired image feature points, calculate the coordinates of the feature points in 3D space through the principle of triangulation. Information on the internal and external parameters of the camera needs to be known. Perform the above operations on multiple images to gradually construct a sparse 3D point cloud. In this process, further preprocessing of the images, such as noise removal and contrast enhancement, is required to improve the accuracy of triangulation.

[0025] 4. Point Cloud Data Processing: Preprocess the point cloud data obtained by lidar, including denoising to remove outliers caused by measurement errors or environmental interference. Statistical filtering, radius filtering, etc. can be used. Perform feature extraction and registration on the processed point cloud data to ensure that the point cloud data and the sparse point cloud generated based on images are in the same coordinate system for subsequent fusion.

[0026] 5. Data Fusion: Fuse the sparse three-dimensional point cloud generated based on images with the lidar point cloud. By matching the features of the point clouds, the point cloud data from the two sources are merged together to form a denser and more accurate three-dimensional point cloud. During the fusion process, it is necessary to solve the problems of overlap and inconsistency of the point cloud data and make some adjustments and optimizations to ensure that the fused point cloud can accurately reflect the actual situation of the terrain.

[0027] 6. Surface Reconstruction: Input the fused three-dimensional point cloud data into the selected three-dimensional reconstruction algorithm for terrain surface reconstruction. For example, use the Poisson reconstruction algorithm, which will automatically construct a triangular mesh model representing the terrain surface according to the distribution and density of the point cloud. During the reconstruction process, closely monitor the reconstruction results and adjust the algorithm parameters as needed to obtain a surface model that better conforms to the actual terrain. It may be necessary to try different parameter combinations multiple times until a satisfactory result is obtained.

[0028] 7. Model Optimization and Refinement: Optimize the reconstructed three-dimensional terrain model by adjusting the vertex positions, patch connection relationships, etc. of the model to reduce the errors and discontinuities of the model and make the model smoother and more accurate. Perform detail enhancement processing, and add some detail information such as terrain textures and geomorphic features according to the characteristics of the actual terrain to make the model more realistic. Simplify the model, reduce the number of patches of the model without losing important terrain features, reduce the complexity of the model, and improve the rendering and processing efficiency of the model.

[0029] Furthermore, the terrain analysis module uses technologies such as digital elevation model (DEM) and digital surface model (DSM) to quantitatively analyze the terrain and obtain information such as the elevation, slope, and aspect of the terrain. The steps include:

[0030] 1. Data Import: Import the three-dimensional terrain model data generated by the terrain model construction module into a terrain analysis software or platform. Ensure that the data format is compatible with the analysis software. If not, perform necessary data format conversions. Preprocess the imported data, such as removing noise and outliers in the model, to ensure the quality of the data.

[0031] 2. Digital Elevation Model (DEM) Generation: Extract the elevation information of the terrain surface points from the three-dimensional terrain model. Select a suitable DEM generation method according to the project requirements and terrain characteristics, such as the regular grid method, the irregular triangular network method (TIN), etc.

[0032] If the regular grid method is used, divide the terrain area into grids of equal size and calculate the elevation values of each grid node through an interpolation algorithm. Common interpolation methods include linear interpolation, bilinear interpolation, spline interpolation, etc.

[0033] Perform quality checks on the generated DEM to ensure that the DEM can accurately reflect the elevation changes of the terrain and has no obvious errors or unreasonable elevation values.

[0034] 3. Digital Surface Model (DSM) generation: Based on the three-dimensional terrain model, generate the digital surface model. The DSM contains the top elevation information of all objects on the ground (such as vegetation, buildings, etc.). Directly obtain the elevation value of each point from the terrain model as the value corresponding to the DSM at that position. For areas covered by vegetation or buildings, use the elevation at the top of the object. Process the generated DSM, such as removing noise and filling holes, to ensure the quality and accuracy of the DSM.

[0035] 4. Terrain elevation information extraction: Extract the elevation information of the terrain from the generated DEM. The elevation of each point on the terrain can be obtained by querying the elevation values of each grid node in the DEM data. Conduct statistical analysis on the elevation information to calculate statistical indicators such as the average elevation, minimum elevation, and maximum elevation of the terrain to understand the overall elevation distribution of the terrain.

[0036] 5. Visualization of analysis results: Organize the extracted terrain elevation, slope, aspect, and other information to form a structured data table or report for easy access and analysis by users. Use Geographic Information System (GIS) software or other visualization tools to display the terrain analysis results in a graphical manner. For example, draw contour maps, slope maps, aspect maps, etc. to visually present the characteristics and changes of the terrain. According to user needs, further process and analyze the analysis results, such as generating terrain profiles and conducting terrain classification, to provide users with more comprehensive terrain information.

[0037] The present invention provides a terrain mapping method based on an unmanned aerial vehicle, comprising the following steps:

[0038] S1. The data collection module collects maps, geological data, and vegetation and building data of the terrain mapping area; annotate the data;

[0039] S2. The route planning module plans the optimal flight route of the unmanned aerial vehicle according to the map, geological data, and monitoring requirements of the mapping area;

[0040] S3. The data acquisition module uses the unmanned aerial vehicle, high-definition camera, and lidar sensor to collect real-time images and point cloud data of the terrain. The environmental parameter monitoring module monitors the flight environmental parameters in real time.

[0041] S4. The image data analysis module preprocesses and analyzes the collected image data; consider factors such as environmental temperature, light, humidity, and wind speed and direction.

[0042] S5. The radar data analysis module preprocesses and analyzes the collected radar data, taking into account environmental temperature, humidity, wind speed, and wind direction factors.

[0043] S6. The feature extraction and matching module uses algorithms based on machine learning and deep learning to extract and match features from the preprocessed image and point cloud data.

[0044] S7. The terrain model construction module constructs a three-dimensional model of the terrain using a three-dimensional reconstruction algorithm based on the results of feature extraction and matching.

[0045] S8. The terrain analysis module uses technologies such as digital elevation model (DEM) and digital surface model (DSM) to quantitatively analyze the terrain and obtain information such as elevation, slope, and aspect of the terrain.

[0046] S9. The result output module outputs the results of terrain mapping in multiple formats.

[0047] 3. Beneficial effects

[0048] One or more of the technical solutions provided in the technical solution of the present application have at least the following technical effects or advantages:

[0049] 1. By preprocessing the collected image data through the image data analysis module, such as operations like denoising and enhancing contrast, the quality of the image can be improved. Considering the influence of environmental temperature, light, humidity, wind speed, and wind direction on the image data, and performing corresponding correction and analysis, the interference of environmental factors on the image can be eliminated, the true features of the terrain can be restored, and the accuracy and reliability of image analysis can be improved.

[0050] 2. By preprocessing the radar data through the radar data analysis module to remove noise and outliers, the quality of the radar data can be improved, enabling the radar data to more accurately reflect the terrain information. Environmental impact compensation can be carried out. Considering the influence of environmental temperature, humidity, wind speed, and wind direction on the radar data, and performing corresponding processing and analysis, the interference of environmental factors on radar measurement can be compensated, the accuracy and reliability of the radar data can be improved, and more accurate data support can be provided for terrain analysis.

[0051] 3. Through the feature extraction and matching module, efficient feature extraction can be achieved. Using algorithms based on machine learning and deep learning to extract features from the preprocessed image and point cloud data can automatically learn and extract key features in the data, improve the efficiency and accuracy of feature extraction, and be more adaptable to complex and variable terrain data compared to traditional manual feature extraction methods. Precise data matching can be achieved. Through the feature matching algorithm, image stitching and point cloud registration can be realized, enabling data collected from different perspectives and at different times to be accurately fused together to establish a unified terrain model, improving the accuracy and integrity of terrain mapping.

[0052] 4. The terrain model construction module constructs a three-dimensional model of the terrain based on the results of feature extraction and matching using a three-dimensional reconstruction algorithm, which can present the terrain in an intuitive three-dimensional form, enabling people to more clearly understand the overall view and spatial structure of the terrain. The three-dimensional terrain model provides important basic data and analysis tools for multiple fields such as geological exploration, urban planning, and civil engineering.

[0053] 5. The terrain analysis module uses technologies such as digital elevation model (DEM) and digital surface model (DSM) to quantitatively analyze the terrain, and can obtain accurate information such as elevation, slope, and aspect of the terrain. This information has important reference value for fields such as land use planning, agricultural production, and water conservancy projects. Through the quantitative analysis of the terrain, the characteristics and change laws of the terrain can be deeply understood, providing important decision-making basis for the site selection, design, and construction of various engineering projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic flow chart of a drone-based terrain mapping method disclosed in this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] The following further describes this application in detail with reference to the accompanying drawings of the specification.

[0056] Referring to Figure 1 , an embodiment of this application provides a drone-based terrain mapping system, including:

[0057] Data collection module: Collect maps, geological data, and vegetation and building data of the area where terrain mapping is required; annotate the data;

[0058] Route planning module: According to the map, geological data, and monitoring requirements of the mapping area, plan the optimal flight route of the drone; according to the scope, terrain characteristics, and accuracy requirements of the mapping area, use geographic information system (GIS) technology to plan flight tasks. Generate detailed flight route, altitude, speed and other parameters, and can adjust task parameters in real time to adapt to different mapping requirements.

[0059] Data Acquisition Module: It includes an unmanned aerial vehicle (UAV), a high-definition camera, and a lidar sensor. The UAV selected is a composite UAV with strong wind resistance and flight stability, such as a design that combines multi-rotors and fixed wings. The flight control system adopts an advanced adaptive flight control algorithm, which can sense the flight state in real time and automatically adjust the flight attitude. At the same time, it is equipped with a high-precision inertial measurement unit (IMU) and a global navigation satellite system (GNSS) module to ensure the accurate positioning and stable flight of the UAV. The high-definition camera has a high resolution and a large aperture, and can obtain clear terrain images under different lighting conditions. At the same time, optical image stabilization technology is adopted to reduce image blurring caused by the flight jitter of the UAV. The lidar sensor is installed with a high-precision lidar for obtaining three-dimensional point cloud data of the terrain. The images and point cloud data of the terrain are collected in real time.

[0060] Environmental Parameter Monitoring Module: It includes auxiliary sensors such as a barometer, a light sensor, a thermometer, and a hygrometer, which can monitor the flight environment parameters in real time and provide references for sensor calibration and data processing.

[0061] Image Data Analysis Module: It preprocesses and analyzes the collected image data; factors such as environmental temperature, light, humidity, and wind speed and direction are considered.

[0062] Radar Data Analysis Module: It preprocesses and analyzes the collected radar data; factors such as environmental temperature, humidity, and wind speed and direction are considered.

[0063] Feature Extraction and Matching Module: It uses algorithms based on machine learning and deep learning to extract and match features from the preprocessed image and point cloud data. For example, the scale-invariant feature transform (SIFT) algorithm is used to extract image feature points, and image stitching and point cloud registration are achieved through feature matching algorithms.

[0064] Terrain Model Construction Module: According to the results of feature extraction and matching, it uses three-dimensional reconstruction algorithms to construct a three-dimensional model of the terrain.

[0065] Terrain Analysis Module: It adopts technologies such as digital elevation model (DEM) and digital surface model (DSM) to quantitatively analyze the terrain and obtain information such as the elevation, slope, and aspect of the terrain.

[0066] Result Output Module: It outputs the results of terrain mapping in various formats, such as maps, reports, three-dimensional models, etc., to meet the needs of different users. At the same time, it provides a data sharing interface to facilitate integration and application with other geographic information systems.

[0067] PLC control module: Network-connected to the result output module, terrain analysis module, terrain model construction module, data collection module, route planning module, data acquisition module, environmental parameter monitoring module, image data analysis module, radar data analysis module and feature extraction and matching module.

[0068] Furthermore, the data collection module is responsible for comprehensively collecting various types of data related to the area that needs to be surveyed and mapped, and making fine annotations on them to provide a solid data foundation for subsequent surveying and mapping work, including:

[0069] Map data collection: Obtain map data of the surveyed area through various channels, including but not limited to high-precision topographic maps provided by professional surveying and mapping institutions, satellite remote sensing image maps, and data from online map service platforms. These maps cover different scales and resolutions to meet surveying and mapping tasks with different accuracy requirements. During the collection process, key information such as the source, scale, and projection method of the map is recorded in detail to ensure the accuracy and traceability of the data.

[0070] Geological data collection: Obtain geological data of the surveyed area. These data include geological structure information, rock type distribution, soil properties, etc. Through on-site investigations, analysis of geological drilling data, and integration of geophysical exploration data, deeply understand the impact of the underground geological structure on the terrain. At the same time, various parameters in the geological data are annotated in detail, such as the hardness of the rock and the water content of the soil, so as to fully consider the impact of geological factors on terrain surveying and mapping in subsequent analysis.

[0071] Vegetation and building data collection: Use high-resolution satellite images, aerial photography, and on-site investigations and other means to collect vegetation and building information in the surveyed area. For vegetation, record information such as the type, distribution range, and coverage density of the vegetation; for buildings, collect data such as the location, shape, height, and structure type of the buildings. By annotating these data, different ground objects can be accurately distinguished, providing more comprehensive information for terrain surveying and mapping. During the annotation process, a unified classification standard and coding system are adopted to facilitate data management and analysis.

[0072] Furthermore, the route planning module plans the optimal flight route of the UAV according to the map, geological data, and monitoring requirements of the surveyed area; including the following steps:

[0073] 1. Data Preparation: Obtain the map data of the survey area from the data collection module, including topographic maps at different scales, satellite image maps, and online map data, etc., to ensure that the map data covers the entire picture and detailed geographical information of the survey area. Collect geological data to understand information such as the geological structure, rock distribution, and soil characteristics of the survey area. This data is very important for evaluating terrain stability and potential risks. Define the monitoring requirements, communicate with relevant departments or customers to determine specific survey objectives, such as whether specific areas, specific geological features, or specific ground objects need to be monitored, and the requirements for survey accuracy.

[0074] 2. Data Preprocessing: Convert the format and unify the projection of the map data to ensure that all map data is in the same geographic coordinate system for subsequent analysis and processing. Organize and analyze the geological data, extract key information related to the terrain, such as areas with large terrain undulations and areas where geological disasters may occur, and mark this information on the map. According to the monitoring requirements, mark and prioritize the key monitoring areas to provide clear guidance for subsequent route planning.

[0075] 3. Preliminary Route Planning: Open professional GIS software, create a new project, and import the preprocessed maps and geological data to establish a geographical information database for the survey area. In the GIS project, manage different types of data in layers, such as the terrain layer, geological layer, monitoring area layer, etc., for subsequent operations and analysis. Determine the flight coverage area. According to the scope and shape of the survey area, draw the boundary of the flight coverage area in the GIS software. Use the path planning tool in the GIS software to preliminarily plan the flight route of the UAV in the flight coverage area. Parallel lines, grid lines, or other suitable route patterns can be adopted to ensure that the route can evenly cover the entire survey area. According to the monitoring requirements, perform special processing on the key monitoring areas, such as encrypting the route or increasing the number of flights, to meet the higher survey accuracy requirements.

[0076] 4. Determine Flight Parameters:

[0077] Determination of Flight Altitude: Based on the terrain characteristics and accuracy requirements of the survey area, determine the flight altitude of the UAV. For relatively flat terrain areas, the flight altitude can be appropriately increased to improve the survey efficiency; for areas with complex terrain or areas that require high-precision surveys, lower the flight altitude to ensure that the collected data can clearly reflect the terrain details. Refer to the geological data to avoid possible geologically unstable areas, such as faults and landslides, and appropriately increase the flight altitude near these areas to ensure the safety of the UAV. At the same time, consider the performance of the sensors carried by the UAV to ensure that the flight altitude is within the effective working range of the sensors to obtain high-quality data.

[0078] Determination of flight speed: Based on the type and performance of the UAV, combined with the scope of the mapping area and the requirements of the mission time, determine the flight speed. On the premise of ensuring the quality of data collection, try to increase the flight speed as much as possible to shorten the mapping time. Consider the terrain factors. In areas with complex terrain or areas that require high-precision collection, appropriately reduce the flight speed to ensure that the UAV can fly stably and collect accurate data. Also consider the impact of meteorological conditions on the flight speed. For example, in the case of strong winds, appropriately reduce the flight speed to ensure flight safety.

[0079] Determination of other parameters: Based on the flight performance of the UAV and the mission requirements, determine other parameters such as flight attitude, flight interval, photo-taking or scanning interval, etc. The setting of these parameters should comprehensively consider factors such as mapping accuracy, data collection efficiency, and the endurance of the UAV.

[0080] When determining the parameters, conduct multiple simulations and tests, evaluate the impact of different parameter settings on the mapping results, and select the optimal parameter combination.

[0081] 5. Route optimization and adjustment: In the GIS software, mark various obstacles within the mapping area, such as buildings, high-voltage lines, trees, etc. Check the flight route of the UAV to ensure that the route avoids these obstacles and prevent collision accidents. Consider flight restriction factors, such as no-fly zones, military control areas, airport clearance protection areas, etc., to ensure that the flight route of the UAV complies with relevant laws and regulations. Adjust the route passing through these restricted areas and re-plan the flight route. Optimize the initially planned flight route according to the distribution of obstacles and restriction factors. By adjusting the starting point, ending point, and turning points of the route, make the route more reasonable and efficient, reducing unnecessary flight distance and time. Use the analysis function of the GIS software to evaluate the impact of the optimized flight route on mapping accuracy and efficiency. Ensure that the optimized route can meet the mapping requirements without reducing the quality of data collection.

[0082] 6. Simulation test: Verify the feasibility and accuracy of the real-time adjustment function. Simulate different actual situations, such as changes in meteorological conditions, discovery of new obstacles, etc., and test whether the operator can adjust the flight parameters of the UAV in a timely manner through the real-time adjustment function to ensure the smooth progress of the mapping mission.

[0083] 7. Generate a flight mission plan: Based on the optimized flight route and determined flight parameters, generate a detailed flight mission plan report. The report should include parameters such as the coordinate information of the flight route, flight altitude, speed, flight attitude, photo-taking or scanning intervals, as well as the time schedule for task execution, precautions, etc. Save the flight mission plan report in a standard file format, such as a text file, spreadsheet, or GIS project file, for easy access and use by operators. In the GIS software, display the flight mission plan in a visual manner. Intuitively present information such as the flight route, flight altitude, and speed of the UAV through methods such as map overlay and animation demonstration, facilitating inspection and confirmation by operators. Conduct a simulated flight demonstration of the flight mission plan to check whether the flight route is reasonable and whether there are potential problems or risks. Based on the results of the simulated demonstration, make final adjustments and improvements to the flight mission plan.

[0084] 8. Real-time adjustment: During the flight, if there are changes in meteorological conditions, new obstacles are discovered, or other situations that require adjusting task parameters, the operator can dynamically adjust parameters such as the flight route, altitude, and speed of the UAV in a timely manner through the ground control station. After adjustment, recalculate the remaining time and flight distance of the flight mission to ensure that the UAV can complete the mapping task safely. At the same time, record the reasons and processes of the adjustment for subsequent analysis and summary.

[0085] Furthermore, the image data analysis module preprocesses and analyzes the collected image data; considering environmental temperature, light, humidity, and wind speed and direction factors. It includes the following steps:

[0086] 1. Data import: Import the image data collected by the UAV into a dedicated image analysis software or platform to ensure compatibility of the data format, such as common formats like JPEG, TIFF, etc. Record the basic information of the images, such as the shooting time, shooting location, camera parameters, etc.

[0087] 2. Acquisition of environmental parameters: Obtain the environmental temperature, light, humidity, and wind speed and direction data corresponding to the image acquisition moment from the environmental parameter monitoring module.

[0088] 3: Image preprocessing: Includes image denoising, filtering, geometric correction, and image enhancement;

[0089] Image denoising: Select an appropriate filtering algorithm according to factors such as environmental temperature and humidity. For example, in a high-humidity environment, the image may generate more noise, and the Gaussian filtering algorithm is preferably used. For Gaussian filtering, set appropriate Gaussian kernel size and standard deviation parameters. When the environmental interference is large, appropriately increase the Gaussian kernel size and standard deviation to enhance the denoising effect.

[0090] Filter the image: Remove the noise generated by environmental factors and camera shooting to improve the clarity and quality of the image.

[0091] Geometric correction: Use the calibration parameters of the camera and geographic information data to determine the distortion model of the image. Different environmental temperatures and lighting conditions may affect the imaging of the camera, resulting in geometric distortion of the image. By establishing the mapping relationship between the image coordinate system and the geographic coordinate system, and using methods such as polynomial transformation to perform geometric correction on the image, eliminate the image distortion caused by the change of the UAV flight attitude, terrain undulation and environmental factors. During the correction process, fine-tune the correction model in combination with environmental parameters to improve the accuracy of the correction.

[0092] Image enhancement: Select a suitable image enhancement method according to the light intensity and spectral distribution information. If the light is insufficient, histogram equalization or contrast stretching techniques can be used to enhance the contrast and brightness of the image. For histogram equalization, calculate the gray histogram of the image, perform equalization processing on it, make the gray distribution of the image more uniform, so as to enhance the visual effect of the image.

[0093] Combine factors such as wind speed and wind direction, and consider the possible blurring or jitter of the image. If there is image jitter caused by wind speed, an image sharpening algorithm can be used to highlight the details of the image and make the terrain features more obvious.

[0094] 4. Image analysis considering environmental factors: including temperature influence correction, light compensation, and humidity and wind speed and wind direction influence processing;

[0095] Temperature influence correction: Establish a relationship model between temperature and the response of the image sensor. Through experiments or referring to the technical documentation of the sensor, determine the influence law of temperature change on parameters such as image color and brightness. According to the real-time monitored temperature data, correct the color and brightness of the image. For example, in a low-temperature environment, the image may be darker, and the image can be corrected by adjusting the brightness and contrast parameters.

[0096] Light compensation: Calculate the light compensation coefficient according to the light intensity and spectral distribution data provided by the light sensor. For uneven light environments, use local light compensation methods to compensate different regions of the image separately. By adjusting the pixel values of the image, achieve light compensation, so that the image can present a consistent visual effect under different light conditions, which is convenient for subsequent feature extraction and analysis.

[0097] Humidity and wind speed / direction influence processing: For the possible image blurring problem in high humidity environments, a defogging algorithm is used to process the images. Commonly used defogging algorithms such as the dark channel prior algorithm estimate the atmospheric light value and transmittance of the image, perform defogging processing on the image, and restore the clear details of the image. Considering the influence of wind speed and direction on image stability, if the image is blurred due to the jitter of the drone, an image stabilization technique is adopted. Through feature point matching and transformation estimation, the image is corrected to eliminate the influence caused by jitter. The defogging processing in high humidity environments is carried out according to the following formula:

[0098] J(x) = [I(x) - A] / [max(τ(x), τ0)] + A;

[0099] A = max[I(x)], x ∈ Top(I d , aH);

[0100] τ(x) = 1 - w * min{[I c (x)] / A c d *e -β(H-50) / (1 + C)}, c ∈ {r, g, b};

[0101] I d (x) = min y∈Ω(x) {min[I c (y)]}, c ∈ {r, g, b}; In the formula, I(x) represents the input original image, x represents the pixel position in the image, x contains all color channel information of this pixel position, and it is the starting input image for the entire defogging processing flow. I d (x) is the dark channel image of I(x). Ω(x) is a small window centered on pixel x, which is used to calculate the dark channel image I d (x). When calculating, operations are performed on the color channel values within this small window range. I c (y) represents the value of the c color channel of image I at pixel y, where c ∈ {r, g, b}, representing the three color channels of Red, Green, and Blue respectively. A is the atmospheric light value. Top(I d , aH) represents the set of pixels selected from the dark channel image I d with the top aH% brightness. This set is used to determine the atmospheric light value A. a is an adjustment coefficient used to adjust the selection of the dark channel image I according to humidity H d ​The proportion of medium pixels. Through this coefficient, the calculation method of the atmospheric light value A can be flexibly adjusted according to the actual situation. H is the environmental humidity. τ(x) is the transmittance, which reflects the transmission ability of light when propagating in the image scene. w is an empirical value used to control the intensity of defogging. β is a humidity influence coefficient used to measure the influence degree of humidity on the transmittance. C is the image complexity factor, which is calculated through the gradient information or texture features of the image and reflects the complexity of the image. J(x) is the defogged image, which is the final image result obtained after defogging processing. τ0 is a threshold used to prevent image distortion caused by too small transmittance τ(x).

[0102] Perform image stabilization processing caused by wind speed and wind direction according to the following steps:

[0103] 1). For two images I1 and I2, respectively obtain the feature point sets P1 = {p 1 1, p 2 1..., p n 1} and P2 = {p 1 2, p 2 2..., p n 2} through an improved feature extraction algorithm (considering the influence of wind speed and wind direction on image features).

[0104] 2). When calculating the matching relationship between feature points, introduce the influence factor of wind speed v and wind direction θ on the matching distance to obtain the set of matching point pairs M = {(p i 1, p i 2)} n i＝1 . Each pair of elements (p i 1, p i 2) in the set represents the feature points that match each other in images I1 and I2, where p i 1 comes from the feature point set P1 and p i 2 comes from the feature point set P2. M is the set of matching point pairs calculated after considering the wind speed and wind direction influence factor F(v, θ).

[0105] 3). Assume that the transformation model of the image is an affine transformation, and its transformation matrix HB is solved through the following formula:

[0106] Min HB {Σ||p i 2 - p i 1|| 2 *F(v, θ)}; F(v, θ) = 1 + u1v + u2sin(θ - θ0); Through

[0107] By minimizing the above formula, the optimal transformation matrix HB can be obtained. In the formula, v represents the wind speed, which is used to describe the intensity of the wind and affects processes such as image feature matching and UAV attitude estimation. θ is the wind direction, which is used to represent the direction of the wind. F(v, θ) is the influence factor of wind speed and wind direction on the image feature matching distance, and it is a function related to wind speed and wind direction. It is used to adjust the calculation of the feature point matching relationship during the image stabilization process, reflecting the influence of wind speed and wind direction on image feature matching. u1 is the wind speed influence coefficient, which is a constant determined according to experiments or experience. It is used to adjust the weight of the influence of wind speed v on the matching distance, reflecting the relative importance of the influence of wind speed on the feature point matching distance. u2 is the wind direction influence coefficient, which is a constant determined according to experiments or experience. It is used to adjust the weight of the influence of wind direction on the matching distance. θ0 is the reference wind direction, which is a set benchmark wind direction value used to determine the relative angle of the wind direction.

[0108] 4), Use the obtained transformation matrix HB to transform the image I2 to align it with the image I1, thereby achieving image stabilization.

[0109] 5. Advanced image analysis: Use algorithms based on machine learning and deep learning to perform more in-depth analysis on the preprocessed images. For example, use a convolutional neural network (CNN) for ground object recognition and classification, identify different ground object types such as buildings, roads, and vegetation in the images, and mark their positions and ranges.

[0110] Furthermore, the radar data analysis module: Preprocess and analyze the collected radar data; consider environmental temperature, humidity, and wind speed and wind direction factors. It includes the following steps:

[0111] 1. Data reading: Read the radar data collected by the lidar into a professional radar data analysis software or platform to ensure the integrity and accuracy of the data. Understand the format and structure of the radar data, including the coordinate information and reflection intensity of the point cloud data.

[0112] 2. Environmental parameter association: Obtain the environmental temperature, humidity, and wind speed and wind direction data corresponding to the radar data collection time from the environmental parameter monitoring module.

[0113] 3. Radar data preprocessing: Include point cloud filtering, point cloud registration, and data augmentation;

[0114] Point cloud filtering: Select a suitable filtering algorithm according to factors such as environmental temperature and humidity. For example, in a high-temperature environment, the radar signal may be interfered, and a statistical filtering algorithm can be used to remove noise points and outliers. For statistical filtering, set appropriate neighborhood sizes and standard deviation thresholds. Adjust these parameters according to the degree of environmental interference to ensure the filtering effect. Perform filtering processing on the radar point cloud data to improve the quality of the point cloud data and remove noise points caused by environmental factors and measurement errors.

[0115] Point cloud registration: Considering the differences in radar measurements under different environmental conditions, during the point cloud registration process, more accurate registration algorithms are adopted, such as feature point-based registration methods or improved versions of the Iterative Closest Point (ICP) algorithm. The registration algorithm is optimized by combining environmental parameters. For example, in the case of strong wind speed, the attitude of the unmanned aerial vehicle (UAV) may change, resulting in large deviations in the point cloud data. At this time, constraint conditions for attitude adjustment can be added during the registration process to improve the registration accuracy. Through point cloud registration, the point cloud data collected at different positions and attitudes is unified into the same coordinate system, providing an accurate data basis for subsequent terrain model construction. Point cloud registration is carried out according to the following formula:

[0116] E(T) = Σ m i＝1 ||q i -(Rp i +t 移 )|| 2 ; R = R z (J1)R y (J2)R x (J3);

[0117] In the formula, E(T) is the error function, which is used to measure the error degree between the point cloud P after being transformed by the transformation matrix T and the point cloud Q. P and Q are two sets of point cloud data to be registered. p i (i = 1, 2..., m) in P represents the i-th point in the first set of point clouds, which contains the coordinate information of the point in three-dimensional space and possible other attributes (such as reflection intensity, etc.). q i (i = 1, 2..., m) in Q represents the i-th point in the second set of point clouds, containing the corresponding coordinate and attribute information. These two sets of point clouds are collected at different positions and attitudes of the UAV. The purpose of registration is to find a suitable transformation to align these two sets of point clouds in space. T is a homogeneous transformation matrix, which is used to describe the transformation relationship that transforms the point cloud P to align with the point cloud Q. R is a 3X3 rotation matrix, which is responsible for describing the rotation operation of the point cloud P in three-dimensional space. Through the rotation matrix, the direction of the point cloud can be changed to make it closer to the direction of the target point cloud Q. t 移 is a 3X1 translation vector, which determines the translation amount of the point cloud in three-dimensional space. Through the translation vector t 移 , the point cloud P can be moved to a more suitable position relative to the point cloud Q. J1 is the rotation angle around the x-axis, J2 is the rotation angle around the y-axis, and J3 is the rotation angle around the z-axis. Through these three angles, the rotation attitude of the UAV in three-dimensional space can be completely described. R z (J1) is the rotation matrix that rotates by J1 angle around the x-axis; Ry (J2) is the rotation matrix that rotates by the J2 angle around the y-axis; R x (J3) is the rotation matrix that rotates by the J3 angle around the z-axis.

[0118] Data augmentation: According to the complexity of the terrain and the requirements of measurement accuracy, data augmentation techniques such as point cloud interpolation and smoothing processing are adopted. In complex terrain areas, the density of the point cloud is increased to more accurately reflect the details of the terrain. The data augmentation parameters are adjusted in combination with environmental factors. For example, in an environment with high humidity, the radar signal may be weak, and the weight of point cloud interpolation can be appropriately increased to improve the quality of the point cloud data.

[0119] 4. Radar data analysis considering environmental factors: including temperature influence correction and humidity, wind speed, and wind direction influence processing;

[0120] Temperature influence correction: Establish a relationship model between temperature and radar ranging error. Through experiments and data analysis, determine the influence law of temperature change on radar ranging accuracy. According to the real-time monitored temperature data, correct the radar ranging data. For example, in a high-temperature environment, the radar ranging may deviate, and the ranging data is adjusted through the correction model to improve the measurement accuracy.

[0121] Humidity, wind speed, and wind direction influence processing: For the problem of radar signal attenuation that may occur in a high-humidity environment, a signal enhancement algorithm is used to process the radar data. By estimating the degree of signal attenuation, the reflection intensity of the radar data is compensated to improve the reliability of the data. Consider the influence of wind speed and wind direction on the attitude of the UAV, and then the influence on radar measurement. During the data analysis process, correct the deviation of the point cloud data caused by the change of the UAV attitude to ensure that the point cloud data can accurately reflect the actual situation of the terrain.

[0122] Furthermore, the feature extraction and matching module: Use algorithms based on machine learning and deep learning to extract and match features from the preprocessed images and point cloud data. For example, the scale-invariant feature transform (SIFT) algorithm is used to extract image feature points, and image stitching and point cloud registration are achieved through feature matching algorithms. The steps include:

[0123] 1. Image Feature Extraction: For image data, the Scale-Invariant Feature Transform (SIFT) algorithm is used to extract feature points. A Gaussian pyramid of the image is constructed, and the image is blurred at different scales to simulate the visual effects at different distances. At each scale, by calculating the gradient magnitude and direction of the image, stable key points, i.e., feature points, are found. A feature descriptor is generated for each feature point, and the descriptor contains the gradient information of the area around the feature point, with scale invariance and rotation invariance. In addition to the SIFT algorithm, other algorithms based on machine learning and deep learning can also be used, such as Convolutional Neural Network (CNN). Appropriate CNN models are constructed, such as VGG, ResNet, etc. The preprocessed image is input into the CNN model, and through operations such as convolutional layers and pooling layers, the features of the image are automatically extracted. These features have higher abstraction and representativeness and can be better used for image matching and classification.

[0124] 2. Point Cloud Feature Extraction: For lidar point cloud data, deep learning algorithms such as Point Cloud Convolutional Neural Network (PointNet) or Point Cloud Residual Network (PointNet++) are used to extract features.

[0125] PointNet directly processes unordered point cloud data and extracts global and local features of the point cloud through a Multi-Layer Perceptron (MLP).

[0126] Based on PointNet, PointNet++ extracts features of point clouds at different scales in a hierarchical manner, which can better capture the geometric structure and detailed information of the point cloud.

[0127] 3. Feature Matching: For image feature points, a matching algorithm based on nearest neighbor search, such as the KD-tree algorithm, is used to find matching point pairs among the feature points of two groups of images. The distances between the feature descriptors of the two groups of feature points are calculated, such as Euclidean distance or Hamming distance. According to the set distance threshold, the matching feature point pairs are screened out.

[0128] For point cloud data, a feature-based registration method is used to match the features of the point cloud and find the corresponding point pairs. During the matching process, the geometric and semantic features of the point cloud are considered to improve the accuracy of the matching. The Random Sample Consensus (RANSAC) algorithm is used to optimize the matching results, removing mis-matched point pairs and improving the matching accuracy.

[0129] 4. Image Stitching: According to the matching image feature point pairs, an image transformation algorithm, such as homography transformation, is used to stitch different images. By solving the homography matrix, one image is mapped into the coordinate system of another image to achieve image stitching. The stitched image is subjected to fusion processing to eliminate the stitching seam and make the stitched image more natural.

[0130] 5. Point cloud registration: Based on the matched point cloud feature points, use the Iterative Closest Point (ICP) algorithm or its improved version to register different point cloud data. Through iterative calculations, continuously adjust the position and orientation of the point cloud to minimize the error between the two groups of point clouds. During the registration process, optimize the registration result by combining environmental factors to ensure that the point cloud data can accurately reflect the actual situation of the terrain.

[0131] Furthermore, according to the results of feature extraction and matching, the terrain model construction module constructs a three-dimensional model of the terrain using a three-dimensional reconstruction algorithm. It includes the following steps:

[0132] 1. Data preparation and inspection: Collect the results output by the feature extraction and matching module, including the matched image feature point pairs and the corresponding point cloud data matching relationships. Carefully check the integrity and accuracy of the data to ensure that there is no missing or incorrect matching information. At the same time, confirm whether the coordinate systems of the data are unified. If not, perform necessary coordinate conversions.

[0133] 2. Parameter setting: According to the specific requirements of the project, data characteristics, and available computing resources, select a suitable three-dimensional reconstruction algorithm, such as a method based on multi-view geometry (such as Bundle Adjustment), a method based on point cloud processing (such as Poisson reconstruction, moving least squares, etc.). Set the corresponding parameters for the selected algorithm. For example, for the Poisson reconstruction algorithm, parameters such as the accuracy and smoothness of surface reconstruction need to be set; for the method based on multi-view geometry, initial values of camera parameters, thresholds of reprojection error, etc. need to be set.

[0134] 3. Three-dimensional information extraction based on images: Using the matched image feature point pairs, calculate the coordinates of the feature points in three-dimensional space through the principle of triangulation. The internal and external parameters of the camera need to be known. Perform the above operations on multiple images to gradually construct a sparse three-dimensional point cloud. During this process, further preprocess the images, such as removing noise, enhancing contrast, etc., to improve the accuracy of triangulation. Calculate the three-dimensional point coordinates P according to the following formula 三维 :

[0135] λ1(K1, S1) -1 m1 = [R|t]λ2(K2, S2) -1 m2.

[0136] e 重投影 = Σ n i＝1 {||π S {(K1, S1)[R i |t i P i}-m 1i || 2 +||πS {(K2, S2)[R’ i |t’

[0137] i P i}-m 2i || 2 +w 平衡 ||△S|| 2}; where m1 and m2 are respectively the feature points that match each other in images I1 and I2. These feature points are the corresponding points found in the two images through feature extraction and matching algorithms, and their position information in the images will be used for subsequent three-dimensional coordinate calculations. I1 and I2 represent two images with a specific scene association for three-dimensional information extraction. K1 and K2 are respectively the camera intrinsic parameter matrices corresponding to images I1 and I2. The camera intrinsic parameter matrix contains the inherent attribute information of the camera, such as focal length, optical center coordinates, etc., and is used to describe the projection relationship from three-dimensional space points to two-dimensional image plane points. R and t are the relative extrinsic parameter matrices. R is the rotation matrix, which is used to describe the rotation attitude of camera I2 relative to camera I1; t is the translation vector, S1, S2 are the scene-related parameter matrices. It synthesizes the influence of factors such as the object distribution in the scene and the terrain complexity on camera imaging. P is the coordinate of the three-dimensional space point to be solved. This point is the three-dimensional space position calculated through the matching feature points m1 and m2 in images I1 and I2, as well as the camera parameters and scene-related parameters. λ1, λ2 are the scale factors related to the scene depth. They are not only related to the projection relationship of the image feature points, but also affected by factors such as the object distribution in the scene and the terrain complexity. π S is the projection function combined with scene-related parameters. Different from the traditional projection function, when calculating the projection of three-dimensional points onto the two-dimensional image plane, it takes into account the influence of the scene-related parameter matrix, thus more accurately reflecting the projection relationship in the actual scene. e 重投影 is the reprojection error. It is used to measure the difference between the points obtained by projecting the calculated three-dimensional points back onto the image plane and the actually observed feature points. n represents the number of pairs of feature points participating in the calculation. [R i |t i is the extrinsic parameter matrix of camera I1 relative to a certain reference coordinate system, where R i is the rotation matrix, describing the rotation attitude of the camera, t i is the translation vector, describing the translation position of the camera. These two matrices together determine the position and orientation of the camera in space. P i is the i-th three-dimensional point used for calculation. m 1i is the i-th actually observed feature point in image I1. ||·|| 2 represents the squared norm of the vector; [R’ i |t’ iis the external parameter matrix of camera I2 relative to a certain reference coordinate system, where R’ i is the rotation matrix, describing the rotation attitude of the camera, and t’ i is the translation vector. m 2i is the i-th actually observed feature point in image I2. w 平衡 is a weight coefficient. Its function is to balance the influence of the change of the scene-related parameter matrix on the reprojection error. ||△S|| 2 represents the square norm of the change amount of the scene-related parameter matrix S between different images.

[0138] 4. Point cloud data processing: Preprocess the point cloud data obtained by the lidar, including denoising processing to remove outliers generated due to measurement errors or environmental interference. Statistical filtering, radius filtering and other methods can be used. Extract features and register the processed point cloud data to ensure that the point cloud data and the sparse point cloud generated based on the image are in the same coordinate system for subsequent fusion. The point cloud data is processed according to the following formula:

[0139] E(T J ) = Σ n j＝1 {min k D[T J (pA j ), pB k};

[0140] D[T J (pA j )] = w geo * d geo (pA j , pB k ) + w sem * d sem (pA j , pB k );

[0141] d sem (pA j , pB k ) = 1 - S(sA j , sB k ); d geo (pA j , pB k ) = ||pA j - pB k ||; w geo + w sem = 1;

[0142] In the formula, pA jrepresents the j-th point in the point cloud PA, which contains the coordinate information of the point in three-dimensional space and possible other attributes (such as reflection intensity, etc.). pB k represents the k-th point in the point cloud PB, containing three-dimensional space coordinates and other related attributes, and is the object to be compared and matched during the registration process. PA and PB represent two sets of point cloud data to be registered. d geo (pA j ,pB k ) represents the point pA in the point cloud PA j and the corresponding point pB in the point cloud PB k The geometric distance between them is calculated here using the Euclidean distance, which can intuitively reflect the position difference between the two points in three-dimensional space.

[0143] d sem (pA j ,pB k ) represents the point pA in the point cloud PA j and the corresponding point pB in the point cloud PB k The semantic distance between them. It is calculated based on the semantic feature descriptor of the point cloud. The semantic feature descriptor reflects the semantic information of the terrain represented by the point cloud (such as terrain type, feature class, etc.), and the semantic distance is calculated through a specific semantic similarity function, which measures the degree of difference between the two points at the semantic level. sA j and sB k are the semantic feature descriptors of the points pA j and pB k respectively. They are mathematical representations used to describe the semantic information of the point cloud. These descriptors can be vectors or other data structures obtained through machine learning algorithms, semantic annotation, etc., and are used for comparison in the semantic similarity function to determine the semantic similarity degree between the two points. S(sA j ,sB k ) is a specific semantic similarity function used to calculate the similarity between the semantic feature descriptors sA j and sB k of the points pA j and pB k . d sem (pA j ,pB k ) is obtained by combining according to certain weights. By comprehensively considering geometric and semantic information, the difference between the two points can be more comprehensively measured, improving the accuracy of registration. w geo and w sem are weight coefficients used to balance the proportion of geometric distance and semantic distance in the comprehensive distance metric. Among them, w geo determines the importance of the geometric distance in the comprehensive distance, w semDetermines the importance degree of semantic distance. In practical applications, the values of these two weight coefficients can be adjusted according to specific topographic surveying and mapping requirements and data characteristics to achieve the best registration effect. T J is the homogeneous transformation matrix used to describe the point cloud transformation in the improved ICP algorithm; E(T J ) is the objective function, which is used to measure the error degree between the point cloud PA after being transformed by the transformation matrix T J and the point cloud PB. n is the number of points in the point cloud PA j . T J (pA j ) represents the point obtained by transforming the point pA j through the transformation matrix T J .

[0144] 5. Data fusion: Fuse the sparse three-dimensional point cloud generated based on the image with the lidar point cloud. By matching the features of the point clouds, the point cloud data from the two sources are merged together to form a denser and more accurate three-dimensional point cloud. During the fusion process, it is necessary to solve the problems of overlap and inconsistency of the point cloud data, and some adjustments and optimizations are required to ensure that the fused point cloud can accurately reflect the actual situation of the terrain.

[0145] 6. Surface reconstruction: Input the fused three-dimensional point cloud data into the selected three-dimensional reconstruction algorithm for terrain surface reconstruction. For example, using the Poisson reconstruction algorithm, the algorithm will automatically construct a triangular mesh model representing the terrain surface according to the distribution and density of the point cloud. During the reconstruction process, closely monitor the reconstruction results and adjust the algorithm parameters as needed to obtain a surface model that better conforms to the actual terrain. It may be necessary to try different parameter combinations multiple times until a satisfactory result is obtained.

[0146] 7. Model optimization and refinement: Optimize the reconstructed three-dimensional terrain model by adjusting the vertex positions, patch connection relationships, etc. of the model to reduce the errors and discontinuities of the model and make the model smoother and more accurate. Perform detail enhancement processing, and add some detail information according to the characteristics of the actual terrain, such as terrain textures, geomorphic features, etc., to make the model more realistic. Simplify the model, and reduce the number of patches of the model and the complexity of the model without losing important terrain features to improve the rendering and processing efficiency of the model.

[0147] Furthermore, the terrain analysis module uses technologies such as digital elevation model (DEM) and digital surface model (DSM) to quantitatively analyze the terrain and obtain information such as the elevation, slope, and aspect of the terrain. The steps include:

[0148] 1. Data Import: Import the 3D terrain model data generated by the terrain model construction module into the terrain analysis software or platform. Ensure that the data format is compatible with the analysis software. If not, perform necessary data format conversions. Preprocess the imported data, such as removing noise and outliers in the model, to ensure the data quality.

[0149] 2. Digital Elevation Model (DEM) Generation: Extract the elevation information of terrain surface points from the 3D terrain model. Select a suitable DEM generation method according to project requirements and terrain characteristics, such as the regular grid method, the Triangulated Irregular Network (TIN) method, etc.

[0150] If the regular grid method is adopted, divide the terrain area into grids of equal size and calculate the elevation values of each grid node through an interpolation algorithm. Common interpolation methods include linear interpolation, bilinear interpolation, spline interpolation, etc.

[0151] Conduct quality inspection on the generated DEM to ensure that the DEM can accurately reflect the elevation changes of the terrain without obvious errors or unreasonable elevation values.

[0152] 3. Digital Surface Model (DSM) Generation: Generate a digital surface model based on the 3D terrain model. The DSM contains the top elevation information of all objects on the ground (such as vegetation, buildings, etc.). Directly obtain the elevation value of each point from the terrain model as the value at the corresponding position of the DSM. For areas covered by vegetation or buildings, use the elevation at the top of the object. Process the generated DSM, such as removing noise and filling holes, to ensure the quality and accuracy of the DSM.

[0153] 4. Terrain Elevation Information Extraction: Extract the elevation information of the terrain from the generated DEM. The elevation of each point on the terrain can be obtained by querying the elevation values of each grid node in the DEM data. Conduct statistical analysis on the elevation information and calculate statistical indicators such as the average elevation, minimum elevation, and maximum elevation of the terrain to understand the overall elevation distribution of the terrain.

[0154] Slope Calculation: Calculate the slope for each grid node in the DEM data. Determine the slope size by comparing the elevation changes between this node and its surrounding nodes. Different algorithms can be used to calculate the slope, such as the central difference method based on a 3×3 neighborhood. The calculated slope values are usually expressed in degrees or percentages. Store the calculated slope values in a new array or data file to form a slope map, visually showing the slope distribution of the terrain.

[0155] Aspect calculation: Based on DEM data, calculate the aspect of each grid node. Aspect refers to the direction indicated by the projection of the normal direction of the terrain surface at that point onto the horizontal plane. The aspect is determined by calculating the direction of the elevation gradient and is usually represented by an angle value ranging from 0° to 360°, indicating different directions (such as north, east, south, west, etc.).

[0156] Store the calculated aspect values in a new array or data file to form an aspect map, visually showing the aspect distribution of the terrain.

[0157] 5. Visualization of analysis results: Organize the extracted terrain elevation, slope, aspect and other information to form a structured data table or report for easy access and analysis by users. Use Geographic Information System (GIS) software or other visualization tools to display the terrain analysis results graphically. For example, draw contour maps, slope maps, aspect maps, etc., to visually present the characteristics and changes of the terrain. According to user needs, further process and analyze the analysis results, such as generating terrain profiles, performing terrain classification, etc., to provide users with richer terrain information.

[0158] The present invention provides a terrain mapping method based on an unmanned aerial vehicle, comprising the following steps:

[0159] S1. The data collection module collects maps, geological data and vegetation building data of the terrain mapping area to be surveyed; label the data;

[0160] S2. The route planning module plans the optimal flight route of the unmanned aerial vehicle according to the map, geological data and monitoring requirements of the survey area;

[0161] S3. The data acquisition module uses the unmanned aerial vehicle, high-definition camera and lidar sensor to collect real-time images and point cloud data of the terrain. The environmental parameter monitoring module monitors the flight environmental parameters in real time.

[0162] S4. The image data analysis module preprocesses and analyzes the collected image data; consider factors such as environmental temperature, light, humidity and wind speed and direction.

[0163] S5. The radar data analysis module preprocesses and analyzes the collected radar data; consider factors such as environmental temperature, humidity and wind speed and direction.

[0164] S6. The feature extraction and matching module uses algorithms based on machine learning and deep learning to extract and match features from the preprocessed image and point cloud data.

[0165] S7. The terrain model construction module constructs a three-dimensional model of the terrain according to the results of feature extraction and matching using three-dimensional reconstruction algorithms.

[0166] S8. The terrain analysis module uses technologies such as digital elevation model (DEM) and digital surface model (DSM) to quantitatively analyze the terrain and obtain information such as the elevation, slope, and aspect of the terrain.

[0167] S9. The result output module outputs the results of terrain mapping in multiple formats.

[0168] The working principle of a terrain mapping system based on an unmanned aerial vehicle (UAV) of the present invention is as follows: The data collection module collects maps, geological data, and vegetation and building data of the area to be mapped; annotates the data; the route planning module plans the optimal flight route of the UAV according to the map, geological data, and monitoring requirements of the mapping area; the data acquisition module uses the UAV, high-definition camera, and lidar sensor to collect real-time images and point cloud data of the terrain. The environmental parameter monitoring module monitors the flight environmental parameters in real time. The image data analysis module preprocesses and analyzes the collected image data; considering factors such as environmental temperature, light, humidity, and wind speed and direction. The radar data analysis module preprocesses and analyzes the collected radar data; considering factors such as environmental temperature, humidity, and wind speed and direction. The feature extraction and matching module uses algorithms based on machine learning and deep learning to extract and match features from the preprocessed image and point cloud data. The terrain model construction module constructs a three-dimensional model of the terrain using a three-dimensional reconstruction algorithm based on the results of feature extraction and matching. The terrain analysis module uses technologies such as digital elevation model (DEM) and digital surface model (DSM) to quantitatively analyze the terrain and obtain information such as the elevation, slope, and aspect of the terrain. The result output module outputs the results of terrain mapping in multiple formats.

[0169] The present invention preprocesses the collected image data through the image data analysis module, such as operations like denoising and enhancing contrast, which can improve the quality of the image, make the terrain features in the image clearer, and facilitate subsequent analysis and feature extraction. Considering the influence of factors such as environmental temperature, light, humidity, and wind speed and direction on the image data, and performing corresponding correction and analysis, can eliminate the interference of environmental factors on the image, restore the true features of the terrain, and improve the accuracy and reliability of image analysis.

[0170] By preprocessing the radar data through the radar data analysis module to remove noise and outliers, the quality of the radar data can be improved, and the radar data can more accurately reflect the terrain information. Considering the influence of factors such as environmental temperature, humidity, and wind speed and direction on the radar data, and performing corresponding processing and analysis, can compensate for the interference of environmental factors on radar measurement, improve the accuracy and reliability of the radar data, and provide more accurate data support for terrain analysis.

[0171] The feature extraction and matching module can efficiently extract features, and use algorithms based on machine learning and deep learning to extract features from pre-processed images and point cloud data. It can automatically learn and extract key features in the data, improve the efficiency and accuracy of feature extraction, and is more adaptable to complex and changeable terrain data than traditional manual feature extraction methods. It can accurately match data, and achieve image splicing and point cloud registration through feature matching algorithms. It can accurately fuse data collected from different perspectives and at different times to establish a unified terrain model, thereby improving the accuracy and integrity of terrain mapping.

[0172] The terrain model building module uses the 3D reconstruction algorithm to build a 3D model of the terrain based on the results of feature extraction and matching, which can present the terrain in an intuitive 3D form, allowing people to more clearly understand the overall picture and spatial structure of the terrain. The 3D terrain model provides important basic data and analysis tools for geological exploration, urban planning, civil engineering and other fields. By analyzing the 3D model, terrain analysis, line of sight analysis, earthwork calculation, etc. can be performed to provide a scientific basis for relevant decision-making.

[0173] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A drone-based topographic surveying method, characterized in that It includes the following steps: S1. The data collection module collects maps, geological data, and vegetation and building data of the area to be surveyed for terrain mapping; Annotate the data; S2. The route planning module plans the optimal flight route of the UAV according to the map, geological data, and monitoring requirements of the survey area; S3. The data acquisition module uses the UAV, high-definition camera, and lidar sensor to collect real-time images and point cloud data of the terrain; The environmental parameter monitoring module monitors the flight environmental parameters in real time; S4. The image data analysis module preprocesses and analyzes the collected image data; S5. The radar data analysis module preprocesses and analyzes the collected radar data; S6. The feature extraction and matching module uses algorithms based on machine learning and deep learning to extract and match features from the preprocessed images and point cloud data; S7. The terrain model construction module constructs a three-dimensional model of the terrain using three-dimensional reconstruction algorithms based on the results of feature extraction and matching; S8. The terrain analysis module uses digital elevation model (DEM) and digital surface model (DSM) technologies to quantitatively analyze the terrain and obtain elevation, slope, and aspect information of the terrain; S9. The result output module outputs the results of terrain mapping in multiple formats.

2. The method for topographic survey based on unmanned aerial vehicle according to claim 1, wherein: Step S4 includes the following steps: S41. Data import: Import the image data collected by the UAV into the image analysis software to ensure data format compatibility; S42. Environmental parameter acquisition: Obtain environmental temperature, light, humidity, and wind speed and direction data corresponding to the image acquisition time from the environmental parameter monitoring module; S43: Image preprocessing: Includes image denoising, filtering, geometric correction, and image enhancement; S44. Image analysis considering environmental factors: Includes temperature influence correction, light compensation, and humidity and wind speed and direction influence processing; S441. Temperature influence correction: Establish a relationship model between temperature and the response of the image sensor, clarify the influence law of temperature on image color and brightness parameters based on experiments, and correct the image color and brightness according to real-time temperature data; S442. Light compensation: Calculate the compensation coefficient using the light intensity and spectral distribution data provided by the light sensor, adopt a local compensation method for uneven light environments, and achieve compensation by adjusting image pixel values to make the image maintain a consistent visual effect under different light conditions; S443. Humidity and wind speed and direction influence processing: When high humidity causes image blurring, use the dark channel prior algorithm to restore clear details of the image by estimating the atmospheric light value and transmittance; Considering the influence of wind speed and direction on image stability, if the image is blurred due to UAV jitter, use image stabilization technology based on feature point matching and transformation estimation to correct the image and eliminate the influence of jitter; S444. Perform image stabilization processing caused by wind speed and direction; S45. Advanced image analysis: Use algorithms based on machine learning and deep learning to conduct more in-depth analysis of the preprocessed images.

3. The method for topographic mapping based on an unmanned aerial vehicle according to claim 2, wherein: In step S443, the defogging process in a high-humidity environment is carried out according to the following formula: J(x) = [I(x) - A] / [max(τ(x), τ0)] + A; A = max[I(x)], x ∈ Top(I d , aH); τ(x) = 1 - w * min{[I c (x)] / A c d * e -β(H-50) / (1 + C)}, c ∈ {r, g, b};​ I d (x) = min y∈Ω(x) {min[I c (y)]}, c ∈ {r, g, b}; where, I(x) represents the input original image, and x represents the pixel position in the image; I d (x) is the dark channel image of I(x); Ω(x) is a small window centered on pixel x; I c (y) represents the value of the c color channel of image I at pixel y, where c ∈ {r, g, b}, representing the red, green, and blue color channels respectively; A is the atmospheric light value; Top(I d , aH) represents the set of pixels selected from the dark channel image I d with the top aH% in brightness; a is an adjustment coefficient used to adjust the proportion of pixels selected from the dark channel image I d according to the humidity H; H is the environmental humidity; τ(x) is the transmittance; w is an empirical value used to control the intensity of defogging; β is a humidity influence coefficient; C is the image complexity factor; J(x) is the defogged image; τ0 is a threshold used to prevent the image from being distorted due to the transmittance τ(x) being too small.

4. The method for topographic mapping based on an unmanned aerial vehicle according to claim 3, characterized in that: Step S444 includes the following steps: S4441), for two images I1 and I2, respectively obtain the feature point sets P1 = {p 1 1, p 2 1..., p n 1} and P2 = {p 1 2, p 2 2..., p n 2} through the improved feature extraction algorithm; S4442) When calculating the matching relationship between feature points, introduce the influence factors of wind speed v and wind direction θ on the matching distance to obtain the set of matching point pairs M = {(p i 1, p i 2)} n i＝1 ; Each pair of elements (p i 1, p i 2) in the set represents the feature points that match each other in images I1 and I2, where p i 1 comes from the set of feature points P1, and p i 2 comes from the set of feature points P2; M is the set of matching point pairs calculated after considering the wind speed and wind direction influence factor F(v, θ); S4443), the transformation model of the image is an affine transformation, and its transformation matrix HB is solved by the following formula: Min HB {Σ||p i 2 - p i 1|| 2 *F(v, θ)}; F(v, θ) = 1 + u1v + u2sin(θ - θ0); By minimizing the above formula, the optimal transformation matrix HB is obtained; where, v represents the wind speed; θ is the wind direction; F(v, θ) is the influence factor of wind speed and wind direction on the image feature matching distance; u1 is the wind speed influence coefficient; u2 is the wind direction influence coefficient; θ0 is the reference wind direction, which is a set reference wind direction value; S4444) Transform the image I2 using the obtained transformation matrix HB to align it with the image I1, thereby achieving image stabilization.

5. The method for topographic surveying based on an unmanned aerial vehicle according to claim 1, wherein: Step S5 includes the following steps: S51. Data reading: Read the lidar data collected by the lidar into a professional lidar data analysis software to ensure the integrity and accuracy of the data; S52. Environmental parameter association: Obtain the environmental temperature, humidity, and wind speed and direction data corresponding to the lidar data collection time from the environmental parameter monitoring module; S53. Lidar data preprocessing: including point cloud filtering, point cloud registration, and data augmentation; Point cloud filtering: Select an appropriate filtering algorithm according to environmental temperature and humidity factors to filter the lidar point cloud data and improve the quality of the point cloud data; Point cloud registration: Select a registration method based on feature points and optimize the registration algorithm in combination with environmental parameters; Through point cloud registration, unify the point cloud data collected at different positions and postures into the same coordinate system; Data augmentation: Adjust the data augmentation parameters in combination with environmental factors according to the complexity of the terrain and the measurement accuracy requirements; S54. Lidar data analysis considering environmental factors: including temperature influence correction and humidity and wind speed and direction influence processing; Temperature influence correction: Establish a relationship model between temperature and lidar ranging error through experiments and data analysis, clarify the influence law of temperature on lidar ranging accuracy, and correct the lidar ranging data based on real-time temperature data; Humidity and wind speed and direction influence processing: When the radar signal attenuates under high humidity, use a signal enhancement algorithm to compensate the radar data reflection intensity by estimating the attenuation degree to improve reliability; Considering that the wind speed and direction affect the attitude of the UAV and thus affect the radar measurement, correct the deviation of the point cloud data caused by the change of the UAV attitude in data analysis to ensure that the point cloud data accurately reflects the actual terrain.

6. The method for topographic surveying based on an unmanned aerial vehicle according to claim 5, wherein: In step S53, point cloud registration is performed according to the following formula: E(T) = Σ m i＝1 ||q i -(Rp i +t 移 )|| 2 ; R = R z (J1)R y (J2)R x (J3); In the formula, E(T) is the error function, which is used to measure the error degree between the point cloud P after being transformed by the transformation matrix T and the point cloud Q; P and Q are two sets of point cloud data to be registered; p in P i (i = 1, 2..., m) represents the i-th point in the first set of point clouds; q in Q i (i = 1, 2..., m) represents the i-th point in the second set of point clouds; T is a homogeneous transformation matrix; R is a 3X3 rotation matrix; t 移 is a 3X1 translation vector; J1 is the rotation angle around the x-axis, J2 is the rotation angle around the y-axis, and J3 is the rotation angle around the z-axis; R z (J1) is the rotation matrix that rotates by an angle of J1 around the x-axis; R y (J2) is the rotation matrix that rotates by an angle of J2 around the y-axis; R x (J3) is the rotation matrix that rotates by an angle of J3 around the z-axis.

7. The method for topographic surveying based on an unmanned aerial vehicle according to claim 1, wherein: Step S6 includes the following steps: S61. Image feature extraction: For image data, use the Scale-Invariant Feature Transform (SIFT) algorithm to extract feature points; S62. Point cloud feature extraction: For lidar point cloud data, use deep learning algorithms such as Point Cloud Convolutional Neural Network (PointNe) or Point Cloud Residual Network (PointNet++) to extract features; S63. Feature matching: For image feature points, use a matching algorithm based on nearest neighbor search to find matching point pairs among the feature points of two groups of images; Calculate the distance between the descriptors of two groups of feature points, and filter out the matching feature point pairs according to the set distance threshold; For point cloud data, use a feature-based registration method to match the features of the point cloud and find the corresponding point pairs; S64. Image stitching: According to the matching image feature point pairs, use an image transformation algorithm to stitch different images; Perform fusion processing on the stitched images; S65. Point cloud registration: According to the matching point cloud feature point pairs, use the Iterative Closest Point (ICP) algorithm to register different point cloud data.

8. The method for topographic survey based on an unmanned aerial vehicle according to claim 1, characterized in that: Step S7 includes the following steps: S71. Data preparation and inspection: Collect the results output by the feature extraction and matching module, including the matching image feature point pairs and the corresponding point cloud data matching relationship; S72. Parameter setting: According to the specific requirements of the project, data characteristics, and available computing resources, select the Poisson reconstruction method based on point cloud processing and set the corresponding parameters; S73. 3D information extraction based on images: Using the matched image feature points, calculate the coordinates of the feature points in the 3D space through the principle of triangulation; S74. Point cloud data processing: Preprocess the point cloud data obtained by the lidar, perform feature extraction and registration on the processed point cloud data to ensure that the point cloud data and the sparse point cloud generated based on images are in the same coordinate system; S75. Data fusion: Fuse the sparse 3D point cloud generated based on images with the lidar point cloud; S76. Surface reconstruction: Input the fused 3D point cloud data into the selected 3D reconstruction algorithm for terrain surface reconstruction; S77. Model optimization and refinement: Optimize the reconstructed terrain 3D model, reduce the errors and discontinuities of the model by adjusting the vertex positions and patch connection relationships of the model to make the model smoother and more accurate.

9. The method for topographic surveying based on an unmanned aerial vehicle according to claim 1, characterized in that: In step S73, calculate the three-dimensional point coordinates P according to the following formula 三维 : λ1(K1, S1) -1 m1 = [R|t]λ2(K2, S2) -1 m2; e 重投影 = Σ n i＝1 {||π S {(K1, S1)[R i |t i P i}-m 1i || 2 + ||π S {(K2, S2)[R’ i |t’ i P i}-m 2i || 2 + w 平衡 ||△S|| 2}; where m1 and m2 are respectively the feature points that match each other in images I1 and I2; I1 and I2 represent two images with a specific scene association for three-dimensional information extraction; K1 and K2 are respectively the camera internal parameter matrices corresponding to images I1 and I2; R and t are the relative external parameter matrices; R is the rotation matrix; t is the translation vector, S1 and S2 are scene-related parameter matrices; P is the coordinate of the 3D space point to be solved; λ1 and λ2 are scale factors related to the scene depth; π S is the projection function combined with scene-related parameters; e 重投影 is the reprojection error; [R i |t i is the external parameter matrix of camera I1 relative to a certain reference coordinate system, where R i is the rotation matrix, describing the rotation attitude of the camera, and t i is the translation vector, describing the translation position of the camera; P i is the i-th 3D point used for calculation; m 1i is the i-th actually observed feature point in image I1; ||·|| 2 represents the squared norm of the vector; [R’ i |t’ i is the external parameter matrix of camera I2 relative to a certain reference coordinate system, where R’ i is the rotation matrix, describing the rotation attitude of the camera, and t’ i is the translation vector; m 2i is the i-th actually observed feature point in image I2; w 平衡 is a weight coefficient, which serves to balance the influence of the change in the scene-related parameter matrix on the reprojection error; ||△S|| 2 represents the squared norm of the change amount of the scene-related parameter matrix S between different images; In step S74, the point cloud data is processed according to the following formula: D[T J (pA j )] = w geo * d geo (pA j , pB k ) + w sem * d sem (pA j , pB k ); d sem (pA j , pB k ) = 1 - S(sA j , sB k );d geo (pA j , pB k ) = ||pA j - pB k ||;w geo + w sem = 1; where, pA j represents the j-th point in the point cloud PA; pB k represents the k-th point in the point cloud PB; PA and PB represent two sets of point cloud data to be registered; d geo (pA j , pB k ) represents the geometric distance between the point pA j in the point cloud PA and the corresponding point pB k in the point cloud PB; d sem (pA j , pB k ) represents the semantic distance between the point pA j in the point cloud PA and the corresponding point pB k in the point cloud PB; sA j and sB k are the semantic feature descriptors of the points pA j and pB k respectively; S(sA j , sB k ) is a specific semantic similarity function used to calculate the similarity between the semantic feature descriptors sA j and sB k of the points pA j and pB k ; d sem (pA j , pB k ) is obtained by combining according to certain weights; w geo and w sem are weight coefficients used to balance the proportion of the geometric distance and the semantic distance in the comprehensive distance metric; among them, w geo determines the importance degree of the geometric distance in the comprehensive distance, and w sem determines the importance degree of the semantic distance; T J is the homogeneous transformation matrix used to describe the point cloud transformation in the improved ICP algorithm; E(T J ) is the objective function used to measure the error degree between the point cloud PA after being transformed by the transformation matrix T J and the point cloud PB; n is the number of points in the point cloud PA j ; T J (pA j ) represents the point obtained by transforming the point pA j through the transformation matrix T J .

10. An unmanned aerial vehicle-based topographic mapping system, comprising: PLC control module, result output module, terrain analysis module, terrain model construction module, data collection module, route planning module, data acquisition module, environmental parameter monitoring module, image data analysis module, radar data analysis module, and feature extraction and matching module; It is characterized in that: Data collection module: Collect the maps, geological data, and vegetation and building data of the area requiring topographic surveying and mapping; Label the data; Route planning module: According to the maps, geological data, and monitoring requirements of the surveying and mapping area, plan the optimal flight route of the unmanned aerial vehicle; Data acquisition module: Includes an unmanned aerial vehicle, a high-definition camera, and a lidar sensor; Real-time collect the images and point cloud data of the terrain; Environmental parameter monitoring module: Includes auxiliary sensors such as a barometer, light, thermometer, and hygrometer to monitor the flight environmental parameters in real-time; Image data analysis module: Preprocess and analyze the collected image data; Consider factors such as environmental temperature, light, humidity, and wind speed and direction; Radar data analysis module: Preprocess and analyze the collected radar data; Consider factors such as environmental temperature, humidity, and wind speed and direction; Feature extraction and matching module: Use algorithms based on machine learning and deep learning to perform feature extraction and matching on the preprocessed image and point cloud data; Terrain model construction module: According to the results of feature extraction and matching, use a 3D reconstruction algorithm to construct a 3D model of the terrain; Terrain analysis module: Adopt digital elevation model DEM and digital surface model DSM technologies to quantitatively analyze the terrain and obtain elevation, slope, and aspect information of the terrain; Result output module: Output the results of topographic surveying and mapping in multiple formats; PLC control module: Network-connected to the result output module, terrain analysis module, terrain model construction module, data collection module, route planning module, data acquisition module, environmental parameter monitoring module, image data analysis module, radar data analysis module, and feature extraction and matching module.

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