High-rise building three-dimensional modeling surveying and mapping method based on unmanned aerial vehicle laser scanning

Abstract

The invention relates to the field of unmanned aerial vehicle surveying and mapping and building three-dimensional modeling, and discloses a high-rise building three-dimensional modeling surveying and mapping method based on unmanned aerial vehicle laser scanning, and the method comprises the following steps: deploying a laser radar, a millimeter wave radar, a polarized light camera and an intelligent blind area supplement unmanned aerial vehicle for cooperative scanning, and generating an optimal path through topological manifold learning; multi-source point cloud high-precision registration is achieved through geometric algebra and Lie group optimization, multi-sensor heterogeneous data are fused in combination with tensor decomposition, blind area dynamic adjustment paths are detected in real time, and finally a complete three-dimensional building model is generated. According to the method, the modeling precision and data integrity of a complex building structure (such as a glass curtain wall) can be remarkably improved, the problems of scanning blind areas and equipment single-point failure are effectively avoided, and an efficient and robust solution is provided for high-rise building three-dimensional modeling.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle surveying and mapping and building three-dimensional modeling, and in particular to a three-dimensional modeling and surveying method for high-rise buildings based on unmanned aerial vehicle laser scanning. Background Art

[0002] With the widespread application of drone technology in the field of architectural surveying and mapping, the 3D modeling method based on LiDAR has become an important means of measuring high-rise buildings. However, existing technologies still face significant challenges in practical applications. Traditional drone scanning methods are prone to scanning blind spots in complex building structures (such as glass curtain walls and dense support frames), resulting in missing modeling data; heterogeneous data collected by multiple sensors are difficult to effectively fuse due to coordinate system deviations and registration errors, affecting model accuracy.

[0003] In addition, the existing system has insufficient fault tolerance for equipment failure or environmental interference, and single point failures can easily lead to interruption of modeling tasks. Especially on reflective surfaces such as glass curtain walls, conventional sensors can easily misjudge open spaces due to reflected light interference, increasing flight risks.

[0004] These problems severely limit the reliability and practicality of UAV mapping technology in high-rise building scenarios, and there is an urgent need for a solution that can balance data integrity, modeling accuracy and system robustness. Summary of the invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a three-dimensional modeling and surveying method for high-rise buildings based on drone laser scanning, which solves the technical problems of scanning blind spots, multi-source data registration errors and insufficient system fault tolerance caused by complex structures in the existing technology in three-dimensional modeling of high-rise buildings.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A three-dimensional modeling and mapping method for high-rise buildings based on drone laser scanning, comprising the following steps: Deploy multiple drones for collaborative scanning to collect LiDAR point cloud data, millimeter wave radar penetration data, and polarized camera material data of buildings; Generate optimal flight paths for drones covering building surfaces based on topological manifold learning; Use geometric algebra and Lie group optimization to register multi-source point cloud data; Fusion of heterogeneous data from LiDAR, millimeter-wave radar, and polarization camera through tensor decomposition; Detect scanning blind spots in real time and dynamically adjust the drone path to generate a complete 3D building model.

[0007] Preferably, the drone comprises: At least one drone equipped with lidar; At least one drone equipped with millimeter-wave radar; At least one drone equipped with a polarized camera.

[0008] Preferably, the drone further comprises: At least one intelligent blind spot supplementary drone is used for real-time detection and filling of scanning blind spots.

[0009] Preferably, the step of generating the optimal flight path of the drone covering the building surface based on topological manifold learning includes: Extract key structural nodes of building point cloud and construct topological skeleton graph; Generate geodesic distance optimal paths covering building surfaces through manifold space embedding.

[0010] Preferably, the key structural nodes are extracted by using a second-order derivative threshold of a point cloud density function.

[0011] Preferably, the step of registering multi-source point cloud data using geometric algebra and Lie group optimization includes: The point cloud data is represented as a spinor, and the rotation and translation parameters are optimized through Lie group parameterization; Minimizing multi-source point cloud registration error.

[0012] Preferably, the step of fusing heterogeneous data of the laser radar, millimeter wave radar and polarization camera by tensor decomposition includes: Construct a four-dimensional tensor containing spatial coordinates, timestamp, sensor type, and feature confidence; Core features are extracted through canonical multivariate decomposition, and weights are dynamically assigned based on polarization data.

[0013] Preferably, the dynamic weight of the polarized light data is adjusted according to the polarization angle confidence of the glass curtain wall area.

[0014] Preferably, the step of real-time detection of scanning blind areas and dynamic adjustment of the drone path includes: Analyze point cloud density distribution in real time and identify low coverage areas; Generate blind spot supplementary path based on manifold geodesic distance.

[0015] The present invention also provides a high-rise building three-dimensional modeling and mapping system based on unmanned aerial vehicle laser scanning, comprising: Data collection modules, including multiple drones equipped with lidar, millimeter-wave radar, and polarized light cameras; Path planning module, used to generate UAV flight paths based on topological manifold learning; Point cloud registration module, aligning multi-source point cloud data through geometric algebra and Lie group optimization; Data fusion module, which fuses heterogeneous sensor data through tensor decomposition; Blind area processing module, real-time detection and dynamic filling of scanning blind areas; Modeling output module generates a complete three-dimensional building model.

[0016] The present invention provides a method for three-dimensional modeling and mapping of high-rise buildings based on drone laser scanning. It has the following beneficial effects: 1. The present invention deploys laser radar, millimeter wave radar and polarized light camera to work together with multiple drones, which can comprehensively collect building surface point cloud, penetrating structure and glass curtain wall material information. The laser radar provides high-precision geometric data, the millimeter wave radar penetrates the blocked area to supplement the depth information, and the polarized light camera avoids misjudgment of glass curtain wall reflection. The three data complement each other to ensure comprehensive coverage of complex building structures.

[0017] 2. The optimal flight path of the drone generated by the present invention based on topological manifold learning can cover the surface of the building with the minimum geodesic distance, avoiding the repeated scanning and blind spot problems in traditional path planning. Through manifold space embedding technology, combined with point cloud density and normal vector constraints, the path planning efficiency is significantly improved and the overall scanning time is shortened.

[0018] 3. The present invention uses geometric algebra and Lie group optimization to register multi-source point cloud data, which can efficiently align the spatial coordinate systems of different sensors and reduce registration errors. Through the spinor representation and Lie group parameterized iterative optimization, the cumulative error problem of traditional registration methods in complex structures is effectively solved, and the spatial consistency of the three-dimensional model is improved.

[0019] 4. The present invention fuses multi-source heterogeneous data through four-dimensional tensor decomposition technology, which can dynamically allocate confidence weights of different sensors (such as the high weight of polarized light data in the glass curtain wall area) to reduce noise interference. This technology combines physical properties with mathematical modeling to significantly improve the credibility of fused data and the ability to restore model details.

[0020] 5. The intelligent blind spot supplementary drone of the present invention detects low coverage areas through real-time point cloud density analysis, and dynamically generates supplementary paths based on manifold geodesic distance to ensure scanning integrity. At the same time, the drone acts as a redundant backup device, automatically taking over tasks when equipment fails, avoiding modeling interruptions due to single point failures, and improving system fault tolerance and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the method flow of the present invention; Figure 2 It is a schematic diagram of the system structure of the present invention.

[0022] Among them, 10, data acquisition module; 20, path planning module; 30, point cloud registration module; 40, data fusion module; 50, blind area processing module; 60, modeling output module. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] Please see attached Figure 1 The present invention provides a three-dimensional modeling and mapping method for high-rise buildings based on UAV laser scanning, which aims to deploy multiple UAVs to work together, use different sensors to collect multi-source data of high-rise buildings, and generate an accurate three-dimensional model of the building through intelligent data fusion and path optimization technology.

[0025] like Figure 1 As shown, the high-rise building three-dimensional modeling and mapping method based on drone laser scanning may include the following steps: S1. Deploy multiple drones to scan collaboratively and collect the building's lidar point cloud data, millimeter-wave radar penetration data, and polarized light camera material data; S2, generating optimal flight paths for drones covering building surfaces based on topological manifold learning; S3, using geometric algebra and Lie group optimization to align multi-source point cloud data; S4, fuses heterogeneous data of LiDAR, millimeter-wave radar and polarization camera through tensor decomposition; S5. Real-time detection of scanning blind areas and dynamic adjustment of the drone path to generate a complete three-dimensional building model.

[0026] The following is a detailed description of each step in the method of the present invention, which comprehensively describes the specific implementation principle, technical details and process of each step.

[0027] As for step S1, step S1 includes performing drone collaborative scanning of high-rise buildings to collect the building's lidar point cloud data, millimeter-wave radar penetration data, and polarized light camera material data. To ensure the comprehensiveness and accuracy of data collection, multiple drone systems work together to cover all parts and features of the building. The specific process is as follows: First, multiple drones are deployed for collaborative scanning. Each drone is equipped with different sensors to collect different types of data, as follows: At least one drone is equipped with a laser radar (LiDAR) to collect precise point cloud data on the surface and surroundings of the building. LiDAR calculates the position and distance of points by emitting laser beams and measuring the time it takes for the laser to return, thereby generating three-dimensional spatial coordinates.

[0028] At least one drone is equipped with a millimeter-wave radar, which is used to penetrate rain, fog or other obstructions and provide depth information outside buildings or obstructed areas. Millimeter-wave radar obtains the location and shape data of target objects by emitting high-frequency electromagnetic waves and receiving reflected waves.

[0029] At least one drone is equipped with a polarization camera to collect material information of the building's facade, especially the characteristics of the glass curtain wall. The polarization camera detects the polarization state of light and analyzes the polarization angle of light reflected from the surface to identify the characteristics of different materials.

[0030] Furthermore, in order to compensate for the blind spots that may be generated by traditional scanning methods, at least one intelligent blind spot supplementary drone is deployed. The drone is used to monitor the blind spots generated during the scanning process in real time and automatically adjust the path to fill these blind spots. The intelligent blind spot supplementary drone has a built-in high-performance computing module that can analyze the density distribution of point cloud data in real time and identify low coverage areas, and dynamically generate blind spot supplementary paths. By optimizing the path, the drone can supplement the unscanned areas to ensure the integrity of the collected data.

[0031] All drones’ working systems are synchronized and dispatched in real time through wireless communication networks. During the scanning process, each drone is dynamically adjusted according to the predetermined flight path to ensure coverage of all sides of the building and avoid repeated scanning or missing some areas. The drone path planning is based on the 3D model of the building and the LiDAR point cloud data to further optimize the flight route. The connection of the wireless communication network ensures that the flight data can be shared in real time between drones, avoiding data redundancy in the collaborative process.

[0032] Specifically, the process of collecting lidar data is to determine the location of a point by emitting a pulsed laser from a laser transmitter and calculating the time it takes for the laser to be reflected back. By continuously emitting lasers and measuring time, point cloud data of buildings can be drawn in three-dimensional space.

[0033] Millimeter wave radar collects data by emitting high-frequency electromagnetic waves and measuring the time delay of the reflected waves. Millimeter wave radar has strong penetration ability and is particularly suitable for scanning areas in the atmosphere or other obstructions. By measuring the time delay of the reflected waves, the radar can calculate the distance and shape of the target object.

[0034] The data collection of polarized light cameras is mainly based on the analysis of polarization angles. Polarized light cameras determine the surface characteristics of different materials by detecting the polarization angle of light reflected from the surface of an object. For surfaces such as glass curtain walls, polarized light cameras can accurately obtain the optical characteristics of the surface material of the object by analyzing the polarization characteristics of the reflected light, providing support for subsequent 3D modeling.

[0035] At the same time, by analyzing the polarization angle of reflected light, the polarized light camera can effectively distinguish between glass curtain walls and open spaces, avoiding misjudgment problems caused by high reflectivity. During drone navigation, if the glass curtain wall is not correctly identified, it may be misjudged as a traversable open space, resulting in flight path planning errors and increased risk of crashes. By introducing data from polarized light cameras, the AI ​​system can provide more reliable material identification information, avoid flight accidents caused by misjudgment, reduce reliance on deep learning models, reduce the risk of black-box AI decisions, and improve the interpretability and safety of the system.

[0036] In summary, the core of step S1 lies in the coordinated work of the multi-UAV system. Each UAV can automatically adjust its path according to the collected data during flight, detect and supplement the scanning blind area in real time.

[0037] For step S2, it generates the optimal flight path of the drone covering the building surface based on topological manifold learning. This step ensures that the drone completes high-precision scanning within the shortest path and optimizes data collection efficiency. The specific implementation method is as follows: First, the initial 3D point cloud model of the building is constructed based on the LiDAR point cloud data. Assume that the point cloud dataset of the building surface is ,in Represents a three-dimensional coordinate point, is the total number of point cloud data points.

[0038] Then, the key structural nodes are extracted from the point cloud data. The point cloud data is screened using the point cloud gradient change rate calculation method, and points with significant local gradient changes are selected as key nodes. The local gradient function is set as: in, Representative Points The gradient change rate at is selected to satisfy The point is taken as the key structural node. is the gradient change threshold.

[0039] Next, build the topological skeleton graph ,in is the set of key structural nodes, For edges between adjacent nodes. Define the weights of the edges using constraints based on geodesic distances : in, Indicate point and The Euclidean distance between are the normal vectors of the corresponding points, is the weight factor. This weight calculation method can take into account both geometric proximity and normal vector changes to ensure the rationality of the topological structure.

[0040] Based on the topological skeleton graph, the manifold learning method is used to build a UAV flight path optimization model. For the optimal path from the starting point to the target point, the path cost function is defined as: Use Dijkstra algorithm or A* algorithm to solve the minimum path cost The corresponding optimal path. Through this optimization process, it is ensured that the drone covers the building surface on the optimal path.

[0041] Finally, the optimized path is converted into the flight instructions of the UAV. Assume that the attitude parameters of the UAV are (corresponding to yaw angle, pitch angle, and roll angle respectively), the flight control input vector is ,in is the displacement speed of the UAV in three-dimensional space, is the angular velocity of rotation. The drone control system adjusts the attitude through the following control law: in, is the position of the drone in the world coordinate system, is the velocity component. Through this control strategy, the UAV is ensured to fly smoothly along the optimized path and complete the task of scanning the building.

[0042] In summary, this step generates the optimal flight path through the topological manifold learning method, enabling the drone to efficiently cover the building surface while avoiding path redundancy and scanning blind spots. The optimized path can be directly converted into drone flight control instructions to achieve accurate trajectory planning.

[0043] For step S3, geometric algebra and Lie group optimization are used to align multi-source point cloud data to ensure that data from different sensors can be accurately aligned to form a unified three-dimensional model. The specific implementation method is as follows: First, a spatial representation model of multi-source point cloud data is established. Assume that the lidar point cloud dataset is , the millimeter wave radar point cloud dataset is , the polarization camera dataset is .in, are the number of points in their respective point cloud data.

[0044] Then, the point cloud data is modeled by the rigid body transformation using the spinor representation method. Suppose the rotation matrix of a data set relative to the global coordinate system is , the translation vector is , then the transformation relationship of point cloud data in the unified coordinate system can be expressed as: in, is the transformed coordinate, belong Rotation group, belong The translation vector.

[0045] In order to optimize the point cloud registration results, Lie group Perform pose optimization. Set the variables to be optimized Lie algebra ,in: in, represents the rotation component, Represents the translation component.

[0046] Through index mapping Lie Algebra Mapping to Lie Group , and get the transformation matrix: in, is the antisymmetric matrix representation of Lie algebra, and its specific form is: The optimal registration parameters are solved by iterative optimization method, so that the error function minimum, where the error is defined as: Use Gauss-Newton method or Levenberg-Marquardt algorithm to minimize and iteratively update the transformation matrix , so that the point cloud data of different sensors are aligned in the same coordinate system.

[0047] Finally, all the registered point cloud data are fused to form a complete 3D building model and output to the subsequent modeling module for further analysis. This step ensures the accurate registration of multi-source data, improves the consistency of point cloud data, and provides accurate basic data for subsequent 3D reconstruction.

[0048] For step S4, tensor decomposition technology is used to fuse multi-source heterogeneous data from lidar, millimeter-wave radar and polarized light camera. This step aims to effectively fuse data obtained by different sensors, improve the accuracy and reliability of data, and provide accurate data support for subsequent 3D modeling. The specific implementation method is as follows: First, construct a four-dimensional tensor containing lidar, millimeter-wave radar, and polarization camera data , which is of the form: in, Represent different sensors, spatial coordinates, time and other features (such as confidence, etc.). Specifically, the dimension Corresponding sensor types (LiDAR, millimeter wave radar, polarization camera), Corresponding to the space coordinates, The corresponding timestamp, Corresponding confidence and other additional information.

[0049] Confidence is a key factor, especially in the process of multi-source data fusion. Different sensors have different accuracy and reliability in data acquired in different environments, so it is crucial to introduce confidence to reflect the credibility of each data. Specifically, confidence can be used to weight data from different sensors to ensure that high-confidence data is given a higher weight during fusion, thereby improving the quality of the overall data.

[0050] Each sensor The point cloud data generated by (such as laser radar, millimeter wave radar, polarized light camera) is , each data point Corresponding to a confidence , indicating the accuracy or reliability of the data point. The calculation of confidence is related to the measurement error of the sensor, environmental factors, the characteristics of the sensor itself, and the data quality. , the basic formula for confidence calculation is: in, For data points The measurement error (standard deviation) of the data. According to this formula, the smaller the measurement error, the greater the confidence level; conversely, the larger the measurement error, the smaller the confidence level.

[0051] In multi-source data fusion, each sensor data is weighted by calculating the weighted confidence of each sensor point cloud data. , the weighted confidence It can be expressed as: When fusing multi-source data, it is assumed that the data of each sensor is ,in For the The weighted data fusion formula is as follows: in, For the The weight of each sensor is calculated as: This means that the weight of each sensor is calculated based on the sum of its confidence scores across all data points. They will be weighted according to their confidence and eventually form a comprehensive fused dataset.

[0052] Next, the four-dimensional tensor is decomposed using tensor decomposition technology. The most common decomposition method is CP decomposition (Canonical Polyadic Decomposition). CP decomposition converts the original four-dimensional tensor into Decomposed into the product of multiple matrices, namely: in, is the rank of the decomposition, is a scalar factor, represents the outer product operation of a tensor, , , , is the factor matrix of the tensor, corresponding to the features of sensor, spatial coordinates, timestamp and confidence. Through this decomposition process, the main features in the data can be effectively extracted and redundant information can be reduced.

[0053] Furthermore, the decomposition results are optimized by introducing regularization terms to ensure the stability and accuracy of the decomposition results in practical applications. The form of the regularization term is: in, , , , is the regularization coefficient, Represents the Frobenius norm of the matrix. This regularization term can suppress overfitting and ensure that the fused data has good generalization ability.

[0054] Then, the decomposed data is used for multi-source data fusion. The data collected by different sensors can be weighted according to their physical properties and confidence levels to generate a comprehensive 3D model. Specifically, the point cloud data of the lidar has a higher accuracy for the appearance of the building, while the data of the millimeter-wave radar is more prominent in penetration, and the data of the polarized light camera can provide material information. When fusing the data, the weighted average method is used to adjust the weights of different sensors according to their accuracy and confidence levels to obtain comprehensive data: in, , , They are the point cloud data of LiDAR, millimeter wave radar and polarized light camera respectively. , , is the weight of each data, The fused point cloud data.

[0055] Finally, a high-precision 3D building model is generated through the fused data, providing reliable data input for subsequent modeling and analysis. This step can achieve accurate fusion of multi-source data, significantly improve the quality and reliability of point cloud data, and provide a basis for subsequent 3D modeling.

[0056] For step S5, it generates a complete 3D building model by real-time detection of scanning blind areas and dynamic adjustment of the drone path. This step identifies low coverage areas by analyzing the point cloud density distribution and generates a supplementary path based on the manifold geodesic distance to ensure the integrity of the scanning coverage. The specific implementation method is as follows: First, based on the fused data processed in steps S1 to S4, a point cloud density distribution model is established. Suppose the point cloud data set is , define the point cloud density function as: in, is the Gaussian kernel function, is the bandwidth parameter, which is used to control the smoothness of density estimation. , will satisfy The area is determined as a scanning blind area. is the preset density threshold.

[0057] Furthermore, based on the topological manifold space generated in step S2, a blind spot supplementary path is constructed. Let the center point of the blind spot be , the current position of the drone is , the shortest complementary path is generated by the geodesic distance optimization model: in, is the metric tensor of the manifold space, derived from the topological skeleton graph in step S2. The optimal path is solved by the A* algorithm , allowing the intelligent blind spot supplementary UAV to fly along the path and fill the blind spot.

[0058] At the same time, the intelligent blind spot supplementary drone is used as a redundant backup device. When other drones fail due to malfunction or communication interruption, it automatically takes over the scanning task of the failed drone. The redundant backup mechanism is realized by real-time monitoring of the status of each drone. Suppose the drone state vector is ,in Indicates The working status of the drone (0 means failure, 1 means normal). When the backup drone's mission takeover protocol is triggered: Then, the flight control parameters of the UAV are adjusted dynamically. Assume the target position is , the speed of the drone is , the control law is designed as: in, is the proportional gain, The PID controller is used to adjust the trajectory of the drone in real time to ensure that it moves along the Precision flying.

[0059] Finally, the scan data will be supplemented Combined with the fused data output from step S4, the registration is performed using the iterative closest point (ICP) algorithm: in, , is the rotation matrix and translation vector, are corresponding points in the global coordinate system. The registered data is input into the 3D reconstruction module to generate a complete 3D building model. .

[0060] In summary, this step realizes real-time detection and supplementation of scanning blind spots through density analysis, redundant backup and dynamic path adjustment, while ensuring the robustness of the system when some equipment fails, and can effectively solve the scanning coverage problem in complex building structures.

[0061] In general, the present invention achieves full scene coverage by deploying multiple drones equipped with laser radars, millimeter-wave radars, and polarized light cameras for collaborative scanning, combined with dynamic path optimization and redundant backup mechanisms of intelligent blind spot supplementary drones. First, the optimal flight path is generated based on topological manifold learning, multi-source point cloud registration is completed through geometric algebra and Lie group optimization, and heterogeneous sensor data is fused using tensor decomposition. Finally, blind spots are detected in real time and the path is adjusted dynamically to supplement scanning. This method can solve the problem of modeling interruption caused by misjudgment of glass curtain walls, missed scanning of blocked areas, and equipment failure, and generate a high-precision, complete three-dimensional building model.

[0062] The three-dimensional modeling and mapping system for high-rise buildings based on drone laser scanning described below and the three-dimensional modeling and mapping method for high-rise buildings based on drone laser scanning described above can be referred to each other.

[0063] Please see attached Figure 2 The present invention also provides a high-rise building three-dimensional modeling and mapping system based on drone laser scanning, comprising: The data acquisition module 10 includes a plurality of drones equipped with laser radars, millimeter wave radars and polarized light cameras; A path planning module 20, for generating a UAV flight path based on topological manifold learning; Point cloud registration module 30, aligning multi-source point cloud data through geometric algebra and Lie group optimization; A data fusion module 40, which fuses heterogeneous sensor data through tensor decomposition; A blind area processing module 50 detects and dynamically fills the scanning blind area in real time; The modeling output module 60 generates a complete three-dimensional building model.

[0064] The system of this embodiment can be used to execute the above method embodiments, and its principles and technical effects are similar, which will not be repeated here.

[0065] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for three-dimensional modeling and mapping of high-rise buildings based on drone laser scanning, characterized in that: The following steps are involved: Deploy multiple drones for collaborative scanning to collect LiDAR point cloud data, millimeter wave radar penetration data, and polarized light camera material data of buildings; Generate optimal flight paths for drones covering building surfaces based on topological manifold learning; Use geometric algebra and Lie group optimization to register multi-source point cloud data; Fusion of heterogeneous data from LiDAR, millimeter-wave radar, and polarization camera through tensor decomposition; Detect scanning blind spots in real time and dynamically adjust the drone path to generate a complete 3D building model.

2. The method for three-dimensional modeling and mapping of high-rise buildings based on drone laser scanning according to claim 1 is characterized in that: The drone comprises: At least one drone equipped with lidar; At least one drone equipped with millimeter-wave radar; At least one drone equipped with a polarized camera.

3. The method for three-dimensional modeling and mapping of high-rise buildings based on drone laser scanning according to claim 2 is characterized in that: The drone also includes: At least one intelligent blind spot supplementary drone is used to detect and fill the scanning blind spots in real time.

4. The method for three-dimensional modeling and mapping of high-rise buildings based on drone laser scanning according to claim 1 is characterized in that: The step of generating the optimal flight path of the drone covering the building surface based on topological manifold learning includes: Extract key structural nodes of building point cloud and construct topological skeleton graph; Generate geodesic distance optimal paths covering building surfaces through manifold space embedding.

5. The method for three-dimensional modeling and mapping of high-rise buildings based on drone laser scanning according to claim 4 is characterized in that: The key structural nodes are extracted by using the second-order derivative threshold of the point cloud density function.

6. The method for three-dimensional modeling and mapping of high-rise buildings based on drone laser scanning according to claim 1 is characterized in that: The step of registering multi-source point cloud data using geometric algebra and Lie group optimization includes: The point cloud data is represented as a spinor, and the rotation and translation parameters are optimized through Lie group parameterization; Minimizing multi-source point cloud registration error.

7. The method for three-dimensional modeling and mapping of high-rise buildings based on drone laser scanning according to claim 1 is characterized in that: The step of fusing heterogeneous data of the laser radar, the millimeter wave radar and the polarization camera by tensor decomposition includes: Construct a four-dimensional tensor containing spatial coordinates, timestamp, sensor type, and feature confidence; Core features are extracted through canonical multivariate decomposition, and weights are dynamically assigned based on polarization data.

8. The method for three-dimensional modeling and mapping of high-rise buildings based on drone laser scanning according to claim 7 is characterized in that: The dynamic weight of the polarized light data is adjusted according to the polarization angle confidence of the glass curtain wall area.

9. The method for three-dimensional modeling and mapping of high-rise buildings based on drone laser scanning according to claim 1, characterized in that: The steps of real-time detection of scanning blind areas and dynamic adjustment of the drone path include: Analyze point cloud density distribution in real time and identify low coverage areas; Generate blind spot supplementary path based on manifold geodesic distance.

10. A high-rise building three-dimensional modeling and mapping system based on drone laser scanning, used to execute the method according to any one of claims 1 to 9, characterized in that: include: Data collection modules, including multiple drones equipped with lidar, millimeter-wave radar, and polarized light cameras; Path planning module, used to generate UAV flight paths based on topological manifold learning; Point cloud registration module, aligning multi-source point cloud data through geometric algebra and Lie group optimization; Data fusion module, which fuses heterogeneous sensor data through tensor decomposition; Blind area processing module, real-time detection and dynamic filling of scanning blind areas; Modeling output module generates a complete three-dimensional building model.

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