Low-altitude economy-oriented surveying and mapping product generation method and system

Abstract

The invention discloses a surveying and mapping product generation method and system for low-altitude economy, and the method comprises the steps: synchronously collecting multi-source data through low-altitude economy equipment, guaranteeing the time-space consistency of the data, enabling the data of images, laser, IMU and the like to work and cooperate with each other, and achieving the effective fusion; besides, IMU data, moving station data and base station data are integrated through integrated navigation solution, the IMU compensates for positioning information when GNSS signals are lost, the GNSS corrects IMU accumulative errors, and the advantages complement each other to improve the equipment positioning and attitude measurement precision; in addition, combined navigation solution, point cloud generation and the like are firstly used as a foundation for image correction and model generation, intermediate links are reduced, and the efficiency is improved; meanwhile, accurate generation of an image parameter list and three-dimensional point cloud is guaranteed through an accurate position and posture file, a foundation is laid for subsequent mapping, aerial triangulation adjustment calculation is carried out according to the accurate image parameter list and geometric deformation is eliminated when a digital orthoimage map is generated, and finally high-quality generation of a surveying and mapping product is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of surveying and mapping product generation technology, and in particular to a method and system for generating surveying and mapping products for the low-altitude economy. Background Technology

[0002] With the rapid development of the low-altitude economy, the technology of using low-altitude aircraft for surveying and mapping has gradually become an important means of acquiring geographic information. Low-altitude economic equipment, such as drones, tethered balloons, and light aircraft, has advantages such as flexibility, efficiency, and low cost, and can quickly acquire high-resolution geographic data in complex terrain and environments. However, how to make full use of the multi-source data acquired by these devices to generate high-precision surveying and mapping products remains an important technical challenge facing the surveying and mapping field.

[0003] Low-altitude economic equipment can carry a variety of sensors, such as optical cameras, IMUs (inertial measurement units), and lidar, to achieve simultaneous acquisition of multi-source data. However, the technology for fusion and processing of multi-source data is still immature. How to effectively integrate image data, IMU data, GNSS (Global Navigation Satellite System) data, and laser data to generate high-precision position and attitude information and three-dimensional point cloud models is a current research hotspot and challenge. Summary of the Invention

[0004] In view of this, the present invention proposes a method and system for generating surveying and mapping products for the low-altitude economy, which can realize the effective fusion and processing of multi-source data, high-precision integrated navigation calculation, and efficient and accurate generation of surveying and mapping products.

[0005] The technical solution of this invention is implemented as follows:

[0006] A method for generating mapping products for the low-altitude economy, comprising:

[0007] Image data, IMU data, rover data, base station data, and laser data are acquired using low-altitude economic equipment;

[0008] Based on IMU data, rover data, and base station data, a combined navigation solution is performed to generate a position and attitude file;

[0009] Based on laser data and position and attitude files, generate an image parameter list and a 3D point cloud;

[0010] Aerial triangulation adjustment is performed on the image data based on the image parameter list to obtain the adjusted image data;

[0011] The 3D point cloud is classified and processed, and a digital elevation model is generated based on the classified 3D point cloud.

[0012] Based on the adjusted image data and digital elevation model, digital orthophoto maps and digital line maps are generated, thus producing surveying and mapping products.

[0013] As a further optional solution to the aforementioned method for generating mapping products for the low-altitude economy, the acquisition of image data, IMU data, rover data, base station data, and laser data based on low-altitude economy equipment specifically includes:

[0014] Low-altitude economic equipment collects image data, IMU data, rover data, base station data, and laser data. The image data is used for image processing and 3D reconstruction. The IMU data is used to provide attitude and motion information of the low-altitude economic equipment. The rover data and base station data are used in conjunction for the positioning of the low-altitude economic equipment. The laser data is used to generate 3D point clouds to obtain spatial information of terrain and ground features.

[0015] As a further optional solution to the aforementioned method for generating mapping products for the low-altitude economy, the step of performing combined navigation calculations based on IMU data, rover data, and base station data to generate a position and attitude file specifically includes:

[0016] Based on rover data and base station data, the three-dimensional position and velocity information of low-altitude economic equipment in a preset coordinate system are calculated in real time using carrier phase differential technology to obtain differential GNSS positioning results.

[0017] Using IMU data, the three-dimensional position, velocity, and attitude angles of the low-altitude economic equipment are calculated through integration to obtain a temporary solution for inertial navigation;

[0018] The differential GNSS positioning results and the inertial navigation temporary solution are input into a tightly coupled Kalman filter to obtain a tightly coupled solution containing timestamps, three-dimensional coordinates, velocity, roll angle, pitch angle, and heading angle.

[0019] The tightly coupled solution results are recorded as position and attitude files in a preset format.

[0020] As a further optional solution to the aforementioned method for generating mapping products for the low-altitude economy, the generation of image parameter lists and 3D point clouds based on laser data and position and attitude files specifically includes:

[0021] The laser data is denoised, filtered, and intensity corrected to generate structured laser point cloud data.

[0022] Based on the position and attitude file, extract the device's three-dimensional coordinates, attitude angles, and timestamps corresponding to each laser scanning moment;

[0023] Based on the conversion parameters between the device coordinate system and the preset geographic coordinate system, the structured laser point cloud data is converted from the scanning coordinate system to the geographic coordinate system. At the same time, the orientation of the point cloud is corrected by the attitude angle in the position and attitude file, and a georegistered 3D point cloud is generated.

[0024] Based on the device's 3D coordinates, attitude angles, and camera parameters in the position and attitude file, the exterior orientation elements at each image exposure time are calculated to generate an image parameter list containing the image's spatial position, attitude, and coverage area.

[0025] As a further optional solution to the aforementioned method for generating mapping products for the low-altitude economy, the step of performing aerial triangulation adjustment on the image data based on the image parameter list to obtain adjusted image data specifically includes:

[0026] Spatiotemporal correlation is established between the exterior orientation elements in the image parameter list and the corresponding image data to create a mapping relationship between the image and the pose data.

[0027] A free network adjustment model is constructed, and the relative orientation elements between images in the image data are solved by bundle adjustment to obtain the free network adjustment results;

[0028] Using the coordinates and elevation information of ground control points in the preset geographic coordinate system as absolute constraints, and combining them with the free network adjustment results, the exterior orientation elements of all images are obtained.

[0029] The exterior orientation elements of all images are adjusted to obtain the optimized exterior orientation elements.

[0030] Based on the optimized exterior orientation elements and the mapping relationship between the image and pose data, the original image is geometrically corrected to obtain the adjusted image data.

[0031] As a further optional solution to the method for generating surveying and mapping products for the low-altitude economy, the step of classifying the 3D point cloud and generating a digital elevation model based on the classified 3D point cloud specifically includes:

[0032] Preprocess the 3D point cloud to generate a structured point cloud dataset;

[0033] Extract geometric and radial features from structured point cloud datasets;

[0034] The ground classification results are obtained by classifying the geometric and radial features of the structured point cloud dataset.

[0035] Morphological opening operations are performed based on the ground classification results to extract ground points;

[0036] The extracted ground points are interpolated using a regular grid to generate a digital elevation model map.

[0037] As a further optional solution to the aforementioned method for generating mapping products for the low-altitude economy, the step of generating digital orthophoto maps and digital line maps based on adjusted image data and digital elevation models specifically includes:

[0038] Based on the terrain elevation information provided by the digital elevation model, the adjusted image data is orthorectified pixel by pixel to obtain orthorectified image units.

[0039] Perform tone equalization and seam processing on the orthorectified image units to generate a digital orthophoto map.

[0040] Based on digital orthophoto maps, a semi-automatic vectorization method is used to extract ground features;

[0041] The extracted geographic features are symbolically rendered and layered to generate digital line maps.

[0042] A mapping product generation system for the low-altitude economy includes:

[0043] The data acquisition module is used to acquire image data, IMU data, rover data, base station data, and laser data based on low-altitude economic equipment;

[0044] The integrated navigation calculation module is used to perform integrated navigation calculations based on IMU data, rover data, and base station data, and generate a position and attitude file.

[0045] The point cloud and parameter generation module is used to generate image parameter lists and 3D point clouds based on laser data and position and attitude files;

[0046] The image correction module is used to perform aerial triangulation adjustment on the image data based on the image parameter list to obtain the adjusted image data.

[0047] The point cloud classification and model generation module is used to classify 3D point clouds and generate digital elevation models based on the classified 3D point clouds.

[0048] The surveying product generation module is used to generate digital orthophoto maps and digital line maps based on adjusted image data and digital elevation models, thereby generating surveying products.

[0049] A computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described methods for generating mapping products for the low-altitude economy.

[0050] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the above-described methods for generating mapping products for the low-altitude economy.

[0051] The beneficial effects of this invention are as follows: By synchronously collecting multi-source data using low-altitude economic equipment, the consistency of data in time and space is ensured. Different types of data cooperate with each other in their respective processing stages. Image data is used to generate intuitive geographic images, laser data helps to construct detailed 3D point clouds, and IMU and other data provide crucial positioning and attitude information for data fusion, thereby achieving effective fusion of multi-source data. Furthermore, the integrated navigation solution comprehensively utilizes IMU data, rover data, and base station data. By combining these three, IMU data can compensate for positioning information when GNSS signals are missing, while GNSS data can correct the accumulated errors of the IMU. Through complementary advantages, high-precision integrated navigation solution is achieved. This approach effectively improves the positioning and attitude measurement accuracy of equipment in various environments. Furthermore, by first providing a foundation for image correction and model generation through integrated navigation calculations and point cloud generation, and then generating mapping products based on the adjusted image data and digital elevation models, unnecessary intermediate steps are reduced, improving processing efficiency. Simultaneously, precise position and attitude files ensure more accurate image parameter lists and 3D point cloud generation, thus laying a solid foundation for digital elevation model mapping and mapping product generation. When generating digital orthophoto maps, aerial triangulation adjustment is performed based on precise image parameter lists, effectively eliminating geometric distortions in the images, improving the accuracy of image data, and ultimately ensuring the high-quality generation of mapping products. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a flowchart of a method for generating surveying and mapping products for the low-altitude economy according to the present invention;

[0054] Figure 2 This is a schematic diagram of the composition of a surveying and mapping product generation system for the low-altitude economy according to the present invention.

[0055] Figure 3 This is a schematic diagram of the composition of a computing device according to the present invention. Detailed Implementation

[0056] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] refer to Figures 1 to 3 A method for generating mapping products for the low-altitude economy, comprising:

[0058] Based on low-altitude economic equipment, imagery data, IMU (Inertial Measurement Unit) data, rover data, base station data, and laser data are acquired, specifically including:

[0059] The system collects image data, IMU (Inertial Measurement Unit) data, rover data, base station data, and laser data using low-altitude economic equipment. The image data is used for image processing and 3D reconstruction. The IMU data provides attitude and motion information for the low-altitude economic equipment. The rover data and base station data work together to locate the low-altitude economic equipment. The laser data is used to generate 3D point clouds to obtain spatial information about terrain and ground features.

[0060] Specifically, by using low-altitude economic equipment to acquire image data, IMU data, rover data, base station data, and laser data at the same time, the synchronous acquisition of multi-source data is achieved. This synchronicity ensures the consistency of data in time and space and avoids matching errors caused by asynchronous data acquisition time.

[0061] MU data provides precise attitude information for low-altitude economic equipment. Combined with rover and base station data for collaborative positioning, it enables high-precision location and attitude parameters of the equipment in three-dimensional space. This helps to accurately match image data with geospatial coordinates, improving the accuracy of image processing. Different types of data can corroborate and complement each other during processing. For example, 3D point clouds generated from laser data can be fused with image data, using the color information of the image to colorize the point cloud, enhancing its readability. Simultaneously, the elevation information of the point cloud can assist in the geometric correction of the image, improving its spatial accuracy. This data fusion method fully leverages the advantages of each data source, enhancing the effectiveness of data processing.

[0062] Image data is used for image processing and 3D reconstruction. Combined with accurate pose and positioning information, it can generate high-precision, highly realistic 3D models and orthophoto maps. 3D point clouds generated from laser data can accurately acquire spatial information of terrain and features, including terrain undulations, building heights, tree distribution, etc. Through the analysis and processing of point cloud data, rich geographic information can be extracted.

[0063] Based on IMU (Inertial Measurement Unit) data, rover data, and base station data, integrated navigation calculations are performed to generate a position and attitude file, specifically including:

[0064] Based on rover and base station data, the device's three-dimensional position (longitude, latitude, and elevation) and velocity information at the centimeter level in a preset coordinate system (such as CGCS2000 or WGS84) are calculated in real time using carrier phase differential technology (RTK).

[0065] Using IMU (Inertial Measurement Unit) data, the three-dimensional position, velocity, and attitude angles (roll angle, pitch angle, and heading angle) of the low-altitude economic equipment are calculated through integration operations to obtain a temporary solution for inertial navigation;

[0066] The differential GNSS positioning results and the inertial navigation temporary solution are input into a tightly coupled Kalman filter. The GNSS observations are used for measurement updates, and the IMU predictions are used for state updates. The accumulated IMU error is corrected in real time to obtain a tightly coupled solution that includes timestamps, three-dimensional coordinates, velocity, roll angle, pitch angle, and heading angle.

[0067] The tightly coupled solution results are recorded as position and attitude files in a preset format.

[0068] Specifically, based on rover and base station data, carrier phase differential technology (RTK) is used to calculate the centimeter-level three-dimensional position (longitude, latitude, and elevation) and velocity information of the equipment in a preset coordinate system in real time. RTK technology can significantly improve positioning accuracy by eliminating common errors, enabling the equipment's position information to reach the centimeter level, meeting the needs of application scenarios with extremely high requirements for position information, such as high-precision surveying and mapping and precision agriculture. Using IMU data, the three-dimensional position, velocity, and attitude angles (roll angle, pitch angle, and heading angle) of the low-altitude economic equipment are calculated through integral calculations to obtain a temporary solution for inertial navigation. The IMU can quickly sense the attitude changes of the equipment, and the integral calculations can convert them into specific attitude angle information, providing the equipment with accurate attitude references and helping to accurately describe the spatial state of the equipment.

[0069] The differential GNSS positioning results and the inertial navigation temporary solution are input into a tightly coupled Kalman filter. GNSS observations are used for measurement updates, while IMU predictions are used for state updates. Accumulated IMU errors are corrected in real time. This tightly coupled approach makes fuller use of information from both data sources, leveraging the advantages of GNSS's long-term stability and IMU's high short-term accuracy and fast update rate. The Kalman filter algorithm performs optimal estimation of both data, effectively overcoming the problems of GNSS signal susceptibility to interference and IMU error accumulation over time, thus improving the accuracy and reliability of position and attitude information. This achieves deep fusion of GNSS and IMU data, not only improving positioning and attitude determination accuracy but also enhancing adaptability in complex environments. For example, in areas where GNSS signals are obstructed or interfered with, the IMU can continue to provide short-term, high-precision attitude and position information; while when GNSS signals are good, it can correct the IMU's accumulated errors.

[0070] By employing RTK technology for real-time computation and real-time state updates via tightly coupled Kalman filters, the system can quickly output the device's position and attitude information. This is of great significance for applications requiring real-time device status acquisition, such as UAV flight control and autonomous driving, enabling timely feedback on the device's motion status. Real-time correction of IMU accumulated errors via tightly coupled Kalman filtering prevents the infinite accumulation of errors, ensuring stability during long-term operation. Even under complex and changing environmental conditions, it can continuously provide accurate position and attitude information, reducing the risk of malfunctions or inaccurate data due to excessive errors.

[0071] Based on laser data and position / pose files, an image parameter list and a 3D point cloud are generated, including:

[0072] The laser data is denoised, filtered, and intensity corrected to generate structured laser point cloud data.

[0073] Based on the position and attitude file, extract the device's three-dimensional coordinates (longitude, latitude, elevation), attitude angles (roll angle, pitch angle, yaw angle), and timestamp corresponding to each laser scanning moment;

[0074] Based on the conversion parameters between the device coordinate system and the preset geographic coordinate system (such as CGCS2000), the structured laser point cloud data is converted from the scanning coordinate system to the geographic coordinate system. At the same time, the point cloud direction is corrected by the attitude angle in the position and attitude file, and a georegistered 3D point cloud is generated.

[0075] Based on the device's 3D coordinates, attitude angles, and camera parameters (such as focal length and principal point coordinates) in the position and attitude file, calculate the exterior orientation elements (longitude, latitude, elevation coordinates, roll angle, pitch angle, and heading angle) for each image exposure time, and generate an image parameter list containing the image's spatial position, attitude, and coverage area.

[0076] Specifically, based on the conversion parameters between the device coordinate system and the preset geographic coordinate system (such as CGCS2000), the structured laser point cloud data is converted from the scanning coordinate system to the geographic coordinate system. Combined with the attitude angle in the position and attitude file, the point cloud direction is corrected to generate a georegistered 3D point cloud. This process realizes the conversion of laser point cloud data from the device's local coordinates to general geographic coordinates, giving the data actual geographic significance and making it easier to integrate and analyze with other geospatial data. At the same time, attitude angle correction ensures the accuracy of the point cloud direction and improves the matching degree between the 3D point cloud and the actual geographic environment.

[0077] By fusing laser data with information such as device coordinates and attitude angles in the position and attitude files, and extracting the device's three-dimensional coordinates and attitude angles corresponding to each laser scanning moment, and combining them with laser point cloud data, the three-dimensional point cloud not only contains the spatial location information of ground features, but also relates to the device's attitude information when acquiring data. This fusion of multi-source data enriches the connotation of the data and can more comprehensively and accurately describe the terrain and ground feature characteristics of the surveying area.

[0078] Based on the device's 3D coordinates, attitude angles, and camera parameters (such as focal length and principal point coordinates) in the position and attitude file, the exterior orientation elements (longitude, latitude, elevation coordinates, roll angle, pitch angle, and heading angle) for each image exposure time are calculated. This generates an image parameter list containing the image's spatial position, attitude, and coverage area. Accurate exterior orientation element calculation can accurately determine the image's position and attitude in space, which is crucial for subsequent image correction, 3D reconstruction, and mapping product generation. It ensures accurate matching between image data and geographic coordinates, improving the image's geometric accuracy and spatial positioning capabilities.

[0079] Based on the image parameter list, aerial triangulation adjustment is performed on the image data to obtain the adjusted image data, which specifically includes:

[0080] Spatiotemporal correlation is established between the exterior orientation elements in the image parameter list and the corresponding image data to create a mapping relationship between the image and the pose data.

[0081] A free network adjustment model is constructed, and the relative orientation elements between images in the image data are solved by bundle adjustment to obtain the free network adjustment results;

[0082] Using the coordinates and elevation information of ground control points in the preset geographic coordinate system as absolute constraints, and combining them with the free network adjustment results, the exterior orientation elements of all images are obtained.

[0083] The exterior orientation elements of all images are adjusted to obtain the optimized exterior orientation elements.

[0084] Based on the optimized exterior orientation elements and the mapping relationship between the image and pose data, the original image is geometrically corrected to obtain the adjusted image data.

[0085] Specifically, the exterior orientation elements in the image parameter list are spatiotemporally correlated with the corresponding image data to establish a mapping relationship between the image and the pose data. This step ensures that the image data accurately corresponds to its position and pose information at the time of shooting, providing a reliable foundation for subsequent adjustment calculations. This allows the adjustment process to fully consider the spatial state at the time of image acquisition and improves the accuracy of the calculations.

[0086] A free net adjustment model is constructed, and the relative orientation elements between images in the image data are solved by bundle adjustment to obtain the free net adjustment results. The free net adjustment model can make full use of the relative relationship between images. Bundle adjustment can accurately calculate the relative orientation elements between images, reduce the geometric deformation and error between images, improve the internal consistency of image data, and prepare for the subsequent introduction of absolute constraints for joint solution.

[0087] By using the coordinates and elevation information of ground control points under the preset geographic coordinate system as absolute constraints, and performing joint calculations with the free network adjustment results, the exterior orientation elements of all images are obtained. The ground control points provide accurate geographic coordinate information, which, when added to the calculation process as absolute constraints, can effectively eliminate systematic errors that may exist in the free network adjustment, further improve the accuracy of the exterior orientation elements of the images, and make the image data better match the actual geographic space.

[0088] An overall adjustment is performed on the exterior orientation elements of all images to obtain optimized exterior orientation elements. The overall adjustment takes into account the interrelationships between the exterior orientation elements of all images. Through optimization and adjustment, each exterior orientation element is made more accurate and coordinated, reducing the impact of local errors on the overall results and improving the geometric accuracy of the entire image dataset.

[0089] Based on the optimized exterior orientation elements and the mapping relationship between the image and pose data, the original image is geometrically corrected to obtain the adjusted image data. After a series of precise adjustment calculations, the optimized exterior orientation elements can accurately reflect the position and orientation of the image in space. Based on this, geometric correction of the original image can effectively eliminate geometric deformations of the image, such as tilt and distortion, making the adjusted image data more consistent with the actual geographic spatial distribution and improving the geometric accuracy and usability of the image.

[0090] The process involves classifying 3D point clouds and generating digital elevation models based on the classified point clouds. Specifically, this includes:

[0091] The original 3D point cloud data is filtered and denoised, missing data is interpolated, and coordinate system is unified to generate a structured point cloud dataset.

[0092] Extract the geometric features (including normal direction, curvature, and height abrupt change value) and radiation features (including echo intensity and color information) of the point cloud, and use a random forest or deep learning classification model to automatically classify the point cloud based on the preset land cover categories (ground, vegetation, buildings, and water bodies).

[0093] Morphological opening operations were performed on point clouds classified as "ground" to remove residual low vegetation or artificial features that could cause interference.

[0094] A progressively encrypted triangular mesh (TIN) filtering algorithm is used to iteratively filter ground points with a grid resolution of 0.5m;

[0095] The extracted ground points are interpolated using a regular grid (interpolation methods include inverse distance weighting or kriging) to generate an initial digital elevation model map.

[0096] By fitting and correcting local outliers using topographic trend surfaces, a corrected digital elevation model map is obtained.

[0097] The revised digital elevation model map is divided into sheets, its outline is embellished, and metadata is added according to the 1:2000 scale surveying and mapping specifications to generate the final digital elevation model map.

[0098] Specifically, geometric features (including normal direction, curvature, and height abrupt change values) and radiation features (including echo intensity and color information) of point clouds are extracted. Random forest or deep learning classification models are used to automatically classify point clouds based on preset land cover categories (ground, vegetation, buildings, and water bodies). The comprehensive use of geometric and radiation features can more comprehensively describe the properties of point clouds. Random forest or deep learning models have powerful classification capabilities and can accurately classify point clouds into different land cover categories.

[0099] Morphological opening operations are performed on point clouds classified as "ground" to remove residual low vegetation or artificial features. Then, a progressively denser triangulated mesh (TIN) filtering algorithm is used to iteratively filter ground points at a grid resolution of 0.5m. Morphological opening operations can smooth the ground point cloud and remove some small bumps and interference. The progressively denser triangulated mesh filtering algorithm can gradually filter out accurate ground points based on the spatial distribution characteristics of the point cloud, improving the accuracy and reliability of ground point extraction.

[0100] Regular grid interpolation can convert discrete ground point data into a continuous terrain surface model; terrain trend surface fitting can further correct local anomalies in the model, making the digital elevation model more consistent with the actual terrain; processing according to surveying and mapping standards ensures the standardization and usability of the resulting maps.

[0101] Based on the adjusted image data and digital elevation model mapping, digital orthophoto maps and digital line maps are generated, specifically including:

[0102] Using a collinear equation model, the image is orthorectified pixel by pixel based on the terrain elevation information provided by the DEM to eliminate projection distortion caused by terrain undulations and generate orthorectified image units with geometric distortion of less than 0.2 pixels. The collinear equation model can accurately describe the geometric relationship between image points, projection center and ground points. Combined with the elevation information of the DEM, it can effectively compensate for image distortion caused by terrain undulations, so that the corrected image units have high geometric accuracy.

[0103] After orthorectification, the image units undergo tone equalization and seam processing to eliminate color differences and overlapping areas between adjacent images. The orthorectified images are then cropped according to preset map sheet division rules (such as 1:2000 scale map sheets), and map frame finishing, coordinate grids, and metadata information are added to generate digital orthorectified image maps. Tone equalization makes the colors of the images more uniform and natural, improving the visual effect of the images. Seam processing eliminates splicing marks, making the overall image more coherent. Map sheet division and the addition of relevant information ensure that the digital orthorectified image maps conform to surveying and mapping standards, facilitating storage, management, and use.

[0104] Based on digital orthophoto maps, a semi-automatic vectorization method is used to extract ground features, including topographic features and ground line features. The topographic features include contour lines and elevation annotations, while the ground line features include road centerlines, building outlines, and water system boundaries. The semi-automatic vectorization method combines the advantages of manual intervention and automatic extraction, which can improve efficiency while ensuring extraction accuracy. Through this method, various ground features can be accurately extracted.

[0105] The extracted geographic features are symbolically rendered and layered to generate digital line maps. Symbolic rendering allows geographic features to be represented on the map with intuitive and easy-to-understand symbols, enhancing the readability of the map. Layer organization facilitates the management and analysis of different types of geographic features.

[0106] A mapping product generation system for the low-altitude economy includes:

[0107] The data acquisition module is used to acquire image data, IMU (Inertial Measurement Unit) data, rover data, base station data, and laser data based on low-altitude economic equipment;

[0108] The integrated navigation calculation module is used to perform integrated navigation calculations based on IMU (Inertial Measurement Unit) data, rover data, and base station data, and generate position and attitude files.

[0109] The point cloud and parameter generation module is used to generate image parameter lists and 3D point clouds based on laser data and position and attitude files;

[0110] The image correction module is used to perform aerial triangulation adjustment on the image data based on the image parameter list to obtain the adjusted image data.

[0111] The point cloud classification and model generation module is used to classify 3D point clouds and generate digital elevation models based on the classified 3D point clouds.

[0112] The surveying product generation module is used to generate digital orthophoto maps and digital line maps based on adjusted image data and digital elevation models, thereby generating surveying products.

[0113] A computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described methods for generating mapping products for the low-altitude economy.

[0114] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the above-described methods for generating mapping products for the low-altitude economy.

[0115] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for generating surveying and mapping products for the low-altitude economy, characterized in that, include: Image data, IMU data, rover data, base station data, and laser data are acquired using low-altitude economic equipment; Based on IMU data, rover data, and base station data, a combined navigation solution is performed to generate a position and attitude file; Based on laser data and position and attitude files, generate an image parameter list and a 3D point cloud; Aerial triangulation adjustment is performed on the image data based on the image parameter list to obtain the adjusted image data; The 3D point cloud is classified and processed, and a digital elevation model is generated based on the classified 3D point cloud. Based on the adjusted image data and digital elevation model, digital orthophoto maps and digital line maps are generated, thus producing surveying and mapping products.

2. The method for generating surveying and mapping products for the low-altitude economy according to claim 1, characterized in that, The acquisition of image data, IMU data, rover data, base station data, and laser data based on low-altitude economic equipment specifically includes: Low-altitude economic equipment collects image data, IMU data, rover data, base station data, and laser data. The image data is used for image processing and 3D reconstruction. The IMU data is used to provide attitude and motion information of the low-altitude economic equipment. The rover data and base station data are used in conjunction for the positioning of the low-altitude economic equipment. The laser data is used to generate 3D point clouds to obtain spatial information of terrain and ground features.

3. The method for generating surveying and mapping products for the low-altitude economy according to claim 2, characterized in that, The step of performing integrated navigation calculations based on IMU data, rover data, and base station data to generate a position and attitude file specifically includes: Based on rover data and base station data, the three-dimensional position and velocity information of low-altitude economic equipment in a preset coordinate system are calculated in real time using carrier phase differential technology to obtain differential GNSS positioning results. Using IMU data, the three-dimensional position, velocity, and attitude angles of the low-altitude economic equipment are calculated through integration to obtain a temporary solution for inertial navigation; The differential GNSS positioning results and the inertial navigation temporary solution are input into a tightly coupled Kalman filter to obtain a tightly coupled solution containing timestamps, three-dimensional coordinates, velocity, roll angle, pitch angle, and heading angle. The tightly coupled solution results are recorded as position and attitude files in a preset format.

4. The method for generating surveying and mapping products for the low-altitude economy according to claim 3, characterized in that, The process of generating an image parameter list and a 3D point cloud based on laser data and position / pose files specifically includes: The laser data is denoised, filtered, and intensity corrected to generate structured laser point cloud data. Based on the position and attitude file, extract the device's three-dimensional coordinates, attitude angles, and timestamps corresponding to each laser scanning moment; Based on the conversion parameters between the device coordinate system and the preset geographic coordinate system, the structured laser point cloud data is converted from the scanning coordinate system to the geographic coordinate system. At the same time, the orientation of the point cloud is corrected by the attitude angle in the position and attitude file, and a georegistered 3D point cloud is generated. Based on the device's 3D coordinates, attitude angles, and camera parameters in the position and attitude file, the exterior orientation elements at each image exposure time are calculated to generate an image parameter list containing the image's spatial position, attitude, and coverage area.

5. The method for generating surveying and mapping products for the low-altitude economy according to claim 4, characterized in that, The step of performing aerial triangulation adjustment on the image data based on the image parameter list to obtain the adjusted image data specifically includes: Spatiotemporal correlation is established between the exterior orientation elements in the image parameter list and the corresponding image data to create a mapping relationship between the image and the pose data. A free network adjustment model is constructed, and the relative orientation elements between images in the image data are solved by bundle adjustment to obtain the free network adjustment results; Using the coordinates and elevation information of ground control points in the preset geographic coordinate system as absolute constraints, and combining them with the free network adjustment results, the exterior orientation elements of all images are obtained. The exterior orientation elements of all images are adjusted to obtain the optimized exterior orientation elements. Based on the optimized exterior orientation elements and the mapping relationship between the image and pose data, the original image is geometrically corrected to obtain the adjusted image data.

6. The method for generating surveying and mapping products for the low-altitude economy according to claim 5, characterized in that, The process of classifying the 3D point cloud and generating a digital elevation model based on the classified 3D point cloud specifically includes: Preprocess the 3D point cloud to generate a structured point cloud dataset; Extract geometric and radial features from structured point cloud datasets; The ground classification results are obtained by classifying the geometric and radial features of the structured point cloud dataset. Morphological opening operations are performed based on the ground classification results to extract ground points; The extracted ground points are interpolated using a regular grid to generate a digital elevation model map.

7. The method for generating surveying and mapping products for the low-altitude economy according to claim 6, characterized in that, The mapping based on the adjusted image data and digital elevation model, generating digital orthophoto maps and digital line maps, specifically includes: Based on the terrain elevation information provided by the digital elevation model, the adjusted image data is orthorectified pixel by pixel to obtain orthorectified image units. Perform tone equalization and seam processing on the orthorectified image units to generate a digital orthophoto map. Based on digital orthophoto maps, a semi-automatic vectorization method is used to extract ground features; The extracted geographic features are symbolically rendered and layered to generate digital line maps.

8. A surveying and mapping product generation system for the low-altitude economy, characterized in that, include: The data acquisition module is used to acquire image data, IMU data, rover data, base station data, and laser data based on low-altitude economic equipment; The integrated navigation calculation module is used to perform integrated navigation calculations based on IMU data, rover data, and base station data, and generate a position and attitude file. The point cloud and parameter generation module is used to generate image parameter lists and 3D point clouds based on laser data and position and attitude files; The image correction module is used to perform aerial triangulation adjustment on the image data based on the image parameter list to obtain the adjusted image data. The point cloud classification and model generation module is used to classify 3D point clouds and generate digital elevation models based on the classified 3D point clouds. The surveying product generation module is used to generate digital orthophoto maps and digital line maps based on adjusted image data and digital elevation models, thereby generating surveying products.

9. A computing device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for generating mapping products for the low-altitude economy as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for generating surveying and mapping products for the low-altitude economy as described in any one of claims 1-7.

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