CN115019007A - Three-dimensional model making method and system based on unmanned aerial vehicle intelligent air route planning - Google Patents
Three-dimensional model making method and system based on unmanned aerial vehicle intelligent air route planning Download PDFInfo
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Abstract
The invention belongs to the technical field of three-dimensional modeling, and discloses a three-dimensional model manufacturing method and a three-dimensional model manufacturing system based on unmanned aerial vehicle intelligent route planning, wherein the manufacturing method comprises the following steps: s1, point cloud data of the objects on the ground are obtained; s2, acquiring aerial image data of the ground object and extracting graph contour data of the ground object; s3, aligning the graphic outline data of the object on the ground with the point cloud data of the object on the ground; s4, respectively generating a coordinate point feature distribution map about the point group data based on S3, and dividing the point group data into different categories; s5, completing three-dimensional modeling of the city according to the classification result of different point group data in S4; the method is used for establishing the urban three-dimensional model, solves the technical problems of large amount of manual participation, complex modeling steps and low modeling efficiency in the modeling process, and has the advantages of automatic modeling, simple modeling steps and high modeling efficiency.
Description
Technical Field
The invention belongs to the technical field of three-dimensional modeling, and particularly relates to a three-dimensional model manufacturing method and system based on unmanned aerial vehicle intelligent route planning.
Background
As mapping techniques and three-dimensional modeling techniques continue to mature, the need for modeling has stayed not only for three-dimensional modeling of individual urban buildings, more and more demands for three-dimensional modeling of buildings in a certain urban area or in a whole city appear, and a three-dimensional city model has wide application, for example, city planning can be more conveniently developed through the three-dimensional city model, and a real game scene can be created by utilizing the three-dimensional city model to increase game experience, in the prior art, for the modeling process of the three-dimensional city model, generally according to image data obtained by unmanned aerial vehicle photography, the method is characterized in that a three-dimensional model of the city is built on a computer, and manual participation is often needed to select object types to be modeled, so that the problems that a large amount of manpower is consumed in the three-dimensional modeling process of the city, the modeling steps are complex, and the modeling efficiency is low are caused.
Disclosure of Invention
In order to solve the technical problems, the invention provides a three-dimensional model manufacturing method based on unmanned aerial vehicle intelligent route planning, and aims to improve the modeling process of a three-dimensional city model in the prior art.
In order to achieve the above object, the following method for making a three-dimensional model based on an intelligent route planning of an unmanned aerial vehicle is provided, and specifically the method is implemented by the following steps:
acquiring point cloud data about an object on the ground by using a laser radar scanning technology, wherein the object on the ground comprises urban buildings with obvious height, trees and urban terrain data without the obvious height;
acquiring aerial image data of an object on the ground by using an aerial technology, extracting figure outline data of the object on the ground from the aerial image data, wherein the figure outline comprises a rectangle and a circle, and then obtaining geographic positions corresponding to different pixels on the figure outline and a geographic position corresponding to a central pixel of the figure outline by depending on a geographic registration technology, wherein the geographic position is specifically represented by latitude and longitude;
step three, aligning the graph outline data about the on-ground object obtained in the step two with the point cloud data about the on-ground object obtained in the step one to preliminarily divide the point cloud data, wherein the aligning is realized by the following specific method:
for a rectangular graph profile, dividing coordinate points of which the X coordinate values and the Y coordinate values of the coordinate points in the point cloud data meet a first formula into the same point group, wherein the first formula is
Wherein X and Y are X coordinate value and Y coordinate value of coordinate point in the point cloud data respectively,
，
respectively the minimum value and the maximum value of the geographic longitude corresponding to different image elements on the figure outline,
，
respectively the minimum value and the maximum value of the geographic latitude corresponding to different pixels on the graph outline;
for a circular graph contour, dividing coordinate points of which the X coordinate values and the Y coordinate values of the coordinate points in the point cloud data meet a second formula into the same point group, wherein the second formula is
，
Wherein X and Y are X coordinate value and Y coordinate value of coordinate point in the point cloud data respectively,
，
respectively the geographic longitude and latitude values corresponding to the central pixel of the figure outline,
，
the geographic longitude and latitude values corresponding to any pixel on the figure outline are respectively;
step four, respectively generating a coordinate point feature distribution map related to the coordinate point group data based on the different point group data of the coordinate points in the point cloud data obtained in the step three, and dividing the different point group data of the coordinate points in the point cloud data into a building roof type, a tree type and a terrain type according to the graph features of the coordinate point feature distribution map;
and step five, according to the classification result of different point group data of the coordinate points in the point cloud data in the step four, respectively constructing a spatial triangular net of the coordinate points in the point group data for the different types of point group data, and further completing three-dimensional modeling of buildings, trees and terrains.
Compared with the prior art, the invention has the following beneficial effects:
the invention relates to a three-dimensional model making method based on unmanned aerial vehicle intelligent route planning, which comprises the steps of firstly obtaining point cloud data about objects on the ground, then acquiring aerial image data of the objects on the ground, extracting graph outline data of the objects on the ground from the aerial image data, calculating the geographic positions corresponding to different pixel elements on the graph outline and the geographic position corresponding to a central pixel element of the graph outline, then, the graphic profile data of the objects on the ground and the point cloud data of the objects on the ground are aligned, then a coordinate point feature distribution graph of the point group data is respectively generated based on different point group data of the coordinate points in the point cloud data, and the point group data is divided into different categories, and finally, modeling of different objects on the ground is completed according to the classification result of different point group data of the coordinate points in the point cloud data. The method has the advantages of simple modeling steps, high modeling efficiency and no need of manual participation, and solves the problems that in the prior art, for the modeling process of the city, manual participation is often needed to select the class of an object to be modeled, and the like, so that a large amount of manpower is consumed in the three-dimensional modeling process of the city, the modeling steps are complex, and the modeling efficiency is low.
Drawings
FIG. 1 is a flow chart of a method for making a three-dimensional model based on intelligent course planning of an unmanned aerial vehicle according to the present invention;
FIG. 2 is a flow chart of step four of the manufacturing method of the present invention;
FIG. 3 is a block diagram of a three-dimensional model building system based on unmanned aerial vehicle intelligent route planning according to the present invention;
FIG. 4 is a block diagram of a third module of the manufacturing system of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of the present application.
Referring to fig. 1, the invention provides a method for making a three-dimensional model based on unmanned aerial vehicle intelligent route planning, which is specifically realized by executing the following steps:
the method comprises the steps of firstly, acquiring point cloud data of objects on the ground by using a laser radar scanning technology, wherein the objects on the ground comprise urban buildings with obvious height, trees and urban terrain data without the obvious height.
Acquiring aerial image data of the ground object by using an aerial technology, extracting figure outline data of the ground object from the aerial image data, wherein the figure outline comprises a rectangle and a circle, and then obtaining geographic positions corresponding to different pixels on the figure outline and a geographic position corresponding to a central pixel of the figure outline by means of a geographic registration technology, wherein the geographic position is specifically represented by latitude and longitude.
And step three, aligning the graph outline data about the objects on the ground obtained in the step two with the point cloud data about the objects on the ground obtained in the step one to preliminarily divide the point cloud data.
And fourthly, respectively generating a coordinate point feature distribution map related to the coordinate point group data based on the different point group data of the coordinate points in the point cloud data obtained in the third step, and dividing the different point group data of the coordinate points in the point cloud data into a building roof type, a tree type and a terrain type according to the graph feature of the coordinate point feature distribution map.
And step five, according to the classification result of different point group data of the coordinate points in the point cloud data in the step four, respectively constructing a spatial triangular net of the coordinate points in the point group data for the different types of point group data, and further completing three-dimensional modeling of buildings, trees and terrains.
Further, the point cloud data about the ground object obtained in the first step is obtained, wherein the X coordinate value and the Y coordinate value of different coordinate points in the point cloud data are respectively represented by longitude and latitude, the X coordinate value and the Y coordinate value jointly represent the geographical positions of the different coordinate points in the point cloud data, and the Z coordinate value of the different coordinate points in the point cloud data represents the height value of the geographical positions of the coordinate points.
Specifically, the invention obtains point cloud data about objects on the ground by using a laser radar system, the laser radar system comprises a laser and a receiver, the laser generates and emits a beam of light pulse, the light pulse is hit on the objects and reflected back, and finally the light pulse is received by the receiver, the receiver can accurately measure the propagation time of the light pulse from emission to reflection, the propagation time can be converted into a distance value considering that the light speed is known, and three-dimensional coordinates of each scanned point on the ground, namely point cloud data about the objects on the ground can be accurately calculated by combining the height of the laser and the angle of laser scanning, the point cloud data describes the top shape of urban buildings with obvious height on the ground, the top shape of trees with obvious height, the top shape of urban terrain without obvious height, and the like, therefore, in the subsequent steps, the point cloud data is mainly used for carrying out three-dimensional modeling on buildings, trees and terrains, and modeling on other objects on the ground such as vehicles, pedestrians and the like is not considered in the invention, so that the modeling steps are simplified, and the modeling speed is improved.
Further, the second step includes, after extracting the figure outline data about the ground object from the aerial image data, acquiring and storing the aerial image data within a range defined by the figure outline.
Specifically, the second step of the invention is to first obtain a high-precision aerial image of the object on the ground, the high-precision aerial image comprises the information of the color and shape of the buildings, trees and urban terrain on the urban ground, then to perform binarization processing on the high-precision aerial image, and to use the edge detection algorithm, further to extract the graphic profile of the object on the ground from the high-precision aerial image into rectangle and circle approximately, and finally to obtain the geographic positions expressed by longitude and latitude corresponding to different pixels on the graphic profile and the geographic position expressed by longitude and latitude corresponding to the central pixel of the graphic profile by means of geographic registration technology, which is to connect the position of each point in the grid data set with the position of the known geographic coordinate point in the standard spatial reference system by establishing a mathematical function, the geographic coordinate of any pixel in the image is determined, the geographic registration technology is not repeated any more, and the method is executed for conveniently dividing the point cloud data in the subsequent steps, eliminating noise data in the point cloud data and improving the modeling efficiency;
the system comprises an unmanned aerial vehicle, a laser radar system, a camera system, a target modeling area, a flying route, a flying angle, a flying height and the like, wherein the point cloud data of the above-ground object is obtained by scanning the target modeling area through the unmanned aerial vehicle carrying the laser radar system, the aerial image data of the above-ground object is obtained by shooting the target modeling area through the unmanned aerial vehicle carrying the camera system, and the unmanned aerial vehicle plans the flying route, the flying angle, the flying height and the like in advance according to the target modeling area so as to obtain high-precision point cloud data and aerial image data.
Further, the alignment of the graphic outline and the point cloud data in the third step is specifically realized by the following means:
for a rectangular graph contour, dividing coordinate points of which the X coordinate values and the Y coordinate values of the coordinate points in the point cloud data meet a first formula into the same point group, wherein the first formula is
Wherein X and Y are X coordinate value and Y coordinate value of coordinate point in the point cloud data respectively,
，
respectively the minimum value and the maximum value of the geographic longitude corresponding to different image elements on the figure outline,
，
are respectively paired with different image elements on the figure outlineMinimum and maximum corresponding geographic latitudes;
for a circular figure outline, dividing coordinate points of which the X coordinate values and the Y coordinate values of the coordinate points in the point cloud data meet a second formula into the same point group, wherein the second formula is
，
Wherein X and Y are X coordinate value and Y coordinate value of coordinate point in the point cloud data respectively,
，
respectively the geographic longitude and latitude values corresponding to the central pixel of the figure outline,
，
respectively the geographic longitude and latitude values corresponding to any image element on the figure outline.
Specifically, the alignment processing is to match the graphic profile of the object on the ground on the aerial image with the corresponding coordinate point in the point cloud data, but not match any graphic profile with the coordinate point in the point cloud data.
Further, referring to fig. 2, the fourth step in the present invention specifically includes the following steps:
firstly, establishing a horizontal axis by using a Z coordinate value which is a height value of a coordinate point in the point group data, and establishing a vertical axis by using a frequency of the height value of the coordinate point appearing in the point group data, so as to establish a characteristic distribution diagram of the coordinate point related to the point group data;
secondly, according to the graph characteristics of the coordinate point characteristic distribution graph, dividing different point group data into different categories respectively, and specifically, dividing the point group data corresponding to the distribution graph with the graph characteristics of the coordinate point, namely the distribution graph with the centralized distribution of the height values of the coordinate points and the rapid increase of the frequency values on the different height values into the category of the roof of the building;
thirdly, according to the graph characteristics of the coordinate point characteristic distribution graph, dividing different point group data into different categories respectively, and particularly dividing the point group data corresponding to the distribution graph with the graph characteristics that the height values of the coordinate points are distributed in a relatively dispersed manner and the frequency values on the different height values are slowly increased into tree categories;
and fourthly, according to the graph characteristics of the coordinate point characteristic distribution graph, dividing different point group data into different categories respectively, and specifically, dividing the point group data corresponding to the distribution graph with the graph characteristics that the dispersion degree of the height values of the coordinate points is large and the frequency values on the different height values are relatively gentle into terrain categories.
Specifically, considering that each coordinate point in the point cloud data regarding the on-ground object, the height values of the coordinate points are different, and the coordinate points are gathered according to the shape of the object, a coordinate point characteristic distribution graph of different point group data can be established based on the coordinate points in the point group data obtained in the third step, the different graphical features of the coordinate point feature map represent different objects on the ground, such as building roofs, trees, terrain, therefore, the classification of the object represented by the different point group data can be carried out, the object class represented by the point group data is determined, the three-dimensional modeling of different objects by the system is convenient, the model is corrected, for example, the point group data is classified into the roof of a building, rather than the top of the tree, the system can continue to automatically generate the graph of the building side after modeling of the building roof is completed.
Further, in the fifth step, a spatial triangulation network of coordinate points in different types of point group data is respectively constructed, and then three-dimensional modeling of buildings, trees and terrains is completed, and mapping processing is further performed on the corresponding three-dimensional models of the buildings, the trees and the terrains by using aerial image data within a range specified by graphic profile data of objects on the ground.
Specifically, the three-dimensional modeling of the object can be completed by constructing the spatial triangulation network based on the coordinate points in the point cloud data, and as the technology for performing three-dimensional reconstruction according to the point cloud data is relatively mature, the detailed modeling process is not repeated in the invention, and in addition, the aerial image data in the range specified by the graphic profile data of the object on the ground is used for carrying out mapping processing on the three-dimensional models of the corresponding buildings, trees and terrains, so that the sense of reality of the models is increased.
Referring to fig. 3, the present invention further provides a three-dimensional model making system based on intelligent route planning of an unmanned aerial vehicle, which is used for implementing the above-described three-dimensional model making method based on intelligent route planning of an unmanned aerial vehicle, and specifically includes the following modules:
a first module for acquiring point cloud data about an above-ground object including urban buildings of significant height, trees, and urban terrain data not having significant height by using a laser radar scanning technique;
the second module is used for acquiring aerial image data of an object on the ground by using an aerial technology, extracting figure outline data of the object on the ground from the aerial image data, wherein the figure outline comprises a rectangle and a circle, and is also used for obtaining geographic positions corresponding to different pixels on the figure outline and a geographic position corresponding to a central pixel of the figure outline by depending on a geographic registration technology, and the geographic position is specifically represented by longitude and latitude;
a third module, configured to align the graph profile data about the above-ground object obtained in the second module with the point cloud data about the above-ground object obtained in the first module, so as to perform preliminary division on the point cloud data;
the fourth module is used for respectively generating a coordinate point feature distribution map related to the point group data based on different point group data of the coordinate points in the point cloud data obtained in the third module, and is also used for dividing the different point group data of the coordinate points in the point cloud data into a building roof category, a tree category and a terrain category according to the graph features of the coordinate point feature distribution map;
and the fifth module is used for respectively constructing a spatial triangular network of the coordinate points in the point group data according to the classification result of different point group data of the coordinate points in the point cloud data in the fourth module and different types of point group data, so as to complete three-dimensional modeling of buildings, trees and terrains.
Referring to fig. 4, the third module in the three-dimensional model making system based on the unmanned aerial vehicle intelligent route planning of the present invention specifically further includes the following units:
a first unit configured to divide, for a rectangular graph contour, coordinate points in which an X coordinate value and a Y coordinate value of a coordinate point in point cloud data satisfy a formula one into the same point group;
and a second unit for dividing coordinate points in the point cloud data, for which the X-coordinate values and the Y-coordinate values of the coordinate points satisfy a formula two, into the same point group with respect to the circular figure contour.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least a portion of sub-steps or stages of other steps.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is specific and detailed, but not to be understood as limiting the scope of the present invention. It should be noted that various changes and modifications can be made by those skilled in the art without departing from the spirit of the invention, and these changes and modifications are all within the scope of the invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. A three-dimensional model manufacturing method based on unmanned aerial vehicle intelligent air route planning is characterized by comprising the following steps:
s1, acquiring point cloud data about objects on the ground by using a laser radar scanning technology, wherein the objects on the ground comprise urban buildings with obvious height, trees and urban terrain data without obvious height;
s2, acquiring aerial image data of the ground object by using an aerial technology, extracting graph outline data of the ground object from the aerial image data, wherein the graph outline comprises a rectangle and a circle, and then obtaining geographic positions corresponding to different pixels on the graph outline and a geographic position corresponding to a central pixel of the graph outline by means of a geographic registration technology, wherein the geographic position is specifically represented by latitude and longitude;
s3, performing an alignment process on the contour data of the graph about the ground object obtained in S2 and the point cloud data about the ground object obtained in S1 to perform a preliminary division on the point cloud data, wherein the alignment process is specifically implemented as follows:
for a rectangular graph contour, dividing coordinate points of which the X coordinate values and the Y coordinate values of the coordinate points in the point cloud data meet a first formula into the same point group, wherein the first formula is
Wherein X and Y are X coordinate value and Y coordinate value of coordinate point in the point cloud data respectively,
，
respectively the minimum value and the maximum value of the geographic longitude corresponding to different image elements on the figure outline,
，
respectively the minimum value and the maximum value of the geographic latitude corresponding to different pixels on the graph outline;
for a circular graph contour, dividing coordinate points of which the X coordinate values and the Y coordinate values of the coordinate points in the point cloud data meet a second formula into the same point group, wherein the second formula is
，
Wherein X and Y are X coordinate value and Y coordinate value of coordinate point in the point cloud data respectively,
，
respectively the geographic longitude and latitude values corresponding to the central pixel of the figure outline,
，
the geographic longitude and latitude values corresponding to any pixel on the figure outline are respectively;
s4, respectively generating a coordinate point feature distribution map related to the point group data based on different point group data of the coordinate points in the point cloud data obtained in S3, and dividing the different point group data of the coordinate points in the point cloud data into a building roof type, a tree type and a terrain type according to the graphic features of the coordinate point feature distribution map;
and S5, respectively constructing a spatial triangular network of the coordinate points in the point group data according to the classification result of different point group data of the coordinate points in the point cloud data in S4, and further completing three-dimensional modeling of buildings, trees and terrains.
2. The method of claim 1, wherein the point cloud data about the above-ground objects obtained in step S1 is obtained, wherein the X coordinate value and the Y coordinate value of each coordinate point in the point cloud data are respectively represented by longitude and latitude, the X coordinate value and the Y coordinate value together represent the geographic location of each coordinate point in the point cloud data, and the Z coordinate value of each coordinate point in the point cloud data represents the height of the geographic location of the coordinate point.
3. The method of claim 1, wherein after the step of extracting the data of the figure outline of the object on the ground from the aerial image data in step S2, the step of obtaining and storing the aerial image data within the range defined by the figure outline is further included.
4. The method for making the three-dimensional model based on the intelligent route planning of the unmanned aerial vehicle as claimed in claim 1, wherein the S4 specifically comprises the following steps:
s41, establishing a horizontal axis according to the height value of the coordinate point in the point group data, namely the Z coordinate value, establishing a vertical axis according to the frequency of the height value of the coordinate point appearing in the point group data, and further establishing a coordinate point characteristic distribution diagram related to the point group data;
s42, according to the graph characteristics of the coordinate point characteristic distribution graph, dividing different point group data into different categories, and specifically dividing the point group data corresponding to the distribution graph with the graph characteristics of the coordinate point, namely the distribution graph with the centralized distribution of the height values of the coordinate points and the rapid increase of the frequency values on the different height values into the category of the roof of the building;
s43, according to the graph characteristics of the coordinate point characteristic distribution graph, dividing different point group data into different categories, and specifically dividing the point group data corresponding to the distribution graph with the graph characteristics that the height values of the coordinate points are distributed in a relatively dispersed manner and the frequency values on the different height values are slowly increased into tree categories;
and S44, according to the graph characteristics of the coordinate point characteristic distribution graph, dividing different point group data into different categories respectively, and specifically, dividing the point group data corresponding to the distribution graph with the graph characteristics that the distribution graph has large dispersion degree of the height values of the coordinate points and relatively gentle frequency values on different height values into terrain categories.
5. The method of claim 1, wherein after the step S5 of constructing a spatial triangulation network of coordinate points in the different sets of data of points, respectively, and completing three-dimensional modeling of buildings, trees, and terrain, the method further comprises the step of mapping the three-dimensional model of buildings, trees, and terrain using aerial image data within a range specified by the graphic profile data of objects on the ground.
6. A three-dimensional modeling system based on unmanned aerial vehicle intelligent route planning, which is used for realizing the method of any one of claims 1-5, and is characterized by comprising the following modules:
a first module for obtaining point cloud data about an above-ground object by using laser radar scanning techniques, the above-ground object comprising urban buildings having significant height, trees, and urban terrain data not having significant height;
the second module is used for acquiring aerial image data of an object on the ground by using an aerial technology, extracting figure outline data of the object on the ground from the aerial image data, wherein the figure outline comprises a rectangle and a circle, and is also used for obtaining geographic positions corresponding to different pixels on the figure outline and a geographic position corresponding to a central pixel of the figure outline by depending on a geographic registration technology, and the geographic position is specifically represented by longitude and latitude;
a third module, configured to align the graphic profile data about the terrestrial object obtained in the second module with the point cloud data about the terrestrial object obtained in the first module, so as to perform preliminary division on the point cloud data, and specifically include the following units:
a first unit configured to divide, for a rectangular graph contour, coordinate points in which an X coordinate value and a Y coordinate value of a coordinate point in point cloud data satisfy a formula one into the same point group;
a second unit for dividing coordinate points, in which the X-coordinate values and the Y-coordinate values of the coordinate points in the point cloud data satisfy a formula two, into the same point group with respect to the circular figure profile;
the fourth module is used for respectively generating a coordinate point feature distribution map related to the point group data based on different point group data of the coordinate points in the point cloud data obtained in the third module, and is also used for dividing the different point group data of the coordinate points in the point cloud data into a building roof category, a tree category and a terrain category according to the graph features of the coordinate point feature distribution map;
and the fifth module is used for respectively constructing a spatial triangular network of the coordinate points in the point group data according to the classification result of different point group data of the coordinate points in the point cloud data in the fourth module and different types of point group data, so as to complete three-dimensional modeling of buildings, trees and terrains.
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| CN117541740A (en) * | 2024-01-09 | 2024-02-09 | 广东广宇科技发展有限公司 | River and lake region three-dimensional modeling method and system based on unmanned aerial vehicle data acquisition |
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