CN117387580B - Mapping method and system based on oblique photography large-scale topographic map - Google Patents
Mapping method and system based on oblique photography large-scale topographic map Download PDFInfo
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
- G01C11/04—Interpretation of pictures
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract
The invention discloses a mapping method and a mapping system based on a large-scale topographic map of oblique photography, and relates to the technical field of topographic map mapping; the technical key points are as follows: carrying out cooperation operation by utilizing a partition evaluation module and a comparison execution module, carrying out partition detection on the three-dimensional model, comprehensively considering all data factors under the corresponding partition to obtain a value capable of preliminarily reflecting the mapping graph precision of the corresponding partition, comparing with an evaluation threshold mol, screening out the partition which does not meet the mapping requirement according to the comparison result, and carrying out targeted secondary measurement and calculation on the partition of the department until the image precision evaluation value of the corresponding partitionThe requirements are met, the overall quality of the topographic map after mapping is improved, and the efficiency of mapping work is quickened.
Description
Technical Field
The invention relates to the technical field of topographic map mapping, in particular to a mapping method and system based on oblique photography large scale topographic map.
Background
Topographic map mapping, which is a process of measuring and recording the shape, elevation and other geographical features of the ground and then plotting them on a map, provides an accurate topographic description for people, including mountains, rivers, lakes, forests, roads, buildings, etc., and usually uses various measurement techniques, such as measuring instruments, satellite remote sensing, laser rangefinders, etc., to obtain accurate ground data, which after being processed and analyzed, can be used to make topographic maps for use in various industries and fields, such as land planning, disaster management, military combat planning, etc.; in oblique photography, a large scale refers to a proportional relationship between a ground area photographed in photography and one unit length on a photogrammetric camera, in other words, a large scale means that an object on a photographic map is enlarged and more details can be seen, and generally, a map of a large scale is suitable for map applications requiring higher precision and more detailed information, such as fields of city planning, engineering design, and building measurement.
The technical scheme pointed out in China patent of the prior application publication number CN112833861A, named as a mapping method and a mapping system based on oblique photography large scale topographic map, comprises the following steps: collecting data of an area to be measured, and planning a mapping line according to the data; arranging image control points in the region to be measured according to the mapping circuit, and processing the image control points according to a measurement specification, wherein the image control points comprise conventional image control points and special image control points; performing unmanned aerial vehicle oblique photography based on the mapping line, and collecting terrain data of the region to be detected; inputting the topographic data into a modeling platform for three-dimensional modeling processing and synthesizing a large-scale three-dimensional topographic map of the region to be detected; error control and precision inspection are carried out on each step, mapping is reduced by the scheme, so that the precision is improved, but error control is carried out in the mapping process, the measurement precision is guaranteed according to specifications and requirements, the mapping area is required to be comprehensively monitored and controlled, and the workload of mapping operation is greatly increased in the process.
In combination with the above patent and the prior art, the tradition is when utilizing oblique photography technology to carry out map survey and drawing, also all deal with comparatively flat region generally, utilize the unmanned aerial vehicle who carries the camera to carry out the shooting of cruising, only need guarantee that unmanned aerial vehicle and camera's stability and work are normal can accomplish the survey and drawing to the flat region of topography smoothly, avoid unsmooth topography to increase the calculated amount when map survey and drawing, also avoid influencing the survey and drawing precision simultaneously, when accomplishing map survey and drawing's precision calculation to the flat region of topography, if all carry out survey and drawing processing to each point position of whole region, then can increase survey and drawing work load with too fine precision detection mode, thereby reduce the work efficiency who finally accomplishes map survey and drawing.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a mapping method and a mapping system based on a large scale topographic map of oblique photography, after the collected point cloud data is built into a three-dimensional model, mapping of the topographic map of the whole area can be primarily realized, a partition evaluation module and a comparison execution module are utilized to carry out cooperation operation, partition detection is carried out on the three-dimensional model, each data factor under the corresponding partition is comprehensively considered, a value which can not only primarily reflect the mapping graph precision of the corresponding partition is obtained, but also the value can be compared with an evaluation threshold mol, the partition which does not meet the mapping requirement is screened out according to the comparison result, and targeted secondary measurement and calculation are carried out on the partition of the department until the image precision evaluation value of the corresponding partition is obtainedThe requirements are met, the overall quality of the topographic map after mapping is improved, the efficiency of mapping work is quickened, and the problems in the background technology are solved.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
a mapping system based on oblique photography large scale topography, comprising:
the data acquisition module is used for acquiring ground point cloud data of a to-be-painted area captured by an unmanned aerial vehicle equipped with an inclined camera device;
the data processing module is used for preprocessing the acquired point cloud data;
the three-dimensional reconstruction module converts the data into a three-dimensional model through a point cloud registration and density control algorithm according to the point cloud data;
the terrain analysis module is used for carrying out terrain analysis on the three-dimensional model, and obtaining terrain data so as to mark the position corresponding to the three-dimensional model;
the partition evaluation module is used for carrying out halving processing on the area to be mapped to form n partitions, acquiring evaluation parameters of each partition, constructing a data analysis model according to the evaluation parameters subjected to dimensionless processing, and generating image precision evaluation values under the corresponding partitions；
The comparison execution module is used for estimating the image precision under each partition according to the sequence of the corresponding numbers of the partitionsAnd comparing the ground point cloud data strategy with a preset evaluation threshold mol, if the difference value is not more than 10, not responding, if the difference value is more than 10, sending out a first-level early warning signal, and executing the ground point cloud data strategy under the corresponding partition again until not responding.
Further, in the data processing module, preprocessing includes data cleansing, data filtering, data registration, and data sampling.
Further, in the three-dimensional reconstruction module, the specific process of forming the three-dimensional model is as follows:
and (3) point cloud registration: performing initial registration on the preprocessed point cloud data, aligning coordinates of a plurality of point cloud data sets to the same coordinate system, and searching alignment transformation by using a point cloud registration algorithm;
and (3) density control: controlling the density of the registered point cloud data by using a voxel grid algorithm;
three-dimensional reconstruction: and performing three-dimensional reconstruction by using the registered point cloud data subjected to density control, wherein the three-dimensional reconstruction method is a voxel method, dividing the space into voxel grids, and generating a three-dimensional model according to the density and attribute information of the point cloud data in the voxels.
Further, in the terrain analysis module, the terrain data includes ground elevation values, ground slope directions, terrain curvatures, and contours.
Further, in the partition evaluation module, the evaluation parameters include a point cloud sampling density value, an average resolution of data points, and a root mean square error value, and the point cloud sampling density value is obtained by: the point cloud sampling density of different partitions is measured by the number of points per square meter; the average resolution of the data points is obtained by the following steps: and selecting at least three data points under the corresponding subareas, obtaining the resolution of each data point, summing the resolutions of each data point to obtain an average value, wherein the average value is the average resolution of the data points.
Further, the mean square error value is obtained as follows:
s101, acquiring a group of reference data, wherein the reference data is obtained through a networked data source;
s102, acquiring measurement data corresponding to reference data, wherein the measurement data is acquired by an oblique image pickup device;
s103, calculating the difference between the measured value and the reference value for each corresponding measuring point, and obtaining a difference value through subtraction;
s104, squaring each difference value to obtain a square error;
s105, adding all square differences, and dividing the obtained value by the number of measurement points to obtain an average square difference;
and S106, squaring the average square difference to obtain the root mean square error of the corresponding partition.
Further, generating an image precision evaluation value under the corresponding partitionThe formula according to is as follows:
；
in the method, in the process of the invention,representing a point cloud sampling density value,/->Representing the average resolution of the data points +.>Representing root mean square error value, & lt + & gt>Preset proportionality coefficients of point cloud sampling density value, data point average resolution and mean square error value respectively, and +.>G is a constant correction coefficient, t= =>T represents the number of each partition, n represents the number of the corresponding number, i.e. the number of partitions, +.>Is a positive integer.
A mapping method based on oblique photography large scale topographic map comprises the following steps:
step one, acquiring ground point cloud data of a to-be-painted area captured by an unmanned aerial vehicle equipped with an inclined camera device;
step two, preprocessing the obtained point cloud data, including data cleaning, data filtering, data registering and data sampling, and data cleaning: removing noise points, outliers and invalid data; and (3) data filtering: screening the point cloud data according to the need, such as filtering according to density, elevation or color; data registration: registering the plurality of point cloud data sets to align the point cloud data sets under the same coordinate system; and (3) data sampling: downsampling the point cloud data according to the requirements so as to reduce the data quantity and accelerate the subsequent processing speed;
step three, converting the data into a three-dimensional model through a point cloud registration and density control algorithm according to the point cloud data;
and (3) point cloud registration: performing initial registration on the preprocessed point cloud data, aligning coordinates of a plurality of point cloud data sets to the same coordinate system, and searching alignment transformation by using a point cloud registration algorithm;
and (3) density control: controlling the density of the registered point cloud data by using a voxel grid algorithm;
three-dimensional reconstruction: performing three-dimensional reconstruction by using the registered point cloud data subjected to density control, wherein the three-dimensional reconstruction method is a voxel method, dividing a space into voxel grids, and generating a three-dimensional model according to the density and attribute information of the point cloud data in voxels;
step four, performing terrain analysis on the three-dimensional model to obtain terrain data so as to mark the position corresponding to the three-dimensional model;
fifthly, equally dividing the region to be mapped to form n partitions, acquiring evaluation parameters of each partition, building a data analysis model according to the evaluation parameters subjected to dimensionless treatment, and generating image precision evaluation values under the corresponding partitions；
Step six, according to the sequence of the corresponding numbers of the subareas, the image precision evaluation value under each subarea is evaluatedAnd (3) comparing the ground point cloud data strategy with a preset evaluation threshold mol, if the difference value is not more than 10, not responding, if the difference value is more than 10, sending a primary early warning signal, and executing the ground point cloud data strategy under the corresponding partition according to the first step until not responding.
The invention provides a mapping method and a mapping system based on a large-scale topographic map of oblique photography, which have the following beneficial effects:
building the collected point cloud data into three dimensionsAfter the model is obtained, mapping of the topographic map of the whole area can be preliminarily realized, the partition evaluation module and the comparison execution module are utilized to carry out cooperation operation, the three-dimensional model is subjected to partition detection, and each data factor under the corresponding partition is comprehensively considered to obtain a value capable of preliminarily reflecting the accuracy of the mapping graph of the corresponding partition, namely, the image accuracy evaluation valueThe method can also be used for screening out the subareas which do not meet the mapping requirement according to the comparison result by comparing with the evaluation threshold mol, and carrying out targeted secondary measurement and calculation on the subareas of the departments until the image precision evaluation value of the corresponding subareas is +.>The requirements are met, the overall quality of the topographic map after mapping is improved, so that the consistency of the precision of each position of the mapping map is ensured, and the efficiency of mapping work is accelerated to a certain extent.
Drawings
FIG. 1 is a schematic diagram of a modular construction of a mapping system based on oblique photography large scale topography of the present invention;
fig. 2 is a schematic overall flow diagram of a mapping method based on oblique photography large scale topographic map according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1: referring to fig. 1, the present embodiment provides a mapping system based on a large scale topographic map of oblique photography, the system includes a data acquisition module, a data processing module, a three-dimensional reconstruction module, a topographic analysis module, a partition evaluation module and a contrast execution module, and the whole mapping system is applied to a plain area, so that accurate ground or topographic data can be conveniently captured, and the accuracy of the mapping system can be ensured to a certain extent;
the data acquisition module acquires ground point cloud data of a region to be painted, which is captured by an unmanned aerial vehicle equipped with an inclined camera device; in actual operation, unmanned aerial vehicle's flight trajectory covers the region of waiting to survey and drawing to the realization carries out image acquisition to whole survey and drawing region, and the slope camera equipment that is equipped with on the unmanned aerial vehicle includes tilt camera or laser scanner, unmanned aerial vehicle and the point cloud data that acquires all accessible wireless transmission technique, for example 5G communication transmission, in the remote sending to the survey and drawing system.
The data processing module is used for preprocessing the acquired point cloud data, wherein the preprocessing comprises data cleaning, data filtering, data registering and data sampling; wherein, data cleaning: removing noise points, outliers and invalid data; and (3) data filtering: screening the point cloud data according to the need, such as filtering according to density, elevation or color; data registration: registering the plurality of point cloud data sets to align the point cloud data sets under the same coordinate system; and (3) data sampling: and downsampling the point cloud data according to the requirements so as to reduce the data quantity and accelerate the subsequent processing speed.
The three-dimensional reconstruction module converts the preprocessed point cloud data into a three-dimensional model through a point cloud registration and density control algorithm, and the specific process for forming the three-dimensional model is as follows:
and (3) point cloud registration: performing initial registration on the preprocessed point cloud data, aligning coordinates of a plurality of point cloud data sets to the same coordinate system, searching for optimal alignment transformation by using a point cloud registration algorithm (such as ICP (inductively coupled plasma) and feature matching), minimizing errors of corresponding points among the point cloud data sets, and iteratively optimizing a registration process until preset accuracy and stability requirements are met;
and (3) density control: performing density control on the registered point cloud data to control the detail degree and resource consumption of the generated three-dimensional model, controlling the density by using a voxel grid algorithm or a rasterization algorithm, and selectively reserving or discarding the point cloud data by presetting a proper sampling rate, grid size or pixel density so as to achieve the effect of controlling the density;
it should be noted that: the ICP algorithm is used for searching the optimal corresponding relation between two point clouds in an iterative optimization mode so as to realize registration, and the characteristic matching algorithm is used for carrying out characteristic matching between different point clouds by extracting key characteristic points or characteristic descriptors of the point clouds so as to realize registration, wherein the ICP algorithm is actually applied in the application;
the voxel grid algorithm divides a three-dimensional space into voxel grids, and samples according to the density of point clouds in the grids, so as to control the density of a generated three-dimensional model; the rasterization algorithm is to project the point cloud data onto a two-dimensional plane, and then sample according to the pixel density of the projection result so as to control the density of the generated three-dimensional model, and the actual application in the application is voxel grid algorithm;
summarizing, the point cloud registration algorithm is used for accurately aligning a plurality of point cloud data sets, and the density control algorithm is used for controlling the density of the generated three-dimensional model, and the specific selection and implementation manner of the algorithm can be changed according to different application scenes;
three-dimensional reconstruction: performing three-dimensional reconstruction by using the registered point cloud data subjected to density control, wherein the three-dimensional reconstruction method comprises a voxel method or a surface reconstruction method; dividing the space into voxel (three-dimensional pixel) grids by a voxel method, and generating a three-dimensional model according to the density and attribute information of the point cloud data in the voxels; the surface reconstruction method constructs a triangular grid to represent a three-dimensional model according to the geometric properties of the point cloud data points, and the actual selection of the method is a voxel method.
The terrain analysis module performs terrain analysis on the three-dimensional model, acquires terrain data to mark at a position corresponding to the three-dimensional model, for example, marks gradient data in the terrain data on a slope surface of a mountain slope corresponding to the three-dimensional model;
the terrain data comprises a ground elevation value, a ground gradient value, a ground slope direction, a terrain curvature and a contour line;
ground elevation measurement: carrying out elevation measurement on each point in the three-dimensional model, obtaining elevation information of the ground point, and estimating elevation values of non-measurement points by using an interpolation method, such as inverse distance weighted interpolation and kriging interpolation;
and (3) gradient calculation: the gradient is a measure of the gradient inclination degree of the ground surface at a certain point, the gradient is realized by calculating the elevation change of each point, calculating the elevation difference of the points in the neighborhood, and calculating the gradient of each point by utilizing a trigonometric function;
and (3) slope direction calculation: the slope direction is the maximum slope direction of the ground at a certain point, and the slope direction of each point is calculated by calculating the elevation difference of the points in the neighborhood and combining a direction cosine formula;
calculating the topographic curvature: the terrain curvature represents the curvature change degree of the ground, the terrain curvature is estimated by calculating the elevation gradient around each point, and common methods comprise any one of least square fitting and directional derivative methods;
contour line generation: determining a plurality of contour lines according to the measured elevation data by an interpolation method, drawing the contour lines on a three-dimensional model, and generating the contour lines needs to use any one of image adjacent point interpolation and B spline curve interpolation;
the partition evaluation module is used for carrying out halving processing on the area to be mapped to form n partitions, acquiring evaluation parameters of each partition, wherein the evaluation parameters comprise a point cloud sampling density value, a data point average resolution and a root mean square error value, constructing a data analysis model according to the evaluation parameters subjected to dimensionless processing, and generating an image precision evaluation value under the corresponding partition；
The number of n subareas is set according to the situation of actual topography, if the topography is more, more subareas can be arranged, if the topography is flat, fewer subareas can be arranged, and the specific number of the subareas is variable;
the acquisition mode of the point cloud sampling density value is as follows: the point cloud sampling density of different partitions can be measured by the number of points owned per square meter; for example, the density of the point cloud of one partition is 1000 points/square meter, and the density of the other partition is 500 points/square meter, and as the stability and performance of the unmanned aerial vehicle in the flight process cannot be kept constant and are easily influenced by multiparty factors such as environmental transformation, the density of the point cloud corresponding to each partition is not kept consistent, so that the sampling density values of the point clouds of the partitions have a certain degree of deviation;
the average resolution of the data points is obtained by the following steps: selecting at least three data points under the corresponding subareas, obtaining the resolution of each data point, summing the resolutions of each data point, and then obtaining an average value, wherein the average value is the average resolution of the data points, and different resolutions can represent the precision of the corresponding ground image;
the mean square error value is obtained as follows:
s101, acquiring a group of reference data, wherein the reference data is obtained through field measurement, other high-precision topographic data or approved data sources after networking, and for example, the data obtained in map software is the approved data sources;
s102, acquiring measurement data corresponding to reference data, wherein the measurement data is acquired by an oblique image pickup device;
s103, calculating the difference between the measured value and the reference value for each corresponding measuring point, and obtaining a difference value through subtraction;
s104, squaring each difference value to obtain a square error;
s105, adding all square differences, and dividing the obtained value by the number of measurement points to obtain an average square difference;
and S106, squaring the average square difference to obtain the root mean square error of the corresponding partition.
Generating image precision evaluation values under corresponding partitionsThe formula according to is as follows:
；
in the method, in the process of the invention,representing a point cloud sampling density value,/->Representing the average resolution of the data points +.>Representing root mean square error value, & lt + & gt>Preset proportionality coefficients of point cloud sampling density value, data point average resolution and mean square error value respectively, and +.>G is a constant correction coefficient, the specific value of which can be set by user adjustment or generated by fitting an analytical function, t= =>T represents the number of each partition, n represents the number of the corresponding number, i.e. the number of partitions, +.>Is a positive integer;
it should be noted that: the number of each partition is selected, the number is numbered according to a preset sequence from left to right and from top to bottom, and the numbering mode is also a conventional mode, wherein the weighted average calculation is carried out on the point cloud sampling density value, the data point average resolution and the root mean square error value in the formula, and the weighted average calculation is carried out on the point cloud sampling density value, the data point average resolution and the root mean square error value, and the final image precision evaluation valueInversely proportional, therefore, it needs to be designed as +.>The larger the root mean square error value is, the image accuracy evaluation value +.>The smaller the corresponding image accuracy is, the lower the corresponding image accuracy is, and the point cloud sampling density value and the average resolution of the data points are equal to the image accuracy evaluation value +.>In proportion, the larger the point cloud sampling density value and the data point average resolution, the image precision evaluation value +.>The larger the corresponding image accuracy is, the higher the corresponding image accuracy is, the value obtained by the weighted average calculation is multiplied by a constant correction coefficient G, the correction of the value is completed, and the final image accuracy evaluation value is ensured to be obtained +.>Accuracy of (2);
the magnitude of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, the magnitude of the coefficient depends on the number of sample data and the corresponding preset proportional coefficient preliminarily set by a person skilled in the art for each group of sample data, that is, the coefficient is preset according to actual practice, as long as the proportional relation between the parameter and the quantized numerical value is not influenced, and the above description is adopted for the preset proportional coefficient and the constant correction coefficient described in other formulas;
the comparison execution module is used for estimating the image precision under each partition according to the sequence of the corresponding numbers of the partitionsComparing the obtained result with a preset evaluation threshold mol, if the difference value is not more than 10, indicating that the precision of the mapped corresponding regional image meets the requirement, and if the difference value is not more than 10, indicating that the precision of the mapped corresponding regional image does not meet the requirement, sending a first-level early warning signal, and executing a ground point cloud data strategy under the corresponding regional again until the system does not respond; the setting of the difference critical point of 10 is obtained according to actual measurement, and the setting of the difference critical point of 10 is more reasonable for the plain area scene applied in the applicationFor different application scenes, such as regions with irregular fluctuation, the setting of the value of the difference critical point is changed according to actual measurement and calculation.
By adopting the technical scheme: after the collected point cloud data are built into a three-dimensional model, the partition evaluation module and the comparison execution module are utilized to carry out cooperation operation, the three-dimensional model is subjected to partition detection, and each data factor under the corresponding partition is comprehensively considered to obtain a value capable of preliminarily reflecting the precision of the mapping graph of the corresponding partition, namely an image precision evaluation valueAnd the method can also be used for comparing with the evaluation threshold mol, screening out the subareas which do not meet the mapping requirement according to the comparison result, and carrying out targeted secondary measurement and calculation on the subareas of the departments, so that the overall quality of the topographic map after mapping is improved conveniently.
Example 2: referring to fig. 1, based on embodiment 1, the present embodiment provides a mapping method based on oblique photography large scale topographic map, comprising the following steps:
step one, acquiring ground point cloud data of a to-be-painted area captured by an unmanned aerial vehicle equipped with an inclined camera device;
step two, preprocessing the obtained point cloud data, including data cleaning, data filtering, data registering and data sampling, and data cleaning: removing noise points, outliers and invalid data; and (3) data filtering: screening the point cloud data according to the need, such as filtering according to density, elevation or color; data registration: registering the plurality of point cloud data sets to align the point cloud data sets under the same coordinate system; and (3) data sampling: downsampling the point cloud data according to the requirements so as to reduce the data quantity and accelerate the subsequent processing speed;
step three, converting the data into a three-dimensional model through a point cloud registration and density control algorithm according to the point cloud data;
and (3) point cloud registration: performing initial registration on the preprocessed point cloud data, aligning coordinates of a plurality of point cloud data sets to the same coordinate system, and searching alignment transformation by using a point cloud registration algorithm;
and (3) density control: controlling the density of the registered point cloud data by using a voxel grid algorithm;
three-dimensional reconstruction: performing three-dimensional reconstruction by using the registered point cloud data subjected to density control, wherein the three-dimensional reconstruction method is a voxel method, dividing a space into voxel grids, and generating a three-dimensional model according to the density and attribute information of the point cloud data in voxels;
step four, performing terrain analysis on the three-dimensional model to obtain terrain data so as to mark the position corresponding to the three-dimensional model;
fifthly, equally dividing the region to be mapped to form n partitions, acquiring evaluation parameters of each partition, building a data analysis model according to the evaluation parameters subjected to dimensionless treatment, and generating image precision evaluation values under the corresponding partitions；
Step six, according to the sequence of the corresponding numbers of the subareas, the image precision evaluation value under each subarea is evaluatedAnd (3) comparing the ground point cloud data strategy with a preset evaluation threshold mol, if the difference value is not more than 10, not responding, if the difference value is more than 10, sending a primary early warning signal, and executing the ground point cloud data strategy under the corresponding partition according to the first step until not responding.
In the application, the related formulas are all the numerical calculation after dimensionality removal, and the formulas are one formulas for obtaining the latest real situation by software simulation through collecting a large amount of data, and the formulas are set by a person skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over several network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application.
Claims (5)
1. A mapping system based on oblique photography large scale topography, comprising:
the data acquisition module is used for acquiring ground point cloud data of a to-be-painted area captured by an unmanned aerial vehicle equipped with an inclined camera device;
the data processing module is used for preprocessing the acquired point cloud data and is characterized in that:
the three-dimensional reconstruction module converts the data into a three-dimensional model through a point cloud registration and density control algorithm according to the point cloud data;
the terrain analysis module is used for carrying out terrain analysis on the three-dimensional model, and obtaining terrain data so as to mark the position corresponding to the three-dimensional model;
the partition evaluation module is used for carrying out halving processing on the area to be mapped to form n partitions, acquiring evaluation parameters of each partition, constructing a data analysis model according to the evaluation parameters subjected to dimensionless processing, and generating image precision evaluation values under the corresponding partitions；
In the partition evaluation module, the evaluation parameters comprise a point cloud sampling density value, a data point average resolution and a root mean square error value, and the point cloud sampling density value is obtained by the following steps: the point cloud sampling density of different partitions is measured by the number of points per square meter; the average resolution of the data points is obtained by the following steps: selecting at least three data points under the corresponding subareas, obtaining the resolution of each data point, summing the resolutions of each data point to obtain an average value, wherein the average value is the average resolution of the data points;
the mean square error value is obtained as follows:
s101, acquiring a group of reference data, wherein the reference data is obtained through a networked data source;
s102, acquiring measurement data corresponding to reference data, wherein the measurement data is acquired by an oblique image pickup device;
s103, calculating the difference between the measured value and the reference value for each corresponding measuring point, and obtaining a difference value through subtraction;
s104, squaring each difference value to obtain a square error;
s105, adding all square differences, and dividing the obtained value by the number of measurement points to obtain an average square difference;
s106, squaring the average square difference to obtain root mean square error of the corresponding partition;
generating image precision evaluation values under corresponding partitionsThe formula according to is as follows:
；
in the method, in the process of the invention,representing a point cloud sampling density value,/->Representing the average resolution of the data points +.>The root mean square error value is represented,preset proportionality coefficients of point cloud sampling density value, data point average resolution and mean square error value respectively, and +.>G is a constant correction coefficient, t= =>T represents the number of each partition, n represents the number of the corresponding number, i.e. the number of partitions, +.>Is a positive integer;
the comparison execution module is used for estimating the image precision under each partition according to the sequence of the corresponding numbers of the partitionsAnd comparing the ground point cloud data strategy with a preset evaluation threshold mol, if the difference value is not more than 10, not responding, if the difference value is more than 10, sending out a first-level early warning signal, and executing the ground point cloud data strategy under the corresponding partition again until not responding.
2. A surveying system based on oblique photography large scale topography as claimed in claim 1, wherein: in the data processing module, preprocessing includes data cleansing, data filtering, data registration, and data sampling.
3. A surveying system based on oblique photography large scale topography as claimed in claim 2, wherein: in the three-dimensional reconstruction module, the specific process of forming the three-dimensional model is as follows:
and (3) point cloud registration: performing initial registration on the preprocessed point cloud data, aligning coordinates of a plurality of point cloud data sets to the same coordinate system, and searching alignment transformation by using a point cloud registration algorithm;
and (3) density control: controlling the density of the registered point cloud data by using a voxel grid algorithm;
three-dimensional reconstruction: and performing three-dimensional reconstruction by using the registered point cloud data subjected to density control, wherein the three-dimensional reconstruction method is a voxel method, dividing the space into voxel grids, and generating a three-dimensional model according to the density and attribute information of the point cloud data in the voxels.
4. A surveying system based on oblique photography large scale topography as claimed in claim 3, wherein: in the terrain analysis module, the terrain data includes ground elevation values, ground slope directions, terrain curvatures, and contours.
5. A mapping method based on oblique photography large scale topography using the system of any one of claims 1 to 4, comprising the steps of:
step one, acquiring ground point cloud data of a to-be-painted area captured by an unmanned aerial vehicle equipped with an inclined camera device;
preprocessing the acquired point cloud data, including data cleaning, data filtering, data registering and data sampling;
step three, converting the data into a three-dimensional model through a point cloud registration and density control algorithm according to the point cloud data;
step four, performing terrain analysis on the three-dimensional model to obtain terrain data so as to mark the position corresponding to the three-dimensional model;
fifthly, equally dividing the region to be mapped to form n partitions, acquiring evaluation parameters of each partition, building a data analysis model according to the evaluation parameters subjected to dimensionless treatment, and generating image precision evaluation values under the corresponding partitions；
Step six, according to the sequence of the corresponding numbers of the subareas, the image precision evaluation value under each subarea is evaluatedAnd (3) comparing the ground point cloud data strategy with a preset evaluation threshold mol, if the difference value is not more than 10, not responding, if the difference value is more than 10, sending a primary early warning signal, and executing the ground point cloud data strategy under the corresponding partition according to the first step until not responding.
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