Three-dimensional terrain modeling method and system based on clustering and multi-base-station PPK calculation

Abstract

The invention relates to the technical field of unmanned aerial vehicle surveying and mapping, in particular to a three-dimensional terrain modeling method and system based on clustering and multi-base-station PPK resolving, and the method comprises the steps: planning an unmanned aerial vehicle route including a plurality of waypoints in a to-be-measured area; clustering the waypoints to obtain a plurality of classes and clustering centers corresponding to the classes, and building base stations at the clustering centers; controlling the unmanned aerial vehicle to fly along an unmanned aerial vehicle route and acquiring topographic image data at a waypoint to obtain unmanned aerial vehicle flight path data, topographic image data and a plurality of base station positioning data; dividing the flight trajectory data of the unmanned aerial vehicle based on classes to obtain a plurality of sub-trajectory data, performing PPK calculation on the sub-trajectory data and corresponding base station positioning data to obtain calculation results, and integrating the calculation results to obtain high-precision route coordinates; and constructing a three-dimensional terrain model of the to-be-measured area based on the high-precision route coordinates and the terrain image data. Compared with the prior art, the invention provides a more accurate surveying and mapping method.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle surveying and mapping, and in particular to a three-dimensional terrain modeling method and system based on clustering and multi-base station PPK solution. Background Art

[0002] UAV photogrammetry is a measurement method that has developed rapidly and is widely used in recent years. It mainly uses UAVs equipped with high-precision cameras and positioning equipment to collect and process data of the target area to generate a high-precision three-dimensional terrain model. UAVs are widely used in scientific research, industrial manufacturing, urban planning, land surveying and mapping and other fields due to their advantages such as high efficiency, low cost and strong flexibility.

[0003] At present, the mainstream positioning data solution method in UAV surveying and mapping technology is single-base station PPK solution, that is, differential solution is performed using the positioning data obtained by a single base station and the UAV. However, when measuring a large area, due to the long flight range of the UAV, the distance between the single base station and the UAV gradually increases, and problems such as signal attenuation and error accumulation increase significantly, resulting in a decrease in PPK solution accuracy. In addition, single-base station differential solution relies on differential calculation of data at the same time. When the base station is far away or the signal is unstable, the error of the solution result will be further amplified, affecting the accuracy of the three-dimensional terrain model. Summary of the invention

[0004] In order to overcome the defects of signal attenuation or error accumulation caused by single base station solution in the above-mentioned prior art, the present invention provides a three-dimensional terrain modeling method and system based on clustering and multi-base station PPK solution.

[0005] In order to achieve the above technical effects, the technical solution of the present invention is as follows:

[0006] A three-dimensional terrain modeling method based on clustering and multi-base station PPK solution includes the following steps:

[0007] Plan a drone route containing several waypoints in the area to be tested;

[0008] Clustering the waypoints to obtain several clusters and their corresponding cluster centers, and building base stations at the cluster centers;

[0009] Controlling the UAV to fly along the UAV route and acquiring terrain image data at the waypoint to obtain UAV flight track data and terrain image data; at the same time, the base station locates the UAV to obtain a number of base station positioning data;

[0010] The flight trajectory data of the UAV is divided based on the category of the waypoint to obtain a number of sub-trajectory data, the sub-trajectory data is subjected to PPK solution with the corresponding base station positioning data, the solution results are obtained and integrated to obtain high-precision route coordinates;

[0011] Modeling is performed based on the high-precision route coordinates and the terrain image data to obtain a three-dimensional terrain model of the area to be measured.

[0012] The present invention also proposes a three-dimensional terrain modeling system based on clustering and multi-base station PPK solution, the system comprising:

[0013] Waypoint clustering module: used to cluster the waypoints to obtain several clusters and their corresponding cluster centers;

[0014] Data acquisition module: used to collect the flight trajectory data and terrain image data of the UAV; when the UAV is flying, the UAV is positioned through several base stations to obtain several base station positioning data;

[0015] Route solving module: used for dividing the UAV flight trajectory data based on the class to obtain a number of sub-trajectory data, performing PPK solving on the sub-trajectory data and the base station positioning data at the corresponding cluster center, obtaining a plurality of solving results and integrating them to obtain high-precision route coordinates;

[0016] Model building module: used to build a model based on the high-precision route coordinates and the terrain image data to obtain a three-dimensional terrain model of the area to be measured.

[0017] The present invention also proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the three-dimensional terrain modeling method based on clustering and multi-base station PPK solution as described in the present invention is implemented.

[0018] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the three-dimensional terrain modeling method based on clustering and multi-base station PPK solution as described in the present invention is implemented.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] The present invention proposes a three-dimensional terrain modeling method based on clustering and multi-base station PPK solution. Multiple base stations are reasonably allocated within the survey area through the clustering method to ensure that each waypoint is matched to the closest and most suitable base station data for PPK solution. By integrating the solution data of multiple base stations, the error accumulation problem in a single base station and a large-scale survey area can be effectively solved, the accuracy of positioning data can be significantly improved, the solution error can be reduced, and the three-dimensional terrain modeling results can be ensured to be more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Flowchart of the three-dimensional terrain modeling method based on clustering and multi-base station PPK solution.

[0022] Figure 2 This is the architecture diagram of the 3D terrain modeling system based on clustering and multi-base station PPK solution. DETAILED DESCRIPTION

[0023] The accompanying drawings are only used for illustrative purposes and are not to be construed as limiting the present invention;

[0024] It is understandable to those skilled in the art that some well-known descriptions may be omitted in the drawings.

[0025] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0026] Example 1

[0027] This embodiment proposes a three-dimensional terrain modeling method based on clustering and multi-base station PPK solution, such as Figure 1 FIG. 1 is a flow chart of a three-dimensional terrain modeling method based on clustering and multi-base station PPK solution in this embodiment.

[0028] The three-dimensional terrain modeling method based on clustering and multi-base station PPK solution proposed in this embodiment includes the following steps:

[0029] Plan a drone route containing several waypoints in the area to be tested;

[0030] Clustering the waypoints to obtain several clusters and their corresponding cluster centers, and building base stations at the cluster centers;

[0031] Controlling the UAV to fly along the UAV route and acquiring terrain image data at the waypoint to obtain UAV flight track data and terrain image data; at the same time, the base station locates the UAV to obtain a number of base station positioning data;

[0032] The flight trajectory data of the UAV is divided based on the category of the waypoint to obtain a number of sub-trajectory data, the sub-trajectory data is subjected to PPK solution with the corresponding base station positioning data, the solution results are obtained and integrated to obtain high-precision route coordinates;

[0033] Modeling is performed based on the high-precision route coordinates and the terrain image data to obtain a three-dimensional terrain model of the area to be measured.

[0034] In this embodiment, the present invention reasonably allocates the base station positions in the survey area through a clustering algorithm to ensure that each waypoint in the route can be matched to the nearest and most suitable base station positioning data, thereby effectively reducing the impact of signal attenuation and error accumulation in the PPK solution, avoiding the problem of decreased positioning accuracy of a single base station in a large-scale survey area, and making the final generated three-dimensional terrain model have higher accuracy and reliability, meeting the needs of high-precision drone mapping in a large-scale survey area.

[0035] Exemplarily, when planning a drone route containing multiple waypoints in the test area, corresponding setting parameters need to be met, including heading overlap, lateral overlap, route angle, flight altitude and camera parameters, etc.; the route trajectory is calculated and output according to the set parameters to obtain the drone flight route.

[0036] As another exemplary description, a K-means clustering algorithm is used when clustering the waypoints.

[0037] In an optional embodiment, the step of clustering the waypoints to obtain a plurality of clusters and their corresponding cluster centers includes:

[0038] S1, dividing the area to be tested into multiple sub-test areas on average, and taking the centers of the sub-test areas as cluster centers;

[0039] S2, assign each waypoint to the class corresponding to the nearest cluster center;

[0040] S3, recalculate the corresponding cluster centers for all classes;

[0041] S4. Repeat steps S2 to S3 until a preset number of iterations is reached or the cluster center does not change, and obtain the dry class and its corresponding cluster center.

[0042] In this embodiment, by dividing the area to be tested into multiple sub-areas and initially setting the cluster centers, the problem of unstable clustering results caused by random initialization can be avoided. Then, the cluster centers are updated by repeated iterations to avoid inaccurate classification caused by cluster center drift.

[0043] In an optional embodiment, the step of recalculating the corresponding cluster centers of all classes includes:

[0044] Calculate the centroid of all waypoints in any class and use the centroid as the new cluster center.

[0045] In this embodiment, by calculating the centroid of each type of waypoint and using it as the new cluster center, the spatial distribution characteristics of the waypoints of this type can be accurately reflected, so that the clustering result is more in line with the actual data distribution.

[0046] In an optional embodiment, the step of performing PPK calculation on the sub-trajectory data and the base station positioning data at the corresponding cluster center includes:

[0047] Determine the exact coordinates of the base station, perform PPK solution based on the sub-trajectory data and the base station positioning data at the corresponding cluster center, and obtain the solution result.

[0048] In this embodiment, the base station positioning data at the corresponding cluster center is selected for different sub-trajectory data for PPK solution, which can reduce the distance error between the base station and the sub-trajectory data, significantly improve the positioning accuracy, avoid the error accumulation caused by the base station position deviating from the trajectory area, and improve the reliability of the solution result, so as to flexibly adapt to the large-scale measurement area environment covered by multiple base stations and ensure the solution effect in complex environments.

[0049] In an optional embodiment, the step of determining the exact coordinates of the base station includes:

[0050] Perform static baseline solution based on the base station positioning data of several base stations to obtain a baseline vector;

[0051] Determine the accurate coordinates of any base station; perform network adjustment calculation based on the baseline vector and the accurate coordinates of the base station to obtain the accurate coordinates of all base stations.

[0052] Exemplarily, when constructing a base station, adjustments can be made near the cluster center according to the actual geographical conditions, and a suitable control point can be selected in the area to be measured, one more base station can be set up and the fine coordinates can be determined to make the subsequent static baseline solution and network adjustment more accurate.

[0053] In this embodiment, the baseline vector is calculated by static baseline solution, and the global base station coordinates are optimized by network adjustment, thereby ensuring the high accuracy of the base station coordinates. The relative position relationship between base stations is adjusted by network adjustment calculation to eliminate the deviation in the coordinate system. Even if the base stations are distributed under different terrain conditions, the network adjustment calculation can effectively handle the baseline vector error and ensure the accuracy of coordinate solution in complex environments.

[0054] In an optional embodiment, the step of modeling based on the high-precision route coordinates and the terrain image data includes:

[0055] Extracting feature points from the terrain image data, matching feature points between different terrain image data, and performing spatial triangulation in combination with the high-precision route coordinates and the matching results to obtain point cloud data;

[0056] Searching for image data containing a base station in the terrain image data, and adjusting the point cloud data by using an adjustment algorithm using the accurate coordinates of the base station as a reference;

[0057] The adjusted point cloud data and terrain image data are used for three-dimensional modeling to obtain a three-dimensional terrain model of the area to be measured.

[0058] In this embodiment, different terrain image data are integrated through feature point extraction and matching to ensure seamless connection of cross-regional images; spatial triangulation is performed by combining high-precision route coordinates with terrain image data to generate high-density and accurate point cloud data; then, the accurate coordinates of the base station are used as a reference, and the point cloud data is optimized through an adjustment algorithm to eliminate the accumulated errors and noise in the point cloud data and enhance the reliability of the model.

[0059] In an optional embodiment, the step of integrating the multiple solution results includes: sorting the multiple solution results based on timestamps, and smoothing the splicing points between different solution results by using a curve fitting method.

[0060] In this embodiment, multiple solution results are sorted based on timestamps to ensure that different solution results are seamlessly connected in chronological order to avoid data gaps or confusion. Subsequently, the splicing points of different solution results are smoothed using a curve fitting method to effectively eliminate possible mutations or jumps at the splicing points, thereby improving the smoothness and naturalness of the integrated results.

[0061] Example 2

[0062] This embodiment proposes a three-dimensional terrain modeling system based on clustering and multi-base station PPK solution, and applies the three-dimensional terrain modeling method based on clustering and multi-base station PPK solution proposed in Example 1. Figure 2 , which is an architecture diagram of a three-dimensional terrain modeling system based on clustering and multi-base station PPK solution according to this embodiment.

[0063] This embodiment proposes a three-dimensional terrain modeling system based on clustering and multi-base station PPK solution, including:

[0064] Waypoint clustering module: used to cluster the waypoints to obtain several clusters and their corresponding cluster centers;

[0065] Data acquisition module: used to collect the flight trajectory data and terrain image data of the UAV; when the UAV is flying, the UAV is positioned through several base stations to obtain several base station positioning data;

[0066] Route solving module: used for dividing the UAV flight trajectory data based on the class to obtain a number of sub-trajectory data, performing PPK solving on the sub-trajectory data and the base station positioning data at the corresponding cluster center, obtaining a plurality of solving results and integrating them to obtain high-precision route coordinates;

[0067] Model building module: used to build a model based on the high-precision route coordinates and the terrain image data to obtain a three-dimensional terrain model of the area to be measured.

[0068] It can be understood that the system of this embodiment corresponds to the method of the above-mentioned embodiment 1, and the options in the above-mentioned embodiment 1 are also applicable to this embodiment, so they will not be described repeatedly here.

[0069] Example 3

[0070] This embodiment proposes a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, wherein when the computer-readable instructions are executed by the processor, the processor executes the steps of the three-dimensional terrain modeling method based on clustering and multi-base station PPK solution proposed in Example 1.

[0071] Example 4

[0072] This embodiment proposes a storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by a processor, implement the steps of the three-dimensional terrain modeling method based on clustering and multi-base station PPK solution proposed in Embodiment 1.

[0073] Exemplarily, the storage medium includes, but is not limited to, a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0074] Exemplarily, the instructions, programs, code sets or instruction sets may be implemented using conventional programming languages.

[0075] Exemplarily, the processor includes but is not limited to a smart phone, a personal computer, a server, a network device, etc., and is used to execute all or part of the steps of the three-dimensional terrain modeling method based on clustering and multi-base station PPK solution based on staged learning described in Example 1.

[0076] The terms used in the drawings are for illustrative purposes only and should not be construed as limiting this patent.

[0077] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A three-dimensional terrain modeling method based on clustering and multi-base station PPK solution, characterized in that: The following steps are involved: Plan a drone route containing several waypoints in the area to be tested; Clustering the waypoints to obtain several clusters and their corresponding cluster centers, and building base stations at the cluster centers; Controlling the UAV to fly along the UAV route and acquiring terrain image data at the waypoint to obtain UAV flight track data and terrain image data; at the same time, the base station locates the UAV to obtain a number of base station positioning data; The flight trajectory data of the UAV is divided based on the category of the waypoint to obtain a number of sub-trajectory data, the sub-trajectory data is subjected to PPK solution with the corresponding base station positioning data, the solution results are obtained and integrated to obtain high-precision route coordinates; Modeling is performed based on the high-precision route coordinates and the terrain image data to obtain a three-dimensional terrain model of the area to be measured.

2. A three-dimensional terrain modeling method based on clustering and multi-base station PPK solution according to claim 1, characterized in that: The step of clustering the waypoints to obtain a plurality of clusters and their corresponding cluster centers comprises: S1, dividing the area to be tested into multiple sub-test areas on average, and taking the centers of the sub-test areas as cluster centers; S2, assign each waypoint to the class corresponding to the nearest cluster center; S3, recalculate the corresponding cluster centers for all classes; S4. Repeat steps S2 to S3 until a preset number of iterations is reached or the cluster center does not change, and obtain the dry class and its corresponding cluster center.

3. A three-dimensional terrain modeling method based on clustering and multi-base station PPK solution according to claim 2, characterized in that: The step of recalculating the corresponding cluster centers of all classes includes: Calculate the centroid of all waypoints in any class and use the centroid as the new cluster center.

4. A three-dimensional terrain modeling method based on clustering and multi-base station PPK solution according to claim 1, characterized in that: The step of performing PPK calculation on the sub-trajectory data and the base station positioning data at the corresponding cluster center includes: Determine the exact coordinates of the base station, perform PPK solution based on the sub-trajectory data and the base station positioning data at the corresponding cluster center, and obtain the solution result.

5. A three-dimensional terrain modeling method based on clustering and multi-base station PPK solution according to claim 4, characterized in that: The step of determining the accurate coordinates of the base station comprises: Perform static baseline solution based on the base station positioning data of several base stations to obtain a baseline vector; Determine the accurate coordinates of any base station; perform network adjustment calculation based on the baseline vector and the accurate coordinates of the base station to obtain the accurate coordinates of all base stations.

6. A three-dimensional terrain modeling method based on clustering and multi-base station PPK solution according to claim 4, characterized in that: The step of modeling based on the high-precision route coordinates and the terrain image data comprises: Extracting feature points from the terrain image data, matching feature points between different terrain image data, and performing spatial triangulation in combination with the high-precision route coordinates and the matching results to obtain point cloud data; Searching for image data containing a base station in the terrain image data, and adjusting the point cloud data by using an adjustment algorithm using the accurate coordinates of the base station as a reference; The adjusted point cloud data and terrain image data are used for three-dimensional modeling to obtain a three-dimensional terrain model of the area to be measured.

7. A three-dimensional terrain modeling method based on clustering and multi-base station PPK solution according to any one of claims 1 to 6, characterized in that: The step of integrating the multiple solution results includes: sorting the multiple solution results based on timestamps, and smoothing the splicing points between different solution results by using a curve fitting method.

8. A three-dimensional terrain modeling system based on clustering and multi-base station PPK solution, applied to a three-dimensional terrain modeling method based on clustering and multi-base station PPK solution as claimed in any one of claims 1 to 7, characterized in that: The system comprises: Waypoint clustering module: used to cluster the waypoints to obtain several clusters and their corresponding cluster centers; Data acquisition module: used to collect the flight trajectory data and terrain image data of the UAV; when the UAV is flying, the UAV is positioned through several base stations to obtain several base station positioning data; Route solving module: used for dividing the UAV flight trajectory data based on the class to obtain a number of sub-trajectory data, performing PPK solving on the sub-trajectory data and the base station positioning data at the corresponding cluster center, obtaining a plurality of solving results and integrating them to obtain high-precision route coordinates; Model building module: used to build a model based on the high-precision route coordinates and the terrain image data to obtain a three-dimensional terrain model of the area to be measured.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the three-dimensional terrain modeling method based on clustering and multi-base station PPK solution is implemented as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the three-dimensional terrain modeling method based on clustering and multi-base station PPK solution as described in any one of claims 1 to 7 is implemented.

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