KR101771492B1 - Method and system for mapping using UAV and multi-sensor - Google Patents

Abstract

The present invention relates to a mapping method and system using an unmanned aerial vehicle equipped with a plurality of sensors and more particularly to a mapping method and system using an unmanned aerial vehicle equipped with a plurality of sensors including a video photographing apparatus, The method includes automatically generating a flight path of an unmanned aerial vehicle with respect to a predetermined mapping area using a flight object, automatically acquiring images and position and attitude data of the unmanned aerial vehicle according to the flight path, And a mapping method and system capable of improving the accuracy of position and orientation data for the image through image matching and bundle block adjustment for the image and quickly generating spatial information for the mapping area will be.
SUMMARY OF THE INVENTION The present invention has been made to solve the above problems of the prior art, and it is an object of the present invention to provide an emergency mapping method and system capable of quickly obtaining aerial photographs at a low cost without the help of professional personnel, .

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to a mapping method and a system using a UAV and a multi-

The present invention relates to a mapping method and system using an unmanned aerial vehicle equipped with a plurality of sensors, and more particularly, to a mapping method and system using an unmanned aerial vehicle equipped with a plurality of sensors including a video photographing apparatus, And automatically acquires the image and position and attitude data of the unmanned aerial vehicle along the flight path while automatically operating the unmanned aerial vehicle according to the flight path, The present invention relates to a mapping method and system capable of improving the accuracy of position and orientation data of an image by using image matching and bundle block adjustment for the image and quickly generating spatial information about the mapping area using the same.

In recent years, a variety of techniques have been applied to map production using aerial photography, which is devoid of traditional map production techniques. An example of this is the case where it is used for map creation to quickly identify the damage situation in the event of a disaster / disaster and to prepare countermeasures, or to generate spatial information for administrative / military / security purposes . In the case of producing a map using aerial photographing, it is possible to produce a map using the image taken in the vertical direction, and coordinate information or other measurement data such as GPS can be utilized together, .

For example, in Korean Registered Patent No. 10-1349148 entitled "Aerial photographing apparatus using automatic photographing system" (registered on April 1, 2014), an automatic photographing system mounted on an airship, Which is capable of quickly acquiring an image.

However, according to the related art, it takes considerable time and expense to acquire an aerial photographing image and produce a map using the aerial photographing image. In order to perform the aerial photographing, it is necessary to take an aerial photograph using an aviation aircraft or an unmanned airplane equipped with image equipment, etc., and a professional manpower capable of precisely operating the manned airplane or the unmanned airplane is required. In order to derive the map data from the aerial photographed image taken together, it is necessary to correct the photographing error and the orientation element data generated at the time of aerial photographing, so that the aerial photographic image can be processed professionally A professional manpower is needed. Accordingly, there has been a problem in that it takes a considerable amount of time and money to produce a map using the aerial photographing according to the prior art.

Accordingly, there is a demand for an urgent mapping system capable of quickly producing a map for a specific area at a low cost. For example, when a disaster / disaster occurs, the aerial photographing is used to acquire aerial photographs of the disaster / disaster area, and the map is produced using the aerial photographs to quickly and accurately grasp the disaster / And the system that can build the appropriate response system for the damage situation.

Furthermore, there is a continuing demand for high-precision mapping systems for small local areas. For example, in order to investigate illegal buildings and investigate changes in intellectual boundaries, people usually visit the site to investigate and survey the problem. However, even in such a case, The problem can be solved more efficiently.

Accordingly, there is a continuing need for a mapping method and a system capable of solving the above problems, but a proper solution has not been proposed yet.

SUMMARY OF THE INVENTION The present invention has been made to solve the problems of the prior art as described above, and provides a mapping method and system capable of quickly obtaining aerial photographs at a low cost without the help of professional manpower, .

According to one aspect of the present invention, there is provided a mapping method using an unmanned aerial vehicle equipped with a video image photographing apparatus, a position sensor, and an attitude sensor with respect to a predetermined mapping area, The system comprising: a flight path generation step of generating a flight path of the unmanned air vehicle; A data acquiring step of acquiring a plurality of images for the mapping area and position and attitude data for each image while flying the unmanned air vehicle along the flight path; An image matching step of calculating at least one conjugation point from the image and position and attitude data through image matching; A bundle block adjusting step of adjusting position and attitude data for the image through bundle block adjustment using the conjugate point and position and attitude data; And a spatial information generating step of generating spatial information on the mapping area using the image and the adjusted position and attitude data.

Here, the flight path generating step may include calculating a flight altitude of the unmanned aerial vehicle using the spatial resolution required for the mapping, the pixel size and the focal length of the image photographing apparatus; Calculating the number of strips in the flight path and the number of shot images per strip in consideration of the number of pixels of the image photographing apparatus, the pixel size and the overlapping degree between the shot images, and the shape of the mapping area; And selecting one or more waypoints to be passed by the unmanned aerial vehicle considering the flight altitude, the number of strips, and the number of shot images per strip.

The flight path may be automatically generated after a predetermined value including the spatial resolution and the shape of the mapping area is input from the user.

In the data acquiring step, the unmanned aerial vehicle may automatically acquire the plurality of images and the position and attitude data of the respective images with respect to the mapping area while operating the automatic unmanned aerial vehicle along the flight path.

The method may further include a data sorting step of sorting data to be used for spatial information by removing images and data of a quality that is difficult to utilize in generating the spatial information among the acquired plurality of images and the position and attitude data for each image can do.

The image matching step may include calculating an initial position for searching for a conjugate point in the image using the position and attitude data; Extracting a conjugate point for the image using a KLT feature tracker algorithm; And a step of confirming the accuracy of the extracted conjugation point.

Also, the step of confirming the conjugation point accuracy may include calculating and comparing crosscorrelation coefficients of the pair of image points recognized as being projected from the same object with respect to the image points and the area around the image points, (KLT) feature point tracker (KLT) algorithm, or to compare the conjugate points extracted by applying the Kanade-Lucas-Tomasi feature tracker algorithm to an image projected on one image by epipolar geometry It is possible to judge whether or not the conjugate point is accurately calculated by examining whether or not the point is projected on the epipolar line in one image.

In the spatial information generation step, the digital elevation model may be generated by using a previously generated digital elevation model or by performing dense matching to obtain a digital elevation model Spatial information can be generated from the image and the adjusted position and orientation data using a digital elevation model.

According to another aspect of the present invention, there is provided a mapping system for mapping a predetermined area to be mapped using an unmanned aerial vehicle equipped with a video photographing apparatus, a position sensor and an attitude sensor, A flight path generating unit for generating a flight path; A control unit for controlling the navigation unit to navigate the unmanned aerial vehicle along the flight path and acquiring and storing a plurality of images and position and attitude data for each image with respect to the mapping area; An image matching unit for calculating at least one conjugation point from the image and position and orientation data through image matching; A bundle block adjuster for adjusting position and attitude data for the image through a bundle block adjustment using the conjugate point and position and attitude data; And a spatial information generator for generating spatial information on the mapping area using the image and the adjusted position and attitude data.

Here, the flight path generation unit may generate the flight path information by considering the spatial resolution required for the mapping, the pixel size, the focal distance, the number of pixels, the pixel size, the overlap between the captured images, Can be automatically generated.

The image processing apparatus may further include a data sorting unit for sorting data to be used for spatial information by removing images and data of a quality that is difficult to utilize in generating the spatial information among the acquired plurality of images and the position and attitude data for each image .

Also, the image matching unit may calculate an initial position for searching for a conjugation point in the image using the position and attitude data, and then calculate an initial position for searching for a conjugate point using the KLT feature tracker algorithm The conjugate point can be extracted.

The spatial information generating unit may generate the spatial information by using a digital elevation model that has been generated in advance or by performing dense matching to generate a digital elevation model, Spatial information can be generated from the image and the adjusted position and attitude data using a digital elevation model having a value.

The control unit may further include a communication unit mounted on the unmanned aerial vehicle and automatically receiving a plurality of images stored in the control unit and determining whether the position and attitude data for each image are updated.

According to the present invention, the flight path of the unmanned aerial vehicle is automatically calculated in consideration of the spatial resolution required for the mapping and the pixel size, focal length, number of pixels, pixel size, overlap between the captured images, Position and attitude data for the mapping area while the unmanned aerial vehicle is automatically operated along the flight path to acquire the aerial photographs quickly at a low cost without the help of professional personnel And has the effect of initiating a mapping method and a system having the same.

In addition, according to the present invention, it is possible to correct the error of generated spatial information by adjusting position and attitude data for the image through image matching and bundle block adjustment for image, position and attitude data for the mapping area, Mapping method, and system.

According to the present invention, a digital elevation model (Digital Elevation Model) generated in advance or a dense matching is performed to generate a digital elevation model The present invention has the effect of disclosing a mapping method and system capable of generating spatial information more quickly by generating spatial information from the image and adjusted position and orientation data using a digital elevation model.

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.
1 is a flowchart of a mapping method using an unmanned aerial vehicle equipped with a plurality of sensors according to an embodiment of the present invention.
2 is a detailed flowchart of a flight path generation step according to an embodiment of the present invention.
3 is an explanatory diagram for calculating the flight altitude according to an embodiment of the present invention.
4 is an exemplary view of a flight path and a ground point generated in accordance with an embodiment of the present invention.
5 is an illustration of an unmanned aerial vehicle equipped with a plurality of sensors according to an embodiment of the present invention.
FIG. 6 is a detailed flowchart of the step of checking the accuracy of a conflicting point according to an embodiment of the present invention.
7 is an explanatory diagram of bundle block adjustment according to an embodiment of the present invention.
8 is a configuration diagram of an image georeferencing module according to an embodiment of the present invention.
9 is a configuration diagram of a mapping system using an unmanned aerial vehicle equipped with a plurality of sensors according to an embodiment of the present invention.
10 is an exemplary view of a flight path and a ground point generated in accordance with an embodiment of the present invention.
11 is an illustration of an actual flight path of the unmanned aerial vehicle measured according to an embodiment of the present invention.
12 is an exemplary view of an orthoimage of a mapping area generated according to an embodiment of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS The present invention is capable of various modifications and various embodiments, and specific embodiments will be described in detail below with reference to the accompanying drawings.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

The terms first, second, etc. may be used to describe various components, but the components are not limited by the terms, and the terms are used only for the purpose of distinguishing one component from another Is used.

Hereinafter, a mapping method and system using an unmanned aerial vehicle equipped with a plurality of sensors according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

First, FIG. 1 illustrates a mapping method using an unmanned aerial vehicle equipped with a plurality of sensors according to an embodiment of the present invention. As shown in FIG. 1, a mapping method using an unmanned aerial vehicle equipped with a plurality of sensors according to an exemplary embodiment of the present invention includes a mapping device, a position sensor, (S110) for generating a flight path of the unmanned aerial vehicle, the method comprising the steps of: (S110) generating a flight path of the unmanned aerial vehicle by flying the unmanned air vehicle along the flight path, A data acquisition step (S120) of acquiring a plurality of images and position and attitude data for each image, a data selection step of selecting data to be used for generating spatial information from the acquired plurality of images and position and attitude data for each image In step S130, one or more conjugation points are obtained from the image and the position and attitude data through image matching A bundle block adjustment step (S150) for adjusting position and attitude data for the image through a bundle block adjustment using the conjugate point and position and attitude data, and a bundle block adjustment step (S150) for adjusting the position and attitude data And a spatial information generating step (S160) of generating spatial information on the mapping area using the spatial information generating step (S160).

Hereinafter, a mapping method using an unmanned aerial vehicle equipped with a plurality of sensors according to an embodiment of the present invention will be described in detail.

First, a mapping system generates a flight path of an unmanned aerial vehicle (S110). In this step S110, the unmanned air vehicle creates a route to the mapping system. At this time, the mapping system calculates the spatial resolution required for generating the flight path of the unmanned aerial vehicle and the data such as the pixel size, the focal distance, the number of pixels, the pixel size, the overlap between the captured images, For example, receiving data from a user, using previously stored data, or receiving data from another system.

After the data necessary for the creation of the flight path is acquired as described above, the acquired data can be used to calculate the flight path that can effectively photograph the area to be mapped.

수 있는 바와 같이, 매핑에 요구되는 공간 해상도와 상기 영상 촬영 장치의 픽셀 사이즈 및 초점 거리를 이용하여 무인 비행체의 비행 고도를 산출하는 단계(S112)와 상기 영상 촬영 장치의 픽셀 개수와 픽셀 크기 및 촬영 영상 간의 중복도, 상기 매핑 대상 지역의 형상을 고려하여 비행 경로의 스트립 수, 스트립당 촬영 영상의 수를 산출하는 단계(S114) 및 상기 비행 고도와 스트립 수, 스트립당 촬영 영상의 수를 고려하여 상기 무인 비행체가 경유하게 되는 하나 이상의 지상점(waypoint)을 선정하는 단계(S116)를 포함하여 구성될 수도 있다.In one embodiment of the present invention, the flight path generation step (S110)

(S112) of calculating the flight altitude of the unmanned aerial vehicle using the spatial resolution required for the mapping and the pixel size and the focal length of the image photographing device, the number of pixels of the image photographing device, A step S114 of calculating the number of strips of the flight path and the number of shot images per strip in consideration of the redundancy of the images, the shape of the mapping area, and the number of strips per shot And selecting one or more waypoints via which the unmanned air vehicle passes (S116).

First, in step S112, the flight altitude of the unmanned aerial vehicle can be estimated using Equation 1 below as shown in FIG.

[Equation 1]

In step S114, the number of strips and the number of strips per strip are calculated with respect to the left lower coordinates pa1 and pa2 and the upper right coordinates pa3 and pa4 of the Minimum Bounding Rectangles (MBR) The number of images can be calculated by Equation (2) below.

&Quot; (2) "

here,

If it is determined in step S116 that the unmanned air vehicle starts from the upper left corner of the minimum area square (MBR), the first waypoint may be calculated using the following equation (3).

&Quot; (3) "

Further, the position of the m-th image of the n-th strip and the n-th strip based on the first ground point according to Equation (3) can be obtained using the following equation (4).

&Quot; (4) "

In addition, the above equations (1) to (4) are not necessarily used in generating the flight path of the unmanned aerial vehicle, and since Equations (1) to (4) are merely the contents of the present invention, But is not limited thereto.

In addition, the mapping system according to an embodiment of the present invention receives predetermined values, which are necessary for the mapping to the mapping area including the spatial resolution and the shape of the mapping area, from the user, The flight path of the unmanned aerial vehicle may be automatically generated through the step of FIG.

FIG. 4 illustrates a flight path and a waypoint generated through step S110. FIG. 4A shows a case where the spatial resolution is 2 cm, FIG. 4B shows a case where the spatial resolution is 3 cm, and the spatial resolution is changed as shown in FIG. 4, The degree of overlap between the captured images and the photographed images is adjusted, or when the shape of the area to be mapped is changed, the calculated flight path and ground points can be changed.

Next, a data acquisition step (S120) of acquiring a plurality of images for the mapping area and position and attitude data for each image while flying the unmanned air vehicle along the flight path will be discussed. In operation S120, while the unmanned air vehicle is operated using the flight path generated in the previous step S110, data obtained by measuring a plurality of images of the mapping area and a position and an attitude of the image photographing apparatus at the time when each image is captured .

5, an image capturing apparatus 520 such as a camera mounted on an unmanned aerial vehicle 510 and an unmanned air vehicle 510 according to an embodiment of the present invention, a position sensor 532 such as a GPS and an inertial measurement unit A sensor module 530 including an image sensor 534 such as an image sensor (IMU) and the like, an image sensing device 520 for sensing an image taken by the sensor module 540, such as a photographed image and the position sensor 532 and an orientation sensor 534. [ 530. The control module 540 stores the measurement data of the memory module 530 in the memory module.

The flight path generated in step S110 may be used to control the flight of the unmanned air vehicle 510. To this end, the flight path may be input to the unmanned air vehicle 510 or may be input to the control module 540 And can be used to control the flight of the unmanned aerial vehicle 510.

As described above, by controlling the flight of the unmanned air vehicle 510 using the pre-generated flight path, the user can automatically navigate the unmanned air vehicle 510 without the help of an expert, Position and attitude data for each image can be acquired.

Further, when controlling the operation of the image photographing apparatus 520, it may be controlled based on a predetermined position such as a ground point using the measurement data of the position sensor 532 such as GPS, It may be controlled by a method of periodically capturing an image according to the interval.

In addition, the position data and the attitude data measured at the photographing time of the image photographing apparatus 520 are stored in synchronization with the photographing time of the image. Accordingly, the control module 540 may include a storage device such as a flash memory for storing the photographed image, position data, and attitude data.

After the flight of the unmanned air vehicle 510 is completed, the image, position data, and attitude data stored in the storage device may be transmitted to a terrestrial system using wireless communication such as Bluetooth or wired communication and used as data for mapping. Of course, the stored image, position data, and attitude data are not necessarily transmitted after the flight is completed, but may be transmitted during the flight of the unmanned air vehicle 510 in consideration of the capacity of the storage device, It is possible.

Next, a data selection step (S130) for selecting data to be used to generate spatial information among the acquired images and position and orientation data for each image will be described. In step S130, the quality of the position and attitude data for the plurality of images and the images acquired before is checked to remove data having quality that is difficult to utilize for generating spatial information, and data having a predetermined quality or more is selected. For example, the data acquired when the image is blurred (blurred), the data acquired when the unmanned air vehicle 510 is vertically moved (takeoff and landing), data acquired by the unmanned air vehicle 510 during movement between the strips, Data to be used for generating information is selected.

Next, an image matching step (S140) for calculating one or more conjugation points from the image and position and orientation data through image matching will be described. In step S140, a conjugate point is generated for each image using the image and position and orientation data obtained in the series of steps described above.

In image matching, pairs of conjugate points representing the same object are found and extracted from multiple images. In this case, as an embodiment of the present invention, an image matching is performed using a KLT feature tracker algorithm, and furthermore, a conjugate point is obtained from the image using the measured position data and attitude data for each image By calculating the initial position to be searched, the spatial information can be generated more quickly.

The KLT feature tracker algorithm extracts feature points that can be distinguished from other regions in one image, and extracts the projected points from the same object in two images, . However, when the amount of movement between the two images is large, the performance of the KLT feature tracker algorithm may deteriorate. By considering the position data and the attitude data together, the weakness of the KLT feature tracker algorithm is complemented, The speed can be improved.

Accordingly, the KLT feature tracker algorithm can be applied to the center of the projection in the image capturing apparatus, the collinearity equation in which the projected image point and the ground point form a straight line, the external appearance element of the image obtained from the position sensor and the posture sensor Position data and attitude data) is used to estimate the position at which the feature points extracted from the image A are to be projected on the image B, and this is used as an initial position for searching the feature points of the image A in the image B. [

Furthermore, since the conjugate points extracted as described above are used as input data in the bundle block adjustment, it is desirable to have higher accuracy. Accordingly, it is possible to check whether the conjugate points are correctly extracted.

Accordingly, FIG. 6 illustrates a flowchart of a method for determining whether or not the extracted conjugated points are correctly calculated according to an embodiment of the present invention. As shown in FIG. 6, the step may include calculating and comparing crosscorrelation coefficients for a pair of image points recognized as being projected from the same object, S142), examining whether the conjugate points extracted by applying the KLT feature tracker algorithm to a pair of images match each other (S144), and comparing the pair of images with an epipolar geometry (S146) whether an object point projected on one image by the geometry is projected on an epipolar line in another image.

However, since each of steps S142 to S146 corresponds to various methods for determining the accuracy of the conjugation point, it is not necessarily required to perform all the steps as shown in FIG. 6, and only one or two steps of steps S142 to S146 .

Next, a bundle block adjustment step (S150) for adjusting the position and attitude data for the image through the bundle block adjustment using the conjugate point and the position and attitude data will be discussed. In step S150, by adjusting the bundle block using the conjugate point and the external facial expression (position data and attitude data) generated through the series of steps, the external facial expression for each image is adjusted to improve its accuracy.

In the bundle block adjustment, as shown in FIG. 7, a conjugate point is extracted from a plurality of images of a mapping area, and a plurality of reference data are used for a mapping area, The coordinates of the ground points are determined.

Here, the bundle refers to a set of rays connecting the image point and the center of the projection and the ground point, and the bundle block adjustment means that the bundle is adjusted collectively using image points projected from the same ground points between the acquired images It is a method of determining the external facial element and the ground point of the image.

)에 대한 함수로 표현될 수 있다. M은 블록에 포함된 총 영상의 개수, n은 지상점의 총 개수, EO ^j 와 X _i , Y _i , Z _i 는 각각 j번째 영상의 외부 표정 요소와 i번째 지상점 좌표를 의미한다.The mathematical model of the bundle block adjustment is based on a collinear conditional expression that a ground point corresponding to an image point and a principal point of an image pickup apparatus such as a video point and a camera exists on one straight line and j (j = 1, 2 (i = 1, 2, ..., n) th ground points on the i-th,

). ≪ / RTI > M is the total number of images included in the block, n is the total number of ground points, EO ^j , X _i , Y _i , Z _i are the outer facial elements of the j th image and the i th ground point coordinates, respectively.

&Quot; (6) "

In the collinear condition expression expressed by Equation (6), an observed value is an image point, an unknown number is an outer facial expression element and a ground point coordinate, and a relationship between an observed value and an unknown value has a nonlinear characteristic. Thus, the interval between the observed and unknown values can be linearized using Taylor series expansion and expressed as a Gauss-Markov model. Subsequently, the least square method is applied to derive the regular determinant, and the outer facial element and the ground point coordinates of the image can be calculated.

Further, in order to generate spatial information at a higher speed, an initial outer appearance element of the image calculated from the measurement data of the position sensor and the posture sensor is used as a probability constraint instead of using the ground reference point , And the bundle block adjustment is automatically performed based on the continuous adjustment, so that the spatial information of the mapping area can be calculated at a high speed.

8, the image matching process in step S140 and the bundle block adjustment process in step S150 may be combined into one software module to form a video georeferencing module You may.

In this case, the image georeferencing module can be classified into a P1M module and a P1M module. Here, the image matching (P1M) module extracts the conjugate points from the position sensor data such as image and GPS and the posture sensor data such as the IMU, and the bundle block adjustment (P1A) module extracts the position sensor data such as GPS, The position and attitude data of the instant at which the image is obtained from the attitude sensor data such as the device (IMU) and the conjugate point are precisely adjusted and output.

Finally, in a spatial information generation step (S160) of generating spatial information about the mapping area using the image and the adjusted position and attitude data, the captured image and the external appearance element (position Dimensional spatial information such as a digital elevation medel (DEM), an orthophotograph, and the like for the mapping target area using the data and the attitude data.

Here, the digital elevation model (DEM) is a model representing the altitude of the terrain, and is a model in which altitude values are assigned to each cell or point in a lattice or irregular triangular network structure at a predetermined interval with respect to a mapping area. The term "image" refers to an orthogonal projection image obtained through conversion to an image acquired by a central projection, such as an aerial photograph. Since the image acquired by the center projection of the aerial photographing or the like may include geometric distortion due to the undulation of the building or the terrain, distortion can be removed by converting it into an orthoimage image.

Conventionally, a digital elevation model (DEM) is generated through dense matching to generate a map from an aerial photographing image, and then an orthoimage image is generated using the digital elevation model to generate a map. However, It takes a considerable amount of time and computer resources to generate the digital elevation model.

Accordingly, in an embodiment of the present invention, when there is a history in which a digital elevation model has already been created, or when there is a digital elevation model generated by another person, A digital elevation model having an average altitude value on the ground is used instead of the elevation model or the dense matching is performed to generate the digital elevation model, By generating the spatial information from the attitude data, the spatial information can be derived quickly.

FIG. 9 illustrates a configuration diagram of a mapping system 900 using an unmanned aerial vehicle equipped with a plurality of sensors according to another aspect of the present invention. As shown in FIG. 9, a mapping system 900 using an unmanned aerial vehicle equipped with a plurality of sensors according to another aspect of the present invention includes a flight path generation unit 910 for generating a flight path of the unmanned air vehicle, A control unit (920) for controlling the navigation unit to navigate the air vehicle along the flight path and acquiring and storing a plurality of images and position and orientation data for each image with respect to the mapping area, A bundle block adjuster 940 for adjusting position and attitude data for the image through bundle block adjustment using the conjugate point and position and attitude data, A space for generating space information on the mapping area using the adjusted position and attitude data, And a beam generating unit 950. The image generating unit 950 removes images and data of a quality that is difficult to use in generating the spatial information among the acquired images and the position and attitude data for each image, And a data selection unit 960 that selects data to be utilized.

In this case, the flight path generation unit 910 according to an embodiment of the present invention calculates the spatial resolution required for the mapping, the pixel size, the focal length, the number of pixels, the pixel size, The flight path of the unmanned aerial vehicle is automatically generated in consideration of the shape of the mapping area, so that the captured image and the external facial expression data for the image can be acquired more quickly.

In addition, the image matching unit 930 may calculate an initial position for searching for a conjugate point in the image using the position and attitude data, and then, using the KLT feature tracker algorithm, The spatial information generating unit 950 may generate a digital elevation model by using a previously generated digital elevation model or performing dense matching to generate a digital elevation model The spatial information is generated from the image and the adjusted position and attitude data using a digital elevation model having an average altitude value of the ground so that the spatial information on the matching target area . ≪ / RTI >

Further, the control unit 920 may include a communication unit 960, which is mounted on the unmanned aerial vehicle, and determines whether the plurality of images stored in the control unit 920 and the position and attitude data of each image are updated, When the photographed image, position data, and attitude data stored in the storage device of the control unit are updated, they can be automatically received.

The data sorting unit 960 selects data to be used for generating spatial information among a plurality of images transmitted from the controller 920 and position and attitude data for each image. That is, the data sorting unit 960 examines the quality of the position and attitude data for the plurality of images and each image transmitted from the controller 920, removes data having quality that is difficult to use for generating spatial information, And selects data having a quality higher than a predetermined level. For example, the data acquired when the image is blurred (blurred), the data acquired when the unmanned air vehicle 510 is vertically moved (takeoff and landing), data acquired by the unmanned air vehicle 510 during movement between the strips, Data to be used for generating information is selected.

Hereinafter, an experimental example for verifying the present invention will be described.

FIG. 10 is an exemplary view of a flight path and a ground point for a mapping area generated according to an embodiment of the present invention. At this time, the area of the mapping area is about 340m x 300m, and it is composed of residential area and agricultural area. The spatial resolution required for the mapping was set at 3cm, so the flight altitude was 150m and the flight speed was 6m / s considering the performance of the unmanned aerial vehicle.

FIG. 11 shows an actual flight path when the generated flight path is used. It can be confirmed that it is slightly different from the scheduled flight path due to the influence of the surrounding environment, and a total of 150 images are obtained through the flight. Next, spatial information was generated using 82 images excluding unnecessary images such as images during takeoff and landing of the unmanned aerial vehicle among the 150 images. FIG. 12 shows an orthoimage image obtained from the photographed image, the position data, and the attitude data obtained through the flight.

The foregoing description is merely illustrative of the technical idea of the present invention, and various changes and modifications may be made by those skilled in the art without departing from the essential characteristics of the present invention. Therefore, the embodiments described in the present invention are not intended to limit the technical spirit of the present invention but to illustrate the present invention. The scope of protection of the present invention should be construed according to the following claims, and all technical ideas within the scope of equivalents thereof should be construed as being included in the scope of the present invention.

510: unmanned vehicle
520: image capturing device
530: Sensor module
532: Position sensor (532)
534: attitude sensor 534
540: Control module
900: Mapping system using unmanned aerial vehicle equipped with multiple sensors
910:
920:
930:
940: Bundle block adjustment section
950: Spatial information generation unit
960:

Claims (14)

There is provided a method of mapping an unmanned aerial vehicle equipped with a video image photographing device, a position sensor, and an attitude sensor with respect to a predetermined mapping target area,
A flight path generation step of the mapping system generating a flight path of the unmanned aerial vehicle;
A data acquiring step of acquiring a plurality of images for the mapping area and position and attitude data for each image while flying the unmanned air vehicle along the flight path;
An image matching step of calculating at least one conjugation point from the image and position and attitude data through image matching;
A bundle block adjusting step of adjusting position and attitude data for the image through bundle block adjustment using the conjugate point and position and attitude data; And
And a spatial information generating step of generating spatial information on the mapping area using the image and the adjusted position and attitude data,
The flight path generation step may include:
Calculating a flight altitude of the unmanned aerial vehicle using the spatial resolution required for the mapping and the pixel size and the focal distance of the image photographing device;
Calculating the number of strips in the flight path and the number of shot images per strip in consideration of the size of the photographing sensor surface of the image photographing apparatus, the overlap between the photographing images, and the shape of the mapping area; And
And selecting one or more waypoints to be passed by the unmanned aerial vehicle considering the flight altitude, the number of strips, and the number of shot images per strip. delete The method according to claim 1,
Wherein the flight path is automatically generated after receiving a predetermined value including the spatial resolution and the shape of the mapping area from a user. The method according to claim 1,
In the data acquiring step,
Wherein the unmanned aerial vehicle is automatically operated along the flight path to acquire a plurality of images for the mapping area and position and attitude data for each image. The method according to claim 1,
Further comprising a data selection step of selecting data to be used for spatial information by removing images and data of a quality that is difficult to be used for generating spatial information among the acquired plurality of images and position and orientation data for each image / RTI > The method according to claim 1,
Wherein the image matching step comprises:
Calculating an initial position for searching for a conjugation point in the image using the position and attitude data;
Extracting a conjugate point for the image using a KLT feature tracker algorithm; And
And a step of checking the accuracy of the extracted conjugate point. The method according to claim 6,
The step of confirming the consecutive points accuracy includes:
A crosscorrelation coefficient for a pair of image points recognized as being projected from the same object is calculated and compared,
We examine whether the conjugate points extracted by applying the KLT feature tracker algorithm to a pair of images match each other,
It is examined whether an object point projected on one image by an epipolar geometry is projected on an epipolar line in another image with respect to a pair of images,
And determines whether or not the conjugate point has been correctly calculated. The method according to claim 1,
Wherein the spatial information generating step comprises:
Using a previously created digital elevation model (Digital Elevation Model)
Spatial information is generated from the image and the adjusted position and orientation data using a digital elevation model having an average altitude value of the ground without generating a digital elevation model by performing dense matching Lt; / RTI > A system for mapping a predetermined area to be mapped using an unmanned aerial vehicle equipped with a video photographing device, a position sensor and an attitude sensor,
Calculating a flying height of the unmanned aerial vehicle using the spatial resolution required for the mapping and the pixel size and the focal length of the image photographing apparatus, The number of strips in the flight path and the number of images per strip are calculated in consideration of the shape of the flight path and the number of strips and the number of images per strip, A flight path generation unit for generating a flight path of the unmanned air vehicle based on the selected flight path;
A controller for generating a plurality of images and position and attitude data for each image with respect to the mapping area while controlling the unmanned aerial vehicle to travel along the flight path by itself;
An image matching unit for calculating at least one conjugation point from the image and position and orientation data through image matching;
A bundle block adjuster for adjusting position and attitude data for the image through a bundle block adjustment using the conjugate point and position and attitude data; And
And a spatial information generator for generating spatial information on the mapping area using the image and the adjusted position and attitude data. delete 10. The method of claim 9,
And a data sorting unit for sorting the data to be used for spatial information by eliminating images and data of a quality that is difficult to be used in generating the spatial information among the acquired plurality of images and the position and attitude data for each image . 10. The method of claim 9,
Wherein the image matching unit comprises:
The system calculates an initial position for searching for a conjugate point in the image using the position and attitude data and then extracts a conjugate point for the image using a KLT feature tracker algorithm. Mapping system. 10. The method of claim 9,
Wherein the spatial information generating unit comprises:
Using a previously created digital elevation model (Digital Elevation Model)
By using a digital elevation model having an average altitude value of the ground without generating a digital elevation model by performing dense matching,
And generates spatial information from the image and the adjusted position and attitude data. 10. The method of claim 9,
The control unit is mounted on the unmanned air vehicle,
Further comprising a communication unit for determining whether to update a plurality of images stored in the control unit and position and attitude data for each image, and automatically transmitting the determined position and attitude data.

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