CN115077490B - Unmanned aerial vehicle naked eye 3D full-digital mapping method - Google Patents
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Abstract
The invention provides an unmanned aerial vehicle naked eye 3D full-digital mapping method, which is characterized in that an unmanned aerial vehicle aerial mapping method is adopted to establish dense point cloud data, a point cloud filtering classification method is adopted to extract ground characteristic points, a point cloud reverse engineering method is converted into a three-dimensional grid model, a grid space structure is optimized through grid hole modification and grid interpolation, contour parameters are set according to the requirements of a scale, a three-dimensional smooth contour is established, and the accuracy can reach within 5 cm. The contour line can be directly converted into a TIN triangular network and a Digital Elevation Model (DEM), and the dense point cloud data can be directly used for earth and stone side calculation and three-dimensional GIS model establishment. The method solves the problems that the existing model can not truly reflect the fluctuation of the terrain space, can not be directly applied to the calculation of earthwork and stone, the route selection of construction channels and the like, and the interaction efficiency of digital terrain products is affected. Belonging to the field of civil engineering.
Description
Technical Field
The invention relates to an unmanned aerial vehicle naked eye 3D full-digital mapping method, which is particularly suitable for field topographic mapping and belongs to the field of civil engineering.
Background
At present, the topographic map mapping mainly takes GPS-RTK as a main material, and has the problems of large field work load, long period, low working efficiency, limited precision and the like, so that the three-dimensional topographic information of the construction site can not be mastered truly, and certain influence is brought to project decision. Along with the popularization of unmanned aerial vehicle aerial survey technology, the advanced method can adopt unmanned aerial vehicle aerial survey to establish a three-dimensional terrain model (also called a live-action model), is influenced by building groups and dense vegetation bodies, but the model cannot truly reflect the fluctuation of the terrain space, cannot be directly applied to earth and stone computing, construction channel route selection and the like, and seriously influences the interaction efficiency of digital terrain products.
Disclosure of Invention
The invention provides an unmanned aerial vehicle naked eye 3D full-digital mapping method, which aims to solve the problems that the existing model cannot truly reflect the fluctuation of the terrain space, cannot be directly applied to earth and stone side calculation, construction channel line selection and the like, and influences the interaction efficiency of digital terrain products.
In order to achieve the purpose, the unmanned aerial vehicle naked eye 3D full-digital mapping method is adopted, and the specific scheme is as follows:
1) Aerial photo collection and processing
Using a portable navigation mark as an image control point, arranging 4-6 points around a measuring area, measuring the coordinate of a navigation mark center point by using RTK, and converting the coordinate into a WGS84-UTM coordinate system;
setting flight parameters on the line gauge software, wherein a flight task adopts a grid or circumferential flight mode, the height is controlled within 120m, the heading and lateral re-pasting rate is 80%, the camera inclination angle is 15 degrees, and the flight sampling time is 2s;
Copying the unmanned aerial vehicle image onto a computer, selecting any aerial photo to adjust the image exposure parameters, recording the parameter values of the aerial photo, carrying out batch processing on all aerial photos, and unifying the resolution, the exposure intensity and the color of all aerial photos;
In the aerial survey data processing software, newly-built engineering, unmanned aerial vehicle image data and phase control point coordinates are imported, a coordinate zone number of a measuring zone is calculated by adopting a formula Int (L/6) +31, L is a measuring zone longitude, a coordinate system is found correspondingly in a northern hemisphere, in bigmap map software, a measuring zone data volume range KML file is downloaded and added into the aerial survey data processing software, an aerial photograph processing range is defined, an aerial survey data processing flow is established, after the accuracy of a control point is checked to be qualified, the sampling interval of point clouds is set to be 1cm, high-accuracy dense point cloud data is produced, and a las format is selected by point cloud output.
2) Classifying point cloud filtering;
Importing a navigation measurement point cloud in point cloud data software, defining an initial point cloud as an unclassified state in a point cloud classification module, and classifying ground points by adopting a mode of combining a parameter filtering classification method with a manual classification method;
the method comprises the steps of parameter filtering classification, firstly extracting ground point cloud by adopting parameter filtering classification, framing all point cloud data, selecting a ground classification function, setting point cloud filtering parameters, and setting points and filtering parameters under different topography ranges as shown in the following table:
table 1: point cloud filtering classification parameter table for different topography
| Topography of the ground | Building size | Ground angle | Iterative angle | Ground distance | Iterative side length |
| Plain area | < Maximum building projection Length | <30° | 4°～6° | <1m | 1m～2m |
| Hilly areas | < Maximum building projection Length | 30°～60° | 6°～12° | 1m～1.5m | 2m～3m |
| Mountain area | < Maximum building projection Length | 60°～75° | 12°～20° | 1m～1.5m | 3m～5m |
After the automatic classification is finished, manual classification matching is needed to be adopted, classification abnormal areas are defined one by one, the 5 filtering parameters are adjusted, the point cloud is optimized until satisfactory ground point cloud is obtained, the range of a building body is directly defined for deleting the area where the building body is dense, and the ground point cloud is reserved;
And (3) smoothing the point cloud, selecting the classified ground point cloud, setting the maximum restoration value of the point cloud to be 0.1m, the shrinkage radius to be 0.2m, and the smoothing type to be the three-dimensional coordinate value XYZ, so as to ensure the fineness and quality of the subsequent three-dimensional grid establishment.
3) Establishing a grid curved surface;
Establishing a grid, setting a proper point cloud sampling interval under a grid module, and converting point cloud data into a three-dimensional grid model;
The internal hole modification, aiming at filling the grid holes, adopts the combination of manual hole filling and automatic hole filling, mainly adopts automatic hole filling, sets the hole length, fully selects the internal holes, and adjusts the smoothness rate of the holes by combining the site topography condition so as to realize filling of the internal holes;
External hole modification, namely selecting two characteristic points or characteristic lines at the edge for filling the external hole, creating a new boundary by a bridging method, dividing the original external hole into a new external hole and an internal hole, and filling the new external hole and the internal hole by adopting an internal hole filling method;
4) Triangle net interpolation process
The triangular net is optimized by adopting a distance square reciprocal weighted spatial interpolation method, slope points are calculated by an interpolation method by utilizing the spatial position relation between points on a slope and points under the slope, and then the slope points are connected with surrounding points again to construct the triangular net.
5) Parameterized mapping
Based on the interpolation grid, setting the distance n of common contour lines and the distance m of main contour lines according to the requirements of digital mapping scales, wherein m=5n, and the contour line parameters of different scales are detailed in the following table:
Table 2: equal-altitude parameter values of different scales
| Proportional scale | 1:500 | 1:1000 | 1:5000 | 1:10000 | 1:50000 |
| Constant-altitude distance | 0.5m | 1m | 2.5m | 5m | 10m |
| Major constant-altitude distance | 2.5m | 5m | 12.5m | 25m | 50m |
Adopting a space spline curve fitting method to optimize broken lines in the existing contour lines, and ensuring that all contour lines are smooth space curves;
The three-dimensional contour line model has complete coordinate information, can be converted into a digital elevation model and an irregular triangular net under any coordinate reference, supports seamless fusion processing with other point cloud data, digital elevation models and digital orthographic images, can be used for generating a high-precision three-dimensional GIS model, has good application value in the aspects of calculation of earth and stone, construction channel line selection, integration of BIM models and the like, and realizes the diversified application of topographic data.
Compared with the prior art, the method has the advantages that the unmanned aerial vehicle aerial survey method is adopted to establish dense point cloud data, the point cloud filtering classification method is adopted to extract ground characteristic points, the point cloud reverse engineering method is converted into the three-dimensional grid model, the grid space structure is optimized through grid hole modification and grid interpolation, contour parameters are set according to the scale requirements, three-dimensional smooth contour is established, and the accuracy can reach within 5 cm. The contour line can be directly converted into a TIN triangular net and a Digital Elevation Model (DEM), dense point cloud data can be directly used for earth and stone side calculation, a three-dimensional GIS model is established, the digital interactivity effect is good, the method is suitable for any stage of construction and production, the social benefit and the economic benefit are obvious, important guiding significance and popularization value are achieved, and meanwhile the method has the advantages of being high in terrain precision, less in manual intervention, good in full digital expression effect, high in digital handover rate, high in working efficiency and the like.
Drawings
FIG. 1 is a classification diagram of ground point cloud filtering;
FIG. 2 is a cross-sectional view of a ground point cloud filtering classification;
FIG. 3 is a triangular mesh hole diagram;
FIG. 4 is a triangular mesh hole trim diagram;
Fig. 5 is a three-dimensional contour diagram.
Detailed Description
For the purpose of promoting an understanding of the principles of the invention, reference will now be made in detail to the embodiments described herein, including examples, illustrated in the accompanying drawings.
Examples
Referring to fig. 1 to 5, the embodiment provides an unmanned aerial vehicle naked eye 3D full digital mapping method, and the operation key points are as follows:
1) Aerial photo collection and processing
In order to improve aerial survey precision, a portable navigation mark is used as an image control point, 4-6 points are distributed around a measuring area, the coordinate of the central point of the navigation mark is measured by using RTK, and the coordinate is converted into a WGS84-UTM coordinate system.
The flight parameters are set on the flight rule software, in order to ensure the flight safety and quality, the flight task adopts a grid or circumferential flight mode, the height is controlled within 120m, the heading and lateral re-attachment rate is 80%, the camera inclination angle is 15 degrees, and the flight sampling time is 2s.
Copying the unmanned aerial vehicle image onto a computer, selecting any aerial photo to adjust the image exposure parameters, recording the parameter values of the image exposure parameters, carrying out batch processing on all aerial photos, unifying the resolution, the exposure intensity and the color of all aerial photos, improving the effect of aerial triangulation calculation, and ensuring the texture definition of the unmanned aerial vehicle real-scene model.
In the aerial survey data processing software, a new project is created, unmanned aerial vehicle image data and phase control point coordinates are imported, a coordinate zone number of a measuring zone is calculated by adopting a formula Int (L/6) +31, L is the longitude of the measuring zone, and a coordinate system is found correspondingly in the northern hemisphere. In order to improve the data processing effect of the measuring area, a KML file of the data volume range of the measuring area is downloaded in bigmap map software and added into aerial survey data processing software, so that an aerial photo processing range is defined, an aerial survey data processing flow is established, after the accuracy of a control point is checked to be qualified, the sampling interval of point clouds is set to be 1cm, high-accuracy dense point cloud data is produced, and a las format is selected for point cloud output.
2) Point cloud filtering classification
And importing the navigation measurement point cloud into the point cloud data software, defining the initial point cloud as an unclassified state in a point cloud classification module, and classifying the ground points by adopting a mode of combining a parameter filtering classification method with a manual classification method.
And (5) parameter filtering classification. Firstly, extracting ground point clouds by adopting parameter filtering classification, selecting all point cloud data by a frame, selecting a ground classification function, setting point cloud filtering parameters, and setting points and filtering parameters under different topography ranges as follows:
table 1: point cloud filtering classification parameter table for different topography
| Topography of the ground | Building size | Ground angle | Iterative angle | Ground distance | Iterative side length |
| Plain area | < Maximum building projection Length | <30° | 4°～6° | <1m | 1m～2m |
| Hilly areas | < Maximum building projection Length | 30°～60° | 6°～12° | 1m～1.5m | 2m～3m |
| Mountain area | < Maximum building projection Length | 60°～75° | 12°～20° | 1m～1.5m | 3m～5m |
And (5) optimizing filtering. Because the point cloud data of the building area and the compact area are not very accurate, after the automatic classification is finished, the abnormal classification areas are defined one by adopting manual classification matching, the 5 filtering parameters are adjusted, and the point cloud is optimized until the satisfactory ground point cloud is obtained. For the region with dense building bodies, the range of the building bodies is directly defined for deleting, and only the ground point cloud is required to be reserved.
The point cloud is smoothed. In order to eliminate the expression of redundant noise point clouds on ground fluctuation, well-classified ground point clouds are selected, the maximum restoration value of the point clouds is set to be 0.1m, the contraction radius is 0.2m, the smoothing type is three-dimensional coordinate value XYZ, and the fineness and quality of the subsequent three-dimensional grid establishment are ensured.
3) Grid surface creation
And (5) building a grid. And setting a proper point cloud sampling interval under the grid module, converting the point cloud data into a three-dimensional grid model, and in order to obtain the finest grid model, as the internal holes and the external holes exist in the grid reconstructed by the point cloud, the obtained three-dimensional contour lines are discontinuous when digital mapping is performed, so that the expression of the topographic data is influenced.
And (5) modifying the inner holes. Aiming at filling grid holes, manual hole filling and automatic hole filling are combined, automatic hole filling is adopted for the internal holes, hole lengths are set, the internal holes are selected completely, the smoothness of the holes is adjusted according to the on-site topography, and the filling of the internal holes is realized.
And (5) external hole modification. For filling the external holes, two characteristic points or characteristic lines are selected at the edge, a new boundary is created through a bridging method, the original external holes are divided into a new external hole and an internal hole, and then the new external holes are filled by adopting an internal hole filling method.
4) Triangle net interpolation process
A large number of sharp corners exist in the triangular net, and the triangular net is not in accordance with the three characteristics of the Digital Elevation Model (DEM) such as convexity retention, reality and smoothness, the triangular net is optimized by adopting a distance square reciprocal weighted spatial interpolation method, the slope points are calculated by utilizing the spatial position relation between the points on the slope and the points under the slope through the interpolation method, and then the slope points are connected with the points on the periphery again to construct the triangular net, so that the spatial arrangement structure of the triangular net is optimized, the fitting degree of the surface of the triangular net and the actual ground is higher, and the fluctuation form of the space is better embodied.
5) Parameterized mapping
Based on the interpolation grid, setting the distance n of common contour lines and the distance m of main contour lines according to the requirements of digital mapping scales, wherein m=5n, and the contour line parameters of different scales are detailed in the following table:
Table 2: equal-altitude parameter values of different scales
| Proportional scale | 1:500 | 1:1000 | 1:5000 | 1:10000 | 1:50000 |
| Constant-altitude distance | 0.5m | 1m | 2.5m | 5m | 10m |
| Major constant-altitude distance | 2.5m | 5m | 12.5m | 25m | 50m |
In order to improve that the contour lines are smooth curves, a space spline curve fitting method is adopted to optimize broken lines in the existing contour lines, so that all contour lines are ensured to be smooth space curves.
The three-dimensional contour line model has complete coordinate information, can be converted into any coordinate reference, such as CGCS2000, WGS84 and the like, can also be converted into a Digital Elevation Model (DEM), an irregular triangular net (TIN), supports seamless fusion processing with other point cloud data, a Digital Elevation Model (DEM), a Digital Orthophoto (DOM) and the like, can also be used for generating a high-precision three-dimensional GIS model, has good application value in the aspects of earth and stone calculation, construction channel selection, BIM integration and the like, and realizes the application of diversified topographic data.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (2)
1. The unmanned aerial vehicle naked eye 3D full-digital mapping method is characterized by comprising the following specific steps of:
1) Aerial photo collection and processing
Using a portable navigation mark as an image control point, arranging 4-6 points around a measuring area, measuring the coordinate of a navigation mark center point by using RTK, and converting the coordinate into a WGS84-UTM coordinate system;
Copying the unmanned aerial vehicle image onto a computer, selecting any aerial photo to adjust the image exposure parameters, recording the parameter values of the aerial photo, carrying out batch processing on all aerial photos, and unifying the resolution, the exposure intensity and the color of all aerial photos;
In the aerial survey data processing software, newly-built engineering, unmanned aerial vehicle image data and phase control point coordinates are imported, a coordinate zone number of a measuring zone is calculated by adopting a formula Int (L/6) +31, L is a measuring zone longitude, a coordinate system is found correspondingly in a northern hemisphere, in bigmap map software, a measuring zone data volume range KML file is downloaded and added into the aerial survey data processing software, so that an aerial photograph processing range is defined, an aerial survey data processing flow is established, after the accuracy of a control point is checked to be qualified, the sampling interval of point clouds is set to be 1cm, high-accuracy dense point cloud data is produced, and a las format is selected for point cloud output;
2) Classifying point cloud filtering;
Importing a navigation measurement point cloud in point cloud data software, defining an initial point cloud as an unclassified state in a point cloud classification module, and classifying ground points by adopting a mode of combining a parameter filtering classification method with a manual classification method;
3) Establishing a grid curved surface;
Establishing a grid, setting a proper point cloud sampling interval under a grid module, and converting point cloud data into a three-dimensional grid model;
The internal hole modification, aiming at filling the grid holes, adopts the combination of manual hole filling and automatic hole filling, mainly adopts automatic hole filling, sets the hole length, fully selects the internal holes, and adjusts the smoothness rate of the holes by combining the site topography condition so as to realize filling of the internal holes;
External hole modification, namely selecting two characteristic points or characteristic lines at the edge for filling the external hole, creating a new boundary by a bridging method, dividing the original external hole into a new external hole and an internal hole, and filling the new external hole and the internal hole by adopting an internal hole filling method;
4) Triangle net interpolation process
Optimizing by adopting a triangular network distance square reciprocal weighted spatial interpolation method, calculating slope points by using a spatial position relation between points on a slope and points under the slope through an interpolation method, and reconnecting the slope points with surrounding points to construct a triangular network;
5) Parameterized mapping
Based on the interpolation grid, setting the distance n of common contour lines and the distance m of main contour lines according to the requirements of digital mapping scales, wherein m=5n, and the contour line parameters of different scales are detailed in the following table:
table 1: equal-altitude parameter values of different scales
And (3) adopting a space spline curve fitting method to optimize broken lines in the existing contour lines, and ensuring that all contour lines are smooth space curves.
2. The unmanned aerial vehicle naked eye 3D full digital mapping method according to claim 1, wherein in the step 2), the point cloud filtering classification is specifically as follows:
the method comprises the steps of parameter filtering classification, firstly extracting ground point cloud by adopting parameter filtering classification, framing all point cloud data, selecting a ground classification function, setting point cloud filtering parameters, and setting points and filtering parameters under different topography ranges as shown in the following table:
Table 2: point cloud filtering classification parameter table for different topography
After the automatic classification is finished, manual classification matching is needed to be adopted, classification abnormal areas are defined one by one, the 5 filtering parameters are adjusted, the point cloud is optimized until satisfactory ground point cloud is obtained, the range of a building body is directly defined for deleting the area where the building body is dense, and the ground point cloud is reserved;
And (3) smoothing the point cloud, selecting the classified ground point cloud, setting the maximum restoration value of the point cloud to be 0.1m, the shrinkage radius to be 0.2m, and the smoothing type to be the three-dimensional coordinate value XYZ, so as to ensure the fineness and quality of the subsequent three-dimensional grid establishment.
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