Section measurement method based on aerial photography dense point cloud of unmanned aerial vehicle

Abstract

The invention relates to a section measurement method based on unmanned aerial vehicle aerial photography dense point clouds, and the method comprises the steps: carrying out the dense point cloud matching according to unmanned aerial vehicle aerial photography data, and obtaining the dense point cloud data matched with an unmanned aerial vehicle image; dense point cloud data matched with an unmanned aerial vehicle image are imported into point cloud data processing software, point cloud manual classification is carried out, and a TIN structure digital elevation model is constructed; making section lines according to line position data and section acquisition requirements; importing a section line into the point cloud data processing software, then selecting the section line in the TIN structure digital elevation model, and extracting section data; and performing section node thinning on the extracted section data, performing format conversion, and outputting a section diagram. The section data can be automatically extracted, manual three-dimensional acquisition is not needed, the operation efficiency is higher, human errors are fewer, the cost is lower, and the safety coefficient is higher.

Description

Technical Field

[0001] The invention relates to the technical field of railway engineering survey, and in particular to a cross-section measurement method based on dense point clouds of unmanned aerial vehicle aerial photography. Background Art

[0002] The demand for new railway construction and reconstruction of existing railways is increasing day by day, and the cross-section measurement of railway projects is also constantly changing with the development of surveying and mapping technology. The horizontal and vertical sections of railway projects can intuitively reflect the trend of terrain changes along the railway, and provide a data basis for survey professionals to calculate earthwork volume, etc.

[0003] At present, the commonly used cross-sectional data acquisition technologies in the industry mainly include field manual measurement, aerial survey stereo image acquisition, LiDAR point cloud extraction and other methods. However, these existing methods all have defects to varying degrees. The field manual measurement method has a large workload, low operating efficiency, is easily affected by weather, and has a low operating safety factor. The aerial survey stereo image acquisition method requires a large number of manual labor to collect cross-sectional feature points one by one. Due to the small image size of the drone, the manual collection speed is slow and the degree of automation is low. Although the LiDAR point cloud extraction method has a high degree of automation, the equipment price is relatively expensive, and the workload of manual classification of point cloud data is large.

[0004] Therefore, it is necessary to propose new measures to overcome the above-mentioned defects. Summary of the invention

[0005] The purpose of the present invention is to provide a cross-section measurement method based on dense point cloud of drone aerial photography, so as to solve the problems of large workload, low operation efficiency and high cost existing in the existing methods.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A cross-sectional measurement method based on a dense point cloud taken by an unmanned aerial vehicle, the method comprising:

[0008] Dense point cloud matching is performed based on drone aerial photography data to obtain dense point cloud data for drone image matching;

[0009] Import the dense point cloud data matched by drone images into the point cloud data processing software, perform manual point cloud classification, and construct a TIN structure digital elevation model;

[0010] Make section lines according to line position data and section acquisition requirements;

[0011] Import the section line into the point cloud data processing software, then select the section line in the TIN structure digital elevation model to extract the section data;

[0012] The extracted cross-section data is subjected to cross-section node thinning, and then format conversion is performed and the cross-section diagram is output.

[0013] Furthermore, the drone aerial photography data includes image data, camera parameters, POS data, and photo control point data.

[0014] Further, dense point cloud matching is performed, including:

[0015] Use image data, camera parameters, and POS data to establish encryption projects and perform dense point cloud matching;

[0016] Measure the image control points to perform regional network adjustment calculations and obtain dense point cloud data that meets the accuracy requirements.

[0017] Furthermore, the dense point cloud data matched by the drone image is imported into the point cloud data processing software, the point cloud is manually classified, and a TIN structure digital elevation model is constructed, including:

[0018] For areas covered with vegetation or with numerous landforms, manual classification of point clouds is performed within the cross-section line collection range;

[0019] The digital elevation model of TIN structure is constructed using the manually classified data.

[0020] Further, manual classification of point clouds includes:

[0021] Separate non-ground points from ground points and only keep ground point data.

[0022] Furthermore, the section line is produced according to the line position data and section acquisition requirements, including:

[0023] Collect line position data, including design centerline lines, curve element tables, and intersection coordinates;

[0024] Collect section collection requirements, including the start and end mileages of the cross section, left and right offsets, the angle between the section line and the design center line, and the start and end mileages of the longitudinal section;

[0025] Produce cross-section lines with plane coordinates according to line position data and section acquisition requirements;

[0026] According to the starting and ending mileage of the longitudinal section, a section is cut on the design center line as the longitudinal section line.

[0027] Furthermore, the section line is imported into the point cloud data processing software, and then the section line is selected in the TIN structure digital elevation model to extract the section data, including:

[0028] Select the cross-section line in the TIN structure digital elevation model and extract the cross-section data in text format.

[0029] Furthermore, the cross-section data includes section node offset, east coordinate, north coordinate, and elevation.

[0030] Furthermore, the extracted cross-section data is subjected to cross-section node thinning, including:

[0031] Set the tolerance of horizontal distance and elevation difference between the section node and its front and rear nodes;

[0032] The feature nodes whose elevation changes are greater than the set amplitude are retained.

[0033] Furthermore, the cross-section diagram is output, including:

[0034] The relative relationship between the elevation and offset of the characteristic nodes is plotted to obtain a cross-sectional view.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention provides a cross-sectional measurement method based on dense point clouds taken from drone aerial photography. The method is based on image data acquired by drone aerial photography and can complete cross-sectional collection only through internal work, which is safer and more efficient. Moreover, cross-sectional data can be automatically extracted without manual stereoscopic collection, resulting in higher operating efficiency and fewer human errors. In addition, drone equipment is cheaper than lidar equipment, has a higher safety factor for aerial photography, and significantly reduces the workload of manual classification of dense point clouds. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0038] Figure 1 It is a flow chart of the method of the present invention.

[0039] Figure 2 It is a schematic diagram of the dense point cloud obtained using drone aerial photography.

[0040] Figure 3 It is a schematic diagram of constructing a digital elevation model using dense point cloud.

[0041] Figure 4 It is a schematic diagram of cross-sectional data extracted using a digital elevation model.

[0042] Figure 5 It is a data schematic diagram in the general format of Latitude Software and the commonly used format of line professionals after section conversion.

[0043] Figure 6 It is a schematic diagram of a cross-section. DETAILED DESCRIPTION

[0044] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. The preferred embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thoroughly understood.

[0045] It should be noted that the terms "comprises", "comprising", "includes", "comprises" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0046] In addition, steps "S1", "S2" and the like involved in the embodiments are only for the convenience of describing the technical solutions and understanding the specific steps and contents of the embodiments, and should not be understood as limitations on the order of the steps. Any changes to the order of the steps should be within the scope of protection of the present invention.

[0047] Section measurement is the measurement of ground undulations in a certain direction, including longitudinal section measurement and cross section measurement. Based on the results of longitudinal section measurement, a longitudinal section diagram can be drawn for slope design. Cross section measurement is mainly used to calculate the volume of earth and stone to be cut and filled.

[0048] In order to reduce costs and increase efficiency and recombine the advantages of existing technologies, the present invention provides a cross-sectional measurement method based on dense point cloud obtained by drone aerial photography. The dense point cloud data is matched by using the images obtained by drone aerial flight to quickly obtain the horizontal and longitudinal sections required for railway engineering, which can effectively reduce operating costs.

[0049] like Figure 1 , the method specifically comprises the following steps:

[0050] S1: Based on the drone aerial photography data, use drone aerial triangulation software or 3D real scene modeling software to perform dense point cloud matching to obtain dense point cloud data for drone image matching.

[0051] The drone aerial photography data includes image data, camera parameters, POS data, and image control point data. Specifically:

[0052] ① Carry out reasonable route planning according to the project area, and strictly obtain drone image data according to the planned aerial photography. The image data format can be *.jpg or *.tif.

[0053] ② Collect the camera parameters of the drone, including focal length, pixel size, image size, distortion parameters, etc. Among them, the distortion parameters are non-essential parameters and can be obtained from the camera calibration certificate provided by the camera manufacturer.

[0054] ③ Collect POS files, which are the image exterior orientation elements obtained synchronously during drone aerial photography, including latitude, longitude, flight altitude, heading angle, pitch angle, and roll angle. Among them, the latitude and longitude in the image exterior orientation elements need to be converted into Gaussian plane coordinates and be consistent with the plane coordinate system and elevation system of the image control point.

[0055] ④According to the regional network layout, a certain number of image control points are obtained by GNSS-RTK measurement method. Image control points must be selected on obvious objects with clear images. After aerial photography, the puncture points can be selected from the intersection of thin linear objects with clear intersection angles, the vertices of obvious objects, or the centers of small point objects. Before aerial photography, the puncture points can be selected from landmarks drawn in the flight area. The layout of image control points must meet the relevant provisions of the regional network layout in "TB 10105-2010 Railway Engineering Photogrammetry Specifications".

[0056] In this step, drone aerial triangulation software or 3D real scene modeling software is used for dense point cloud matching, such as Godwork, ContextCapture, etc. Dense point cloud matching specifically includes:

[0057] S101: Use image data, camera parameters, and POS data to establish an encryption project and perform dense point cloud matching;

[0058] S102: Measure the control points of the images and perform regional block adjustment calculations to obtain dense point cloud data that meets the accuracy requirements of the "TB 10105-2010 Railway Engineering Photogrammetry Specifications", such as Figure 2 .

[0059] The filtering algorithms of different drone aerial triangulation software or 3D real scene modeling software vary greatly. Different dense point cloud matching strategies can be selected according to the image resolution and terrain features of the project, thereby improving the matching results of dense point clouds and reducing subsequent manual intervention. In some embodiments, Godwork drone encryption software can be used for dense point cloud matching, the feature point limit in the GPU configuration is set to 80,000, the connection point limit is set to 8,000, and the dense point cloud data finally obtained is in *.las format.

[0060] S2: Import the dense point cloud data matched by drone images into the point cloud data processing software, manually classify the point cloud, and build a TIN structure digital elevation model. Specifically include:

[0061] S201: The dense point cloud quality in areas without vegetation cover and simple terrain is good, and no manual classification is required; for areas with vegetation cover or numerous landforms, manual classification of ground points is performed within the cross-section line collection range;

[0062] S202: Use point cloud data processing software to construct a digital elevation model of the TIN structure using the manually classified ground points.

[0063] Point cloud classification is to separate non-ground points from ground points and only retain ground point data. In this way, the extracted section data are all coordinate values ​​of ground points, and the accuracy of section node elevation values ​​is not affected by vegetation or buildings that are above the ground.

[0064] In this step, TerraSolid point cloud data processing software was used to manually classify the point cloud, and the TerraModel module was used to build a digital elevation model.

[0065] S3: Create cross-section lines based on line position data and cross-section acquisition requirements. Specifically include:

[0066] S301: Collect line position data, i.e. the plane line parameters of railway design provided by survey data professionals, including design centerline lines, curve element tables, intersection coordinates, etc.

[0067] S302: Collect section collection requirements, including the start and end mileages of the cross section, left and right offsets, the angle between the section line and the design center line, and the start and end mileages of the longitudinal section.

[0068] S303: Use the section line making tool to make a two-dimensional cross-section line with plane coordinates, which can intuitively reflect the positional relationship between the cross-section line and the design center line. The two-dimensional line has a direction and is generally drawn from left to right along the direction of the railway. The section line data can be in *.dwg or *.dxf format.

[0069] S304: The longitudinal section line generally refers to the center line of the railway line. According to the starting and ending mileage of the longitudinal section, a section is cut on the designed center line as the longitudinal section line.

[0070] In this step, the section line making function module in the "Mapmate" plug-in developed based on the AutoCAD platform can be used as a section line making tool. After inputting the railway line position data and the specific parameters of the cross section required by professional requirements, the cross section line is automatically generated on the design center line. The section line data format is *.dwg, such as Figure 3 shown.

[0071] S4: Import the horizontal and vertical section lines into the point cloud data processing software, and then select the section lines in the TIN structure digital elevation model to extract the section data. Specifically include:

[0072] Click to select the cross-section line, and use the "Output profile elevations" function in the "Draw Section View" toolbar of the TerraScan module to automatically extract the cross-section data in text format. The direction of the cross-section line is set to "Increasing", enter the cross-section left offset in the starting point data box, and check the automatically generated cross-section right offset. The layout of the section node is set to "1m", the tolerance of the slope change is set to "2%, and the section data format is selected as "Section node offset, east coordinate, north coordinate, elevation", such as Figure 4 shown.

[0073] Generally, the longitudinal section data only requires the section node mileage, east coordinate, north coordinate and elevation value, which can be used directly after the data is extracted.

[0074] S5: Thin out the extracted cross-section data, convert the format, and output the cross-section diagram. Specifically including:

[0075] S501: performing cross-section node thinning on the extracted cross-section data, including:

[0076] The cross-section data extracted from the point cloud data processing software has dense nodes and large data volume. It can be thinned out by setting the horizontal distance and elevation difference limit between the cross-section node and its front and rear nodes, and retaining the feature nodes with elevation changes greater than the set range. The set range is specifically that the distance between adjacent cross-section nodes is no more than 5m, and the elevation value difference is no more than 0.3m, ensuring that there is at least one cross-section node every 5m, and there must be a cross-section node when the terrain change elevation difference is greater than 0.3m.

[0077] Then, the section format extracted from the point cloud data processing software is converted into the data format required by each surveying profession.

[0078] Finally, the relative relationship between the elevation and offset of the characteristic nodes is plotted in AutoCAD to obtain the cross-sectional view.

[0079] The longitudinal section generally only requires the east coordinate, north coordinate and elevation values ​​at integer mileages, or special locations required by the surveying profession, such as the coordinates and elevations of the straight-slow points and slow-straight points on the center line. Generally, there is no need to draw a cross-section diagram, and only text information of the coordinates is required.

[0080] This step uses the "Section Assisted Calculation Software" tool to convert the section data obtained from the TerraSolid point cloud data processing software into the general format of the Latitude software and the format commonly used by the line professional, such as Figure 5As shown. The section nodes are thinned out while the format conversion is being performed. The parameter settings for section thinning are distance "5m" and height difference "0.3m". Next, use the section line display function in the "Mapmate" plug-in developed based on the AutoCAD platform, set the drawing scale to "1:200, table height, layer, color, text style and other parameters can be set according to professional needs, and the final output format is a cross-section view of *.dwg, as shown in the figure below. Figure 6 shown.

[0081] Compared with the manual field measurement method, the present invention is based on the image data obtained by the drone flight, and the cross-section collection can be completed only through the office work, which is safer and more efficient. Compared with the aerial survey stereo image acquisition method, this method can automatically extract cross-section data without the need for manual stereo acquisition, with higher operating efficiency and fewer human errors. Compared with the LiDAR point cloud extraction method, drone equipment is lower in cost than laser radar equipment, has a higher safety factor for aerial photography, and has a smaller workload for manual classification of dense point clouds. Compared with the oblique photogrammetry method, drone equipment is lower in cost, obtains less image data, does not require high-performance computer cluster processing, and has fewer and more convenient working steps, reducing the time for modeling and repairing real-life three-dimensional models.

[0082] Although the present invention takes railway engineering as the research object, the proposed solution is highly applicable and can also be extended and applied to other fields, such as highways and other rail transportation.

[0083] The above specific examples are used to illustrate the present invention, which is only used to help understand the present invention and is not intended to limit the present invention. For those skilled in the art, according to the concept of the present invention, some simple deductions, modifications or substitutions can be made.

Claims

1. A cross-sectional measurement method based on dense point cloud from drone aerial photography, characterized by: The method comprises: Dense point cloud matching is performed based on drone aerial photography data to obtain dense point cloud data for drone image matching; Import the dense point cloud data matched by drone images into the point cloud data processing software, perform manual point cloud classification, and construct a TIN structure digital elevation model; Make section lines according to line position data and section acquisition requirements; Import the section line into the point cloud data processing software, then select the section line in the TIN structure digital elevation model to extract the section data; The extracted cross-section data is subjected to cross-section node thinning, and then format conversion is performed and the cross-section diagram is output.

2. The cross-section measurement method based on dense point cloud of drone aerial photography according to claim 1 is characterized by: The drone aerial photography data includes image data, camera parameters, POS data, and photo control point data.

3. The cross-section measurement method based on dense point cloud of drone aerial photography according to claim 2 is characterized by: Perform dense point cloud matching, including: Use image data, camera parameters, and POS data to establish encryption projects and perform dense point cloud matching; Measure the image control points to perform regional network adjustment calculations and obtain dense point cloud data that meets the accuracy requirements.

4. The cross-section measurement method based on dense point cloud of drone aerial photography according to claim 3 is characterized by: Import the dense point cloud data matched by drone images into the point cloud data processing software, perform manual point cloud classification, and build a TIN structure digital elevation model, including: For areas covered with vegetation or with numerous landforms, manual classification of point clouds is performed within the cross-section line collection range; The digital elevation model of TIN structure is constructed using the manually classified data.

5. The cross-section measurement method based on dense point cloud of drone aerial photography according to claim 4 is characterized in that: Manual classification of point clouds, including: Separate non-ground points from ground points and only keep ground point data.

6. The cross-section measurement method based on dense point cloud from drone aerial photography according to claim 5 is characterized by: Make section lines according to line position data and section acquisition requirements, including: Collect line position data, including design centerline lines, curve element tables, and intersection coordinates; Collect section collection requirements, including the start and end mileages of the cross section, left and right offsets, the angle between the section line and the design center line, and the start and end mileages of the longitudinal section; Produce cross-section lines with plane coordinates according to line position data and section acquisition requirements; According to the starting and ending mileage of the longitudinal section, a section is cut on the design center line as the longitudinal section line.

7. The cross-section measurement method based on dense point cloud from drone aerial photography according to claim 6 is characterized by: Import the section line into the point cloud data processing software, then select the section line in the TIN structure digital elevation model to extract the section data, including: Select the cross-section line in the TIN structure digital elevation model and extract the cross-section data in text format.

8. The cross-section measurement method based on dense point cloud from drone aerial photography according to claim 7 is characterized in that: Cross-section data include section node offset, east coordinate, north coordinate, and elevation.

9. The cross-section measurement method based on dense point cloud from drone aerial photography according to claim 8 is characterized in that: The extracted cross-section data is subjected to cross-section node thinning, including: Set the tolerance of horizontal distance and elevation difference between the section node and its front and rear nodes; The feature nodes whose elevation changes are greater than the set amplitude are retained.

10. The cross-section measurement method based on dense point cloud of drone aerial photography according to claim 9 is characterized in that: Output cross-section diagram, including: The relative relationship between the elevation and offset of the characteristic nodes is plotted to obtain a cross-sectional view.

Images