KR102121974B1 - Disaster damage investigation·analysis system using drone and disaster damage investigation·analysis method - Google Patents

Abstract

The present invention relates to a disaster damage investigation and analysis system using drones and a disaster damage investigation and analysis method using the same. According to an embodiment of the present invention, the disaster damage investigation and analysis system using drones comprises: a drone device photographing a disaster-damaged region from above through a mounted camera module to obtain image information of the disaster-damaged region; and a controller device receiving the image information of the disaster-damaged region from the drone device through wireless communication with the drone device, and processing the transmitted image information through a preset automatic calibration algorithm to obtain 3D modeling information. The automatic calibration algorithm applies preset ground reference point information and interior orientation parameters (IOPs) information of the camera module mounted on the drone device to the image information obtained through the drone device to set up an initial camera model so as to calibrate the image information, and then automatically optimizes the initial camera model while repeatedly adjusting external orientation parameters (EOPs) information of the camera module through bundle adjustment to increase the accuracy of the image information.

Description

DISASTER DAMAGE INVESTIGATION, ANALYSIS SYSTEM USING DRONE AND DISASTER DAMAGE INVESTIGATION, ANALYSIS METHOD}

The present invention relates to a disaster damage investigation/analysis system using a drone and a disaster damage investigation/analysis method using the drone. More specifically, a disaster detection special vehicle and a drone wirelessly connected to a wide range of disaster damage areas are more accurately and quickly. It relates to a disaster damage investigation and analysis system using a drone capable of research and analysis, and a disaster damage investigation and analysis method using the same.

In general, in the event of a natural disaster such as a flood, local governments or central agencies in the affected area conduct damage investigations on private and public facilities.

However, there is a shortage of investigators who are actually invested in the damage investigation, and the on-site manager who is in charge of the damage site conducts a total investigation of roads around the small rivers, rivers, rivers, and off-site facilities at the disaster site, such as by observation. The overall survey is insufficient, such as surveys and calculations.

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As described above, in accurately surveying and calculating the damage amount in the disaster area, there is a problem in that the accuracy of the result information according to the survey of the damaged area is remarkably reduced, as the investigation manpower and the investigation time are insufficient and the survey tools are also insufficient.

Meanwhile, drone-based disaster system technologies are being developed that can grasp the disaster damage situation by using a small drone, which has been spotlighted as an investigation platform by quickly and conveniently acquiring a wide range of spatial information.

An example of this is the technology of a disaster site exploration system using a drone of Korean Patent Publication No. 2019-0037212, and the disaster site exploration system using the drone is an exploration device equipped with a gas sensor, etc. to explore a disaster site such as a fire. It provides real-time situation information (temperature, harmful gas, human movement, and LiDAR measurement results) collected from (drone) to users in real time.

However, in addition to providing real-time information of the disaster site as described above, there is also a demand for a technique for accurately surveying and calculating the scale of the damage to recover the damaged area after the disaster.

In particular, in the case of disasters such as floods, the technology that can efficiently conduct damage investigations of rivers or roads with long irradiation distances in a short period of time by using a small drone and an investigation vehicle that can be quickly moved in the field because the damage range is relatively wide. This is required, and furthermore, there is a need for a technique capable of more accurately and promptly conducting natural disaster damage investigation by improving the accuracy of image information of a damaged area using a drone.

Korean Patent Publication No. 10-2019-0037212

The present invention was devised to solve the above-described problem, and after acquiring the image information of the affected area by tilting and vertically shooting the disaster-affected area through a camera device mounted on the drone by floating the drone in the disaster-affected area A disaster damage investigation and analysis system using drones that can accurately and quickly investigate and analyze a wide range of disaster-affected areas by constructing and providing a 3D terrain model through a preset automatic correction algorithm using one image information. The aim is to provide a method of investigating and analyzing disasters used.

In particular, it is possible to improve the positioning accuracy of drone mapping by optimizing the camera model for processing image information by repeatedly adjusting the external expression element through the determination of the internal expression element through the pre-camera test and the bundle adjustment method. It is to provide technology to investigate and analyze disaster damage information.

Other objects of the present invention will be more apparent through the preferred embodiments described below.

According to an aspect of the present invention, a drone device for acquiring image information of a disaster-affected area by photographing a disaster-affected area from above through a mounted camera module; And a controller device for wirelessly communicating with the drone device to receive image information of a disaster-affected area from the drone device, and processing the transmitted image information through a preset automatic correction algorithm to obtain 3D modeling information. , The automatic correction algorithm applies preset ground reference point information and interior orientation parameters (IOPs) information of the camera module mounted on the drone device to the image information acquired through the drone device. Establishing an initial camera model for correcting the image information, and then automatically adjusting the initial camera model while repeatedly adjusting the external orientation parameters (EOPs) information of the camera module through a bundle adjustment method. Optimization can improve the accuracy of the video information.

According to another aspect of the present invention, in the disaster damage analysis method using a disaster damage analysis system using a drone device in wireless communication with the controller device provided in the investigation vehicle, the disaster damage area through the camera module mounted on the drone device Oblique shooting; Selecting the mapping area information based on the image information by transmitting the image information obtained through oblique shooting to the controller device; Transmitting selected mapping area information to the drone device and vertically photographing the mapping area through a camera module of the drone device; Establishing an initial camera model by transmitting image information obtained through vertical imaging to the controller device and inputting predetermined ground reference point information to the transmitted image information; Correcting the camera model by automatically correcting the image information by applying internal expression element information and external expression element information of the camera module to image information acquired through the initial camera model; Determining whether the corrected camera model is included in a preset optimization condition; If it is determined that the corrected camera model is optimized, generating 3D image information by performing 3D modeling; Providing the generated three-dimensional image information to the user through the display device; through the disaster damage area can be more accurately and quickly investigated and analyzed.

The disaster damage investigation and analysis system using a drone according to the present invention and a disaster damage investigation analysis method using the same are obtained by acquiring image information of a disaster area by using a drone connected to a controller device mounted in a disaster investigation special vehicle and a wide range of damage It has the advantage of being able to safely and quickly check and analyze damage information on areas that are difficult or impossible to access by an area or an investigator.

In addition, it is possible to further improve the accuracy of image information by processing the image information by optimizing the camera model through the process of repeatedly adjusting external facial elements such as the camera position or posture information of the drone.

In addition, various sensors such as the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS), gyro sensor, accelerometer, and barometer provided in the drone and each sensing information of the LiDAR scanner and GNSS/INS sensors installed in the special vehicle for disaster investigation By combining them, it is possible to more accurately collect and construct 3D spatial topographic information of the disaster accident investigation site.

1 is a view for explaining a basic investigation system of a disaster damage investigation and analysis system using a drone according to an embodiment of the present invention.
2 is a system diagram showing a specific configuration of a disaster damage investigation and analysis system using a drone according to an embodiment of the present invention.
Figure 3 is a flow diagram of a disaster damage investigation analysis method using a disaster damage investigation and analysis system according to an embodiment of the present invention.
4 is a view for explaining a method of oblique photography of a drone device according to an embodiment of the present invention.
5 is a view for explaining a vertical imaging method of a drone device according to an embodiment of the present invention.

The present invention can be applied to a variety of transformations and may have various embodiments, and specific embodiments will be illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all conversions, equivalents, and substitutes included in the spirit and scope of the present invention. In the description of the present invention, when it is determined that detailed descriptions of related known technologies may obscure the subject matter of the present invention, detailed descriptions thereof will be omitted.

Terms such as first and second may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from other components.

The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, terms such as “include” or “have” are intended to indicate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, one or more other features. It should be understood that the existence or addition possibilities of fields or numbers, steps, operations, components, parts or combinations thereof are not excluded in advance. Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a view for explaining a basic investigation system of a disaster damage investigation and analysis system 10 using a drone according to an embodiment of the present invention.

First, referring to FIG. 1, a disaster damage investigation and analysis system 10 using a drone according to an embodiment of the present invention is wirelessly connected to the investigation vehicle 20 and the controller device 200 equipped with the controller device 200 Through the drone device 100 connected to the communication it is possible to investigate and analyze the damage scale of the disaster-affected area.

That is, the controller device mounted on the survey vehicle 20 through wireless communication to obtain the video information of the disaster-affected area by floating the drone device 100 over the disaster-affected area where a disaster such as a flood occurred. It transmits to (100), and then, through the controller device 100, analyzes the damage scale of the disaster area by acquiring drone mapping processing and 3D image information using a preset automatic correction algorithm for the image information of the disaster-affected area. It becomes possible.

2 is a system diagram showing a specific configuration of a disaster damage investigation and analysis system 10 using a drone according to an embodiment of the present invention.

Referring to Figure 2, the disaster damage investigation and analysis system 10 using a drone according to an embodiment of the present invention may be composed of a drone device 100 and a controller device 200 mounted on an investigation vehicle.

The drone device 100 may include a main body (not shown), a camera module 110, a sensor module 120, a flight driving module 130, a communication module 140 and a control module 150.

The camera module 110 is provided on the main body of the device, and is composed of an optical camera to photograph the disaster-affected area to generate image information of the disaster-affected area.

The sensor module 120 is provided in the main body, and may be made of various sensors such as Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS), gyro sensor, accelerometer and barometer.

The flight driving module 130 is provided on the main body and can be operated by floating the main body in the air. Here, the flight driving module 130 may be applied to a variety of drone flight devices that are currently available, and of course can be variously changed according to the flight driving setting.

The communication module 140 is provided in the main body, and consists of communication equipment such as frequency communication and LTE-based mobile communication to perform real-time wireless communication with the controller device 200.

The control module 150 is provided inside the main body, and transmits image information generated by the camera module 110 to the controller device 200 through the communication module 140, and is transmitted from the controller device 200. The camera module 110, the sensor module 120, and the flight driving module 130 are controlled according to a control signal.

The controller device 200 includes a communication unit 210, a sensor unit 220, an image correction unit 230, an image processing unit 240, a damage information analysis unit 250, an information input unit 260, and an information display unit 270. It includes.

The communication unit 210 consists of communication equipment such as frequency communication and LTE-based mobile communication, and performs real-time wireless communication with the drone device 100.

The sensor unit 220 includes a LiDAR scanner and a GNSS/INS sensor, and acquires various sensing information such as LiDAR point cloud data on the ground where the survey vehicle is located.

The image correcting unit 230 corrects the image information transmitted from the drone device 100 through a preset automatic correction algorithm.

In this case, the image correction unit 230 may include a dictionary information setting unit 231, a camera model establishment unit 232, and a camera model correction unit 233.

The dictionary information setting unit 231 pre-calculates ground reference point information previously calculated using natural terrain objects and anti-aircraft signs to acquire absolute coordinates of image information, and a dictionary of the camera module 110 mounted in the drone device 100. Information on interior orientation parameters (IOPs) calculated through camera calibration is set in advance. Here, the interior orientation parameters (IOPs) information include location information of a main point on a photo coordinate system, camera focal length information, and distortion amount information of a camera lens.

At this time, the ground reference point information and the internal expression element information may be input and set from the user through the information input unit 260.

The camera model establishing unit 232 establishes an initial camera model by applying ground reference point information and internal expression element information set by the prior information setting unit 231 to image information.

The camera model correction unit 233 repeatedly adjusts the external orientation parameters (EOPs) information on the image information of the initial camera model and/or the camera model that needs to be recalibrated later through a bundle adjustment. To optimize the camera model. Here, exterior orientation parameters (EOPs) information include GPS location information and gimbal orientation information.

Meanwhile, the information on the internal expression elements (IOPs) and the information on the external expression elements (EOPs) may be obtained using metadata of image information (EXchangeable Image File, EXIF). In other words, the metadata (EXIF) includes basic information such as the size of the photo file, the date and time of shooting, aperture, ISO, and the model name of the camera, the focal length at the time of shooting, the latitude and longitude coordinates of the GNSS mounted on the drone, and the flight altitude. Posture angle (roll, pitch, yaw) information, etc. are recorded.

Therefore, the internal expression elements (IOPs) information may be obtained through the pre-image information of the camera module, and the external expression elements (EOPs) may be acquired through the photographed image information of the camera module through the flight operation of the drone device.

The image processing unit 240 performs 3D modeling based on the image information corrected by the image correction unit 230 to obtain 3D image information such as 3D point cloud data, 3D terrain information, and orthoimage map information. To acquire.

At this time, the image processing unit 240 fuses the sensing information obtained through the sensor unit 220 and the image information corrected through the image correction unit 230, and performs the 3D modeling to perform a high-precision 3D image. Information can be obtained.

The damage information analysis unit 250 analyzes the damage area of the linear terrain feature through a predetermined analysis program based on the 3D point cloud data, 3D terrain information, and orthogonal image map information obtained through the image processing unit 240. To provide damage scale information.

The information input unit 260 receives various input information of the user, and the information display unit 270 displays and provides various input/output information and image information to the user.

As described above, the disaster damage investigation and analysis system 10 using the drone according to the present invention acquires the image information of the disaster-affected area through the drone device 100, and receives the image information obtained through the controller device 200. By processing through a preset automatic correction algorithm, it is possible to obtain 3D modeling information, and use this to analyze damage scale information in a disaster area.

At this time, the automatic correction algorithm according to the present invention, the image information obtained through the drone device, preset ground reference point information and the internal orientation of the camera module mounted on the drone device (interior orientation parameters (IOPs)) information Applying to establish the initial camera model for correcting the image information, and then, while repeatedly adjusting the external orientation elements (exterior orientation parameters (EOPs)) information of the camera module through the bundle adjustment method (bundle adjustment) the initial camera model It may be characterized by automatically optimizing to increase the accuracy of the image information.

Hereinafter, a method of analyzing disaster damage using the disaster damage analysis system as described above will be described in detail with reference to FIGS. 3 to 5.

Figure 3 is a flow chart of a disaster damage investigation analysis method using a disaster damage investigation and analysis system according to an embodiment of the present invention, Figure 4 is a view for explaining the tilting method of the drone device according to an embodiment of the present invention , FIG. 5 is a view for explaining a vertical imaging method of a drone device according to an embodiment of the present invention.

As shown in Figure 3, the disaster damage investigation analysis method using a disaster damage analysis system according to an embodiment of the present invention, prior to shooting the disaster damage area through the camera module mounted on the drone device, prior information setting Step (S100) is performed.

In the pre-information setting step, the ground reference point is preset using absolute coordinate information measured using a linear terrain feature and an anti-aircraft cover, and the internal expression element information is recorded through camera calibration of the drone device. Can be set.

That is, in this embodiment, the interior orientation elements (IOPs) of the camera were calculated through the test of the non-surveying camera module mounted on the drone device before aerial photography. Subsequently, absolute coordinates were obtained by Virtual Reference Station (VRS) GNSS survey of 16 reference points and 11 check points (CPs) using natural topographic features and anti-aircraft landmarks in the target area.

When the pre-information setting is completed, the disaster-damaged area is tilted through the camera module mounted on the drone device (S110).

4(a) and 4(b), the drone device is operated at a first altitude P1, and rivers and roads are set in a predetermined range (for example, a radius of 2 m to 4 m). Primary image information may be obtained by performing oblique imaging along the centerline of a linear feature such as.

In this case, the first altitude P1 is 30 m to 50 m, and the primary image information includes foreground image information in a wider range than the vertical shooting range, which will be described later.

The reason for performing the oblique shooting is to find a specific mapping area, that is, a target area for intensive analysis by quickly securing a relatively wide range of foreground images while operating the drone device at a low altitude, that is, the first altitude P1.

Meanwhile, the image information obtained through oblique shooting is transmitted to the controller device through the communication module of the drone device, and the controller device selects mapping region information based on the transmitted image information (S120).

Thereafter, the controller device wirelessly communicates the selected mapping area information through the communication unit and transmits it to the drone device.

The drone device performs vertical shooting through the camera module on the transmitted mapping area (S130).

As shown in FIGS. 5(a) and 5(b), vertical imaging is performed while the drone device is operated at a second altitude (P2) at 400 m to 600 m intervals, thereby obtaining secondary image information. do.

At this time, the second altitude P2 is 90 m to 110 m, and the second image information includes image information for each session.

That is, by vertically photographing the mapping area secured through the oblique shooting at regular intervals, that is, preferably at 500 m intervals, it is possible to acquire detailed image information on the mapping area that is the target of intensive analysis.

The image information acquired through the vertical shooting is transmitted to the controller device, and the controller device establishes an initial camera model by applying preset ground reference point information (coordinate information) to the transmitted image information (S140).

Thereafter, the controller device determines whether the established initial camera model is included in a preset optimization condition (S160), and when it is determined that the initial camera model is not optimized, in the video information of the initial camera model, the inside of the camera module The camera model is corrected by automatically correcting the image information by applying facial expression element information and external expression element information (S170).

At this time, the internal orientation elements (interior orientation parameters (IOPs)) information uses a preset fixed value through a prior camera module test, and the external orientation elements (exterior orientation parameters (EOPs)) information is adjusted through the light beam adjustment method The camera model can be automatically calibrated using the calibration values.

Here, the interior orientation parameters (IOPs) information includes location information of a pub on a photo coordinate system, camera focal length information, and distortion amount information of a camera lens, and exterior orientation parameters (EOPs) information Includes GPS location information and gimbal direction information.

Thereafter, the controller device again determines whether the corrected camera model is included in a preset optimization condition (S160), and when the corrected camera model is determined to be optimized, performs 3D terrain modeling (S180) to perform a 3D spatial image. Generate information.

At this time, the controller device may perform 3D terrain modeling by fusing sensing information obtained from the LiDAR scanner and GNSS/INS sensor provided in the controller device with the image information in the process of performing 3D modeling, through which High-precision 3D spatial image information can be obtained.

That is, in the drone aerial photographing method, since point cloud data is generated mainly on the roof surface of the building exposed in the vertical direction, it is common that the side information of the object such as a building is mostly omitted.

Accordingly, in this embodiment, in order to integrate the ground LiDAR data and the drone sensing data at the site of the disaster accident, the natural terrain feature matching point in the target area is selected, and the coordinate transformation model formula is applied to the ground LiDAR data based on the coordinate system of the drone point group data. And registered heterogeneous point cloud data.

In this process, a 7-parameter similarity transformation coordinate transformation model formula that can be transformed while preserving the shape of the original coordinate system was applied.

At this time, the coordinate transformation model equation is as shown in [Equation 1] below.

[Equation 1]

Here, t is the amount of translation, s is the scale, R is the rotation matrix, and the seven transform coefficients can be obtained through Equation 2 below.

[Equation 2]

Here, A and B are coordinates of the matching points, respectively, and the transformation coefficients t, s, and R can be calculated through Equation 3 below.

[Equation 3]

On the other hand, in order to solve the position error that may occur in the process of transforming the coordinate system by the 7-parameter similarity transformation, the condition between the two coordinate systems is the same and the vertical component is not rotated is reflected in the coordinate transformation model formula. The occurrence error was minimized.

At this time, the amount of movement in the vertical direction (ΔZ) was given as an average altitude value, and a transformation model equation through adjustment of the average altitude value was applied as in [Equation 4] below.

[Equation 4]

The 3D image information obtained through the above-described process may include 3D point cloud data, 3D terrain information, and orthogonal image map information, and the 3D image information is a display device such as a display provided in the controller device. Is provided to the user through.

On the other hand, if it is determined that the corrected camera model is still not included in the preset optimization condition, the camera model is returned to the correcting step (S160), and the external expression element information is repeatedly corrected in the image information of the camera model. To optimize the camera model.

In other words, after establishing the initial camera model, drone mapping by optimizing the camera model for processing image information by repeatedly adjusting the external expression element through bundle adjustment and determination of the internal expression element through pre-camera verification. It is possible to improve the positioning accuracy of.

If the camera model corrected through the iterative adjustment process of the camera model is determined to be optimal, 3D image information is generated through 3D modeling (S180).

Thereafter, based on the 3D image information, damage analysis information such as a damaged area of a linear terrain such as a road or a river may be analyzed through a predetermined analysis program (S190), and analysis result information may be provided to a user (S200). .

As described above, the disaster damage analysis system using the drone and the disaster damage analysis method using the same according to the present invention have the advantage of improving the accuracy of image information in a disaster-affected area through a drone mapping technique that optimizes the camera model using camera verification and luminous flux adjustment. There is this.

In addition, there is an effect that it can contribute to the national disaster safety management work by quickly collecting and building high-precision 3D spatial information of the future disaster accident site through the fusion between the ground LiDAR data of a special vehicle and the sensing data of a drone device.

The above-described preferred embodiments of the present invention are disclosed for the purpose of illustration, and those skilled in the art will appreciate various modifications, changes, and additions within the spirit and scope of the present invention. It should be considered that the addition is within the scope of the following claims.

10: Disaster damage analysis system using drone
20: irradiated vehicle
100: drone device
110: camera module
120: sensor module
130: flight driving module
140: communication module
150: control module
200: controller device
210: communication unit
220: sensor unit
230: image correction unit
231: dictionary information setting unit
232: camera module establishment unit
233: camera module correction unit
240: image processing unit
250: damage information analysis unit
260: information input unit
270: information display

Claims (26)

A drone device for acquiring image information of the disaster-affected area by photographing a disaster-affected area from above through a mounted camera module; And
Includes a controller device for wirelessly communicating with the drone device to receive image information of a disaster-affected area from the drone device, and processing the transmitted image information through a preset automatic correction algorithm to obtain 3D modeling information.
The automatic correction algorithm,
For correcting the image information by applying preset ground reference point information and interior orientation parameters (IOPs) of the camera module mounted in the drone device to the image information obtained through the drone device Establishing an initial camera model, and then automatically adjusting the initial camera model while repeatedly adjusting the external orientation parameters (EOPs) information of the camera module through a bundle adjustment, to thereby accurately optimize the image information. Characterized by raising,
The drone device,
Obtain primary image information by obliquely shooting the disaster-affected area at a first altitude of 30m to 50m, and vertical image at a second altitude of 90m to 110m to obtain secondary image information.
Acquiring foreground image information in a wider range than the vertical shooting range by performing shooting along the centerline of a linear terrain feature in a preset range through the oblique shooting,
Using the drone, it is possible to quickly find and investigate a target area for intensive analysis of a disaster-affected area by acquiring video information for each session by performing shooting in a certain unit at intervals of 400m to 600m through the vertical shooting. Disaster damage investigation and analysis system. According to claim 1,
The interior orientation parameters (IOPs) information is a disaster damage investigation and analysis system using a drone, characterized in that it includes the location information of the pub on the photo coordinate system, the camera focal length information and the amount of distortion of the camera lens. According to claim 1,
Disaster damage investigation and analysis system using a drone, characterized in that the external orientation elements (exterior orientation parameters (EOPs)) information includes GPS location information and gimbal orientation information. delete delete delete delete delete According to claim 1,
The drone device,
A camera module provided on the main body of the device, and configured with an optical camera to photograph the disaster-affected area and generate image information of the disaster-affected area;
A sensor module provided on the main body and composed of various sensors;
It is provided on the main body, the flight driving module for operating the body floating above;
A communication module provided in the main body, for real-time wireless communication with the controller device; And
It is provided inside the main body, and transmits the image information generated by the camera module to the controller device through the communication module, and controls the camera module, sensor module, and flight driving module according to a control signal transmitted from the controller device. Control module;
Disaster damage investigation and analysis system using a drone, characterized in that comprises a. The method of claim 9,
The sensor module includes a Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS), a gyro sensor, an accelerometer and a barometer, a disaster damage investigation and analysis system using a drone. According to claim 1,
The controller device,
A communication unit for wireless communication with the drone device;
An image correction unit for correcting image information transmitted from the drone device through a preset automatic correction algorithm;
An image processing unit for acquiring 3D point cloud data, 3D terrain information and orthoimage map information by performing 3D modeling based on the image information corrected by the image correction unit;
Based on the 3D point cloud data, 3D topographic information and orthoimage map information obtained through the image processing unit, a damage information analysis unit that analyzes a damaged area of a linear topography through a predetermined analysis program and provides damage scale information ;
An information input unit for receiving various input information of a user; And
An information display unit for displaying various input/output information and image information to a user;
Disaster damage investigation and analysis system using a drone, characterized in that comprises a. The method of claim 11,
The image correction unit,
In order to acquire the absolute coordinates of the image information, ground reference point information calculated in advance using natural terrain and anti-aircraft signs and interior orientation parameters (IOPs) calculated through pre-testing of the camera module mounted on the drone device )) a preset information setting unit in which information is preset;
A camera model establishing unit for establishing an initial camera model by inputting ground reference point information and internal expression element information set in the prior information setting unit into the image information; And
A camera model correction unit that optimizes and corrects the initial camera model by repeatedly adjusting exterior orientation parameters (EOPs) information in the image information through a bundle adjustment method;
Disaster damage investigation and analysis system using a drone, characterized in that comprises a. The method of claim 11,
The controller device,
Further comprising a sensor unit consisting of a LiDAR scanner and GNSS/INS sensor,
The image processing unit fuses sensing information obtained through the sensor unit and image information corrected through the image correction unit, and performs high-precision three-dimensional image information by performing the three-dimensional modeling. Damage investigation analysis system. In the disaster damage investigation analysis method using a disaster damage investigation and analysis system using a drone device in wireless communication with the controller device provided in the investigation vehicle,
Obliquely photographing a disaster-affected area through a camera module mounted on the drone device;
Selecting the mapping area information based on the image information by transmitting the image information obtained through oblique shooting to the controller device;
Transmitting selected mapping area information to the drone device, and vertically photographing the mapping area through a camera module of the drone device;
Establishing an initial camera model by transmitting image information acquired through vertical shooting to the controller device and inputting predetermined ground reference point information to the transmitted image information;
Determining whether the camera model is included in a preset optimization condition;
If it is determined that the camera model is optimized, generating 3D image information by performing 3D modeling;
Providing the generated 3D image information to a user through a display device;
Including,
The inclined photographing is performed while driving the drone device at a first altitude of 30 m to 50 m along a center line of a linear terrain feature in a preset range, obtaining foreground image information in a wider range than the vertical photographing range,
The vertical shooting is performed at a second altitude of 90 m to 110 m while the drone device is photographed in a certain unit at intervals of 400 m to 600 m to obtain image information for each session, thereby quickly exploring the target area for intensive analysis of the disaster-affected area. Disaster damage investigation analysis method using a disaster damage investigation analysis system, characterized in that it can be found and investigated. The method of claim 14,
When the camera model is not included in a preset optimization condition, by applying the internal expression element information and the external expression element information of the camera module to the image information obtained through the initial camera model, the image information is automatically corrected by A method of calibrating a camera model, and returning to the step of determining whether the calibrated camera model is included in the preset optimization condition. The method of claim 15,
In the step of calibrating the camera model,
The interior orientation parameters (IOPs) information uses a preset fixed value through a pre-camera module test, and the exterior orientation parameters (EOPs) information is adjusted by adjusting the speed of light. Disaster damage investigation analysis method using a disaster damage investigation analysis system, characterized in that using the value. The method of claim 14,
Before the oblique imaging step,
Preset information setting step of presetting the ground reference point using absolute coordinate information measured by using a linear terrain feature and an anti-aircraft cover, and presetting internal expression element information through the camera module test of the drone device; includes Disaster damage survey analysis method using a disaster damage analysis system, characterized in that. delete delete delete delete The method of claim 16,
The interior orientation parameters (IOPs) information includes the location information of the tavern on the photo coordinate system, the camera focal length information, and the amount of distortion of the camera lens. Method of analysis. The method of claim 16,
The external orientation elements (exterior orientation parameters (EOPs)) information is a disaster damage investigation analysis method using a disaster damage investigation analysis system, characterized in that it comprises a GPS location information and gimbal direction information. The method of claim 14,
The three-dimensional image information is a disaster damage survey analysis method using a disaster damage survey analysis system, characterized in that it comprises three-dimensional point cloud data, three-dimensional terrain information and orthogonal image map information. The method of claim 14,
In the step of performing the three-dimensional modeling,
A disaster damage investigation and analysis system comprising generating sensing information obtained from a LiDAR scanner and a GNSS/INS sensor provided in the controller device and 3D modeling by combining the image information to generate high-precision 3D image information. Disaster damage analysis method used. The method of claim 14,
After performing 3D modeling to generate 3D image information, a step of providing a disaster damage analysis information to a user by analyzing a damaged area of a linear terrain feature through a predetermined analysis program based on the generated 3D image information Disaster damage investigation analysis method using a disaster damage investigation analysis system, characterized in that further included.

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