CN113607135B - Unmanned aerial vehicle inclination photogrammetry method for road and bridge construction field - Google Patents
Unmanned aerial vehicle inclination photogrammetry method for road and bridge construction field 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
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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
An unmanned aerial vehicle oblique photography measurement method for the road and bridge construction field comprises the following steps: step (1): software and hardware equipment selection (1); step (2): determining unmanned aerial vehicle oblique photography parameters; step (3): unmanned aerial vehicle oblique photography route planning (7); step (4): data processing; step (5): the three-dimensional live-action model and the product are directly applied (12); step (6): the three-dimensional digital model is combined with BIM (13); step (7): and (5) performing precision inspection on the generated model after aviation. The invention has the advantages of improving the measuring efficiency, reducing the safety risk of measuring operation, reducing the construction cost and the like. The method has obvious advantages in the aspects of large-scale landform mapping and high-resolution influence acquisition, can greatly reduce the workload of mapping field industry and greatly improve the working efficiency.
Description
Technical Field
The invention relates to the field of road and bridge construction, in particular to an unmanned aerial vehicle oblique photogrammetry method for the field of road and bridge construction.
Background
In recent years, a new measurement technology, namely an oblique photogrammetry technology, is developed, the limitation that the traditional aerial survey remote sensing influence can only be shot from the vertical direction is changed, the oblique photogrammetry technology utilizes a plurality of sensors to acquire data from different angles, massive data information is efficiently and rapidly acquired, the objective condition of the ground is truly and reliably reflected, and the requirement of people on three-dimensional information is met.
However, current oblique photogrammetry techniques suffer from the following significant drawbacks: the measuring efficiency is low, the measuring operation safety risk is high, and the construction cost is high.
Disclosure of Invention
The invention provides an unmanned aerial vehicle oblique photogrammetry method used in the road and bridge construction field.
An unmanned aerial vehicle oblique photography measurement method for the road and bridge construction field comprises the following steps:
step (1): software and hardware equipment selection (1);
step (2): determining unmanned aerial vehicle oblique photography parameters;
step (3): unmanned aerial vehicle oblique photography route planning (7);
step (4): data processing;
step (5): the three-dimensional live-action model and the product are directly applied (12);
step (6): the three-dimensional digital model is combined with BIM (13);
step (7): and (5) performing precision inspection on the generated model after aviation.
The beneficial effects are that:
the invention has the advantages of improving the measuring efficiency, reducing the safety risk of measuring operation, reducing the construction cost and the like. The method has obvious advantages in the aspects of large-scale landform mapping and high-resolution influence acquisition, can greatly reduce the workload of mapping field industry and greatly improve the working efficiency.
Drawings
FIG. 1 is a flow chart of an application of unmanned aerial vehicle oblique photogrammetry technology in the field of road and bridge construction;
FIG. 2 is a schematic diagram of the unmanned aerial vehicle tilt photogrammetry technology earth and stone rechecking and engineering quantity rapid estimation for road and bridge construction field;
fig. 3 is a schematic diagram of three-dimensional measurement of topographic data of unmanned aerial vehicle oblique photogrammetry technology for road and bridge construction field.
Meaning of symbols in the drawings: 1. selecting software and hardware equipment; 2. unmanned plane; 3. modeling software; 4. image precision; 5. a focal length; 6. shooting distance; 7. unmanned aerial vehicle oblique photography route planning; 8. calculating route image space three; 9. densely matching multiple video images; 10. constructing a triangle net TIN; 11. texture mapping; 12. the three-dimensional live-action model and the product are directly applied; 13. The three-dimensional digital model is combined with the BIM.
Detailed Description
The following detailed description of the present invention, taken in conjunction with the accompanying drawings, will clearly and fully describe the technical solutions of the embodiments, it being evident that the embodiments described are only some, but not all, of the embodiments of the present invention. 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.
An unmanned aerial vehicle oblique photography measurement method for the road and bridge construction field comprises the following steps:
step (1): software and hardware equipment selection (1);
step (2): determining unmanned aerial vehicle oblique photography parameters;
step (3): unmanned aerial vehicle oblique photography route planning (7);
step (4): data processing;
step (5): the three-dimensional live-action model and the product are directly applied (12);
step (6): the three-dimensional digital model is combined with BIM (13);
step (7): and (5) performing precision inspection on the generated model after aviation.
Example 1
An unmanned aerial vehicle oblique photography measurement method for the road and bridge construction field comprises the following steps:
step (1): software and hardware device selection (1):
firstly, the type of the aircraft is selected, and the unmanned aerial vehicle (2) suitable for the construction site conditions is selected according to the brands and different types of the unmanned aerial vehicles (2) on the market. The unmanned aerial vehicle (2) with stable flight, strong cruising ability, high pixel precision (4) and high positioning precision is preferentially selected.
International mainstream modeling software (3): the content-Capture of Bentley company is based on the rapid three-dimensional scene operation software of graphic operation unit GPU, can generate ultra-high density point cloud based on real images, can generate a realistic three-dimensional scene model from simple continuous images without manual intervention, and can generate a series of format data such as point cloud data, DSM, DEM, orthographic images and the like through modeling software (3).
Step (2): unmanned aerial vehicle oblique photography parameter determination:
the image accuracy (4) is determined based on the planning model accuracy, so in order to achieve a predetermined image accuracy (4), the focal distance (5) and the imaging distance (6) must be determined according to the following formula:
image accuracy (4) focal length (5) maximum size of image = sensor width) shooting distance (6).
Step (3): unmanned aerial vehicle oblique photography route planning (7):
before flying, comprehensively analyzing the whole topography and the ground object of the project area, making a flying route, planning the aerial photographing height and the image overlapping degree, wherein the aerial photographing height has the following calculation formula:
wherein: ls=sensor length (m);
d = distance (m) between camera and object;
f=focal length of the digital camera;
l = image length (Px);
step (4): and (3) data processing:
after the aviation is finished, the acquired image data is subjected to internal processing, and the data processing technology comprises main technical contents such as aerial image space three calculation (8), multi-view image dense matching (9), TIN triangular network generation (10), texture mapping (11) and the like, so that the real product data is finally obtained.
Route image space three calculation (8):
the basic principle of oblique photography is basically the same as that of traditional aerial survey, and the oblique photography is divided into two parts of continuous point extraction and aerial three calculation, because the oblique photography causes larger deformation among photos, the matching difficulty of connecting points is increased, but because the oblique photography is usually shooting at different angles of five navigation belts, the number of homonymous points is increased by four times, the relation between unknowns is complex, and in recent years, along with the development of technology, a popular basic algorithm is as follows:
wherein: [ X ] A Y A Z Z ] T Representing coordinates of the image point in a space coordinate system; lambda is the projection coefficient; r is an orthogonal transformation matrix of outer azimuth element angle elements,GPS position for the moment of flight shooting->Representing the speed of the unmanned aerial vehicle in three directions, [ X Y Z ]] T Representing coordinates of an image space auxiliary coordinate system, Δt represents camera exposure delay.
Multi-view image dense matching (9):
dense matching (9) of multi-view images is the core of oblique photogrammetry, and the multi-view images have the characteristics of high ground resolution and large overlapping degree, but the multi-view images also cause data redundancy of the images; meanwhile, as the unmanned aerial vehicle has lower flying height and unstable flying gesture, the base height of the image is smaller and the change of the overlapping degree is obvious, so that the difficulty is brought to acquiring the homonymous points of the multi-view image, and how to quickly find the homonymous points in the multi-view image, so that the recovery of the three-dimensional position information of the photographed object is the most important step of multi-view image dense matching. The current mature algorithm is SGM semi-global matching algorithm or PMVS multi-view matching algorithm.
Triangle net TIN construction (10):
the triangular net structure (10) is mainly divided into two stages: firstly, the point cloud data comprises constraint points to jointly establish a triangle network; and secondly, carrying out diagonal line exchange by taking line elements obtained by image matching as constraint conditions, and adjusting each line segment in the triangular network by utilizing a local optimization process LOP to form a TIN triangular network (10) with constraint conditions.
Texture mapping (11):
the essence of the texture mapping (11) is that the photographed image is processed to generate a texture image, the corresponding relation of two space points is established through a certain mathematical relation, the two space points are the space points of the two-dimensional texture and the space points of the three-dimensional model respectively, and finally the texture image is attached to the three-dimensional model to form a vivid three-dimensional model. Texture mapping can be classified into forward and reverse mapping according to mapping direction.
After the image data is processed and modeled by the data platform, a plurality of column models and data such as: three-dimensional live-action model, DEM, DSM, DOM, point cloud data, etc.
After the three-dimensional live-action model is generated, format conversion is needed, the common formats are OSGB and OBJ, and after conversion is finished, the three-dimensional model can be imported into an application platform, such as: the Unity3D, wish3D network end, the new earth PC end, the Altizure and the like can also be used for carrying out secondary deep processing on the three-dimensional model, and the model can be subjected to operations such as texture modification, mapping, model splicing and the like by using software such as DP-model, model side and SVS.
Step (5): three-dimensional live-action model and product direct application (12):
as shown in fig. 2 and 3, through the three-dimensional real model after aviation, various functions and applications can be realized: (1) quickly rechecking the earthwork; (2) direct measurement in the topographic data field; (3) Investigation analysis and scheme formulation of emergency and disaster relief conditions on site; (4) flood risk assessment and simulation and disaster prevention scheme establishment; (5) different subthreshold buffer analysis; (6) dynamic data management and behavior analysis; (7) other GIS-related applications.
Step (6): three-dimensional digital model in combination with BIM (13):
the application of the point cloud data in the Civil3D, the point cloud data generated by the three-dimensional model can be imported into the Civil3D, and after a high-precision terrain curved surface is generated, roads and a field plateau are created according to the design file, so that rapid earth and stone side calculation, various slope setting calculations and the like can be realized.
The method for integrating the three-dimensional live-action model and the Revit by selecting the Bentley LumentRT software firstly aims to solve the problem of multi-source heterogeneous data integration, namely how to integrate various format data obtained in different modes together, and mainly comprises the following steps: data structure, data type, application platform, coordinate system, elevation system, etc.; the map projection conversion and coordinate mapping are particularly noted with respect to the columns, so that the project must be made by considering how to unify all the standards.
And programming by Dynamo, reading bridge design parameters, and placing parameterization families, so that the problem of short plates of Revit in bridge modeling at the present stage is solved. After the bridge modeling is completed, the Revit model is converted into a format accepted by a corresponding platform, usually obj, skp, fbx, 3Dtiles and the like, and after the bridge modeling is imported into an operation platform, the fusion of the Revit project and the three-dimensional real model can be realized.
Step (7): and (5) performing precision inspection on the generated model after aviation.
In order to reduce the influence of projection difference on the accuracy of the image matching result, the position of the image control point is arranged at a position 1-2cm away from the image boundary; the specific method for loading the corrected three-dimensional model into ContextCaptureViewer software comprises the following steps: taking 5 times of measurement on each inspection point, taking coordinate values measured by field industry as true values, and combining the coordinate values measured for 5 times to calculate errors in the inspection point, wherein in order to reduce adverse effects of accidental errors on detection results, a calculation formula is as follows:
wherein:
m is the error in the point location of the check point;
xi, yi is a plane coordinate value acquired for the ith time on the three-dimensional model;
x and Y are measured check point values.
Example 2
The unmanned aerial vehicle oblique photography measurement system for the road and bridge construction field comprises data acquisition from different angles by utilizing a plurality of sensors, efficient and rapid acquisition of massive data information, software and hardware equipment selection (1), unmanned aerial vehicle oblique photography parameter determination, unmanned aerial vehicle oblique photography route planning, aerial survey image space three calculation (8), multi-view image dense matching (9), construction of a triangular network TIN (10), texture mapping (11), direct application of a three-dimensional live-action model and a product (12), and combination of a three-dimensional digital model and BIM (13).
Firstly, the type of the aircraft is selected, and the unmanned aerial vehicle (2) suitable for the construction site conditions is selected according to the brands and different types of the unmanned aerial vehicles (2) on the market. Unmanned aerial vehicle with stable flight, strong cruising ability, high pixel precision (4) and high positioning precision (4) is preferably selected.
The image accuracy (4) is determined based on the planning model accuracy, so in order to achieve a predetermined image accuracy (4), the focal distance (5) and the imaging distance (6) must be determined according to the following formula: image accuracy (4) focal length (5) maximum size of image = sensor width) shooting distance (6).
Before flying, comprehensively analyzing the overall topography and the ground object of the project area, making a flying route, planning the aerial photographing height and the image overlapping degree.
After the aviation is finished, the acquired image data is subjected to internal processing, and the data processing technology comprises main technical contents such as aerial image space three calculation (8), multi-view image dense matching (9), TIN triangular network generation (10), texture mapping (11) and the like, so that the real product data is finally obtained.
In order to reduce the influence of the projection difference on the accuracy of the result of the image matching (9), the position of the image control point is arranged at a position 1-2cm away from the image boundary.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
In the description of the present invention, it should be noted that, directions or positional relationships indicated by terms such as "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., are directions or positional relationships based on those shown in the drawings, or are directions or positional relationships conventionally put in use of the inventive product, are merely for convenience of describing the present invention and simplifying the description, and are not indicative or implying that the apparatus or element to be referred to must have a specific direction, be constructed and operated in a specific direction, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
In the description of the present invention, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Claims (1)
1. An unmanned aerial vehicle oblique photography measurement method for the road and bridge construction field is characterized by comprising the following steps:
step (1): software and hardware equipment selection (1);
firstly, selecting the type of an aircraft, and selecting unmanned aerial vehicles (2) suitable for construction site conditions and Context-Capture modeling software (3) of a Bentley company according to brands and different models of the unmanned aerial vehicles (2) on the market;
step (2): determining unmanned aerial vehicle oblique photography parameters;
the image accuracy (4) is determined on the basis of the planning model accuracy, and in order to achieve a predetermined image accuracy (4), the focal distance (5) and the shooting distance (6) must be determined according to the following formula:
image accuracy (4) focal length (5) maximum size of image = sensor width capture distance (6);
step (3): unmanned aerial vehicle oblique photography route planning (7);
before flying, comprehensively analyzing the whole topography and the ground object of the project area, making a flying route, planning the aerial photographing height and the image overlapping degree, wherein the aerial photographing height has the following calculation formula:
wherein: ls=sensor length (m);
d = distance (m) between camera and object;
f=focal length of the digital camera;
l = image length (Px);
p=image precision (no units);
step (4): and (3) data processing:
after the aviation is finished, carrying out internal processing on the acquired image data, wherein the data processing technology comprises main technical contents of aerial image space three calculation (8), multi-view image dense matching (9), TIN triangular net generation (10) and texture mapping (11), and finally obtaining real product data;
route image space three calculation (8);
the basic principle of oblique photography is basically the same as that of traditional aerial survey, and the oblique photography is divided into two parts of continuous point extraction and aerial three calculation, because the oblique photography causes larger deformation among photos, the matching difficulty of connecting points is increased, but because the oblique photography is usually shooting at different angles of five navigation belts, the number of homonymous points is increased by four times, the relation between unknowns is complex, and in recent years, along with the development of technology, a popular basic algorithm is as follows:
wherein: [ X ] A Y A Z Z ] T Representing coordinates of the image point in a space coordinate system; lambda is the projection coefficient; r is an orthogonal transformation matrix of outer azimuth element angle elements,GPS position for the moment of flight shooting->Watch (watch)
Illustrating the speeds of the unmanned aerial vehicle in three directions, [ X Y Z ]] T Coordinates representing an image space auxiliary coordinate system, Δt representing camera exposure delay;
multi-view image dense matching (9);
the algorithm of the multi-view image dense matching (9) is SGM semi-global matching algorithm or PMVS multi-view matching algorithm;
constructing a triangle net TIN (10);
the triangulated mesh (10) is divided into two phases: firstly, the point cloud data comprises constraint points to jointly establish a triangle network; secondly, line elements obtained by image matching are used as constraint conditions to perform diagonal line exchange, and each line segment in the triangular network is adjusted by utilizing a local optimization process LOP to form a TIN triangular network (10) with constraint conditions;
-texture mapping (11);
the essence of the texture mapping (11) is that the photographed image is processed to generate a texture image, the corresponding relation of two space points is established through a certain mathematical relation, the two space points are the space points of the two-dimensional texture and the space points of the three-dimensional model respectively, and finally the texture image is attached to the three-dimensional model to form a vivid three-dimensional model; texture mapping can be classified into forward and reverse mapping according to mapping direction;
after the image data is processed and modeled by the data platform, a plurality of column models or data can be generated according to actual needs;
after the three-dimensional live-action model is generated, format conversion is needed, the common formats are OSGB and OBJ, the three-dimensional model can be imported into an application platform after conversion is completed, and the three-dimensional model can also be subjected to deep processing again, texture modification, mapping and model splicing;
step (5): the three-dimensional live-action model and the product are directly applied (12);
through the three-dimensional real model after aviation, various functions and applications can be realized: (1) quickly rechecking the earthwork; (2) direct measurement in the topographic data field; (3) Investigation analysis and scheme formulation of emergency and disaster relief conditions on site; (4) flood risk assessment and simulation and disaster prevention scheme establishment; (5) different subthreshold buffer analysis; (6) dynamic data management and behavior analysis; (7) other GIS-related applications;
step (6): the three-dimensional digital model is combined with BIM (13);
the application of the point cloud data in the Civil3D, the point cloud data generated by the three-dimensional model can be imported into the Civil3D, and after a high-precision terrain curved surface is generated, roads and a field plateau are created according to the design file, so that rapid earth and stone side calculation and various slope releasing calculation can be realized;
fusing the three-dimensional live-action model with Revit by selecting Bentley Lumen RT software, programming by utilizing Dynamo, reading bridge design parameters, and placing parameterized families, so that the short plates of Revit in bridge modeling at the present stage are solved; after the bridge modeling is completed, converting the Revit model into a format accepted by a corresponding platform, and importing the Revit model into an operation platform to realize fusion of a Revit project and a three-dimensional live-action model;
step (7): performing precision inspection on the generated model after aviation;
in order to reduce the influence of projection difference on the accuracy of the image matching result, the position of the image control point is arranged at a position 1-2cm away from the image boundary; the corrected three-dimensional model is loaded into Contex-tCapture-Viewer software by the following specific steps: measuring each inspection point for 5 times respectively, taking coordinate values actually measured by the field industry as true values, and solving errors in the inspection point by combining the coordinate values measured for 5 times, wherein the calculation formula is as follows:
wherein: m is the error in the point location of the check point;
xi, yi is a plane coordinate value acquired for the ith time on the three-dimensional model;
x and Y are measured check point values.
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| CN114202981B (en) * | 2021-12-10 | 2023-06-16 | 新疆工程学院 | Simulation platform for photogrammetry experiments |
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| CN114509051A (en) * | 2022-01-26 | 2022-05-17 | 中交二公局第三工程有限公司 | Unmanned aerial vehicle measurement lofting method in road engineering |
| CN114782619B (en) * | 2022-01-26 | 2023-04-18 | 中交二公局第三工程有限公司 | BIM technology-based visual safety facility circulation management method |
| CN116465370A (en) * | 2023-04-01 | 2023-07-21 | 中铁六局集团路桥建设有限公司 | Method and system for calculating soil and stone quantity of dense vegetation area |
| CN116989767A (en) * | 2023-08-02 | 2023-11-03 | 济南市勘察测绘研究院 | Rail transit measurement method based on BIM and oblique photogrammetry technology |
| CN116753962B (en) * | 2023-08-16 | 2023-10-13 | 成都睿铂科技有限责任公司 | Route planning method and device for bridge |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109520479A (en) * | 2019-01-15 | 2019-03-26 | 成都建工集团有限公司 | Method based on unmanned plane oblique photograph auxiliary earth excavation construction |
| CN111322994A (en) * | 2020-04-22 | 2020-06-23 | 福州市勘测院 | Large-scale cadastral survey method for intensive house area based on unmanned aerial vehicle oblique photography |
| CN111458720A (en) * | 2020-03-10 | 2020-07-28 | 中铁第一勘察设计院集团有限公司 | Airborne laser radar data-based oblique photography modeling method for complex mountainous area |
| CN111951398A (en) * | 2020-07-27 | 2020-11-17 | 中建三局第二建设工程有限责任公司 | Intelligent lofting construction method based on unmanned aerial vehicle oblique image technology |
-
2021
- 2021-08-13 CN CN202110927136.7A patent/CN113607135B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109520479A (en) * | 2019-01-15 | 2019-03-26 | 成都建工集团有限公司 | Method based on unmanned plane oblique photograph auxiliary earth excavation construction |
| CN111458720A (en) * | 2020-03-10 | 2020-07-28 | 中铁第一勘察设计院集团有限公司 | Airborne laser radar data-based oblique photography modeling method for complex mountainous area |
| CN111322994A (en) * | 2020-04-22 | 2020-06-23 | 福州市勘测院 | Large-scale cadastral survey method for intensive house area based on unmanned aerial vehicle oblique photography |
| CN111951398A (en) * | 2020-07-27 | 2020-11-17 | 中建三局第二建设工程有限责任公司 | Intelligent lofting construction method based on unmanned aerial vehicle oblique image technology |
Non-Patent Citations (1)
| Title |
|---|
| 焦旺.基于倾斜摄影数据的大比例尺地形图制作方法研究.基础科学辑.2020,第第2021卷卷(第第2021卷期),论文正文. * |
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