KR101839599B1 - Road facility surveying system using drone - Google Patents

Abstract

According to the present invention, a road facility measuring and inspecting system using a drone comprises: a drone system configured to enable a user to search for a route to a destination without boarding of a pilot or to photograph a road facility by using an unmanned aircraft and transmit photographed image data in real time wherein the unmanned aircraft enables short distance flight within a wireless control range by an unmanned controller; and a road information building system configured to generate a road layer by using the image data with respect to a road facility transmitted from the drone system and build a road model.

Description

[0001] ROAD FACILITY SURVEYING SYSTEM USING DRONE [0002]

The present invention relates to a road facility surveying system using a drone, and more particularly, to a road facility surveying system using a drone, and more particularly, to a road facility surveying system using a drone, A drone system for photographing a facility and transmitting photographed image data in real time; And a road information construction system for generating a road layer using image data of the road facilities transmitted from the drones system and constructing a road model, The present invention relates to a road facility surveying system using a drone, which can generate a more accurate road model by reflecting the inclination of road data.

As the modern society has become more complicated and advanced, the importance of geospatial information among various knowledge information has been increasing for efficient utilization and management of the national land space.

The field of using spatial information is a world-wide interest industry, and Internet-based map service and 3D geographic information service are already provided by Google and Microsoft.

In addition, the three-dimensional geographical information software industry market has been developed by functional subdivision. The diversity and expertise of geographic information utilization field creates the application field of geographic information system (GIS) based technology Contributed.

Analysis techniques using aerial photographs are increasing to generate more realistic and accurate geospatial information by using more accurate analysis tools such as aerial photographs and aerial laser survey data.

In addition, although aerial photographs provide rich texture information for the surface, there are disadvantages such as shadows and undulating displacements. Airborne laser surveying data provides accurate 3D terrain coordinates as points, but does not provide rich texture information .

Therefore, in order to provide more accurate and realistic spatial information, it is necessary to compensate for the disadvantages of the aerial photograph or airborne laser survey data or to merge the advantages.

However, the related art has a problem in that it can not provide a technique for producing a higher quality digital map using airborne laser survey data and generating a more accurate road model by reflecting the inclination of road data.

Korean Patent No. 10-0905497

It is an object of the present invention to provide a navigation system capable of capturing a road facility by using a unmanned air vehicle capable of navigating a route to a destination by itself or by a radio navigator in a radio control range, In real time; And a road information construction system for generating a road layer using image data of the road facilities transmitted from the drones system and constructing a road model, And to provide a road facility surveying system using a drone capable of generating a more accurate road model reflecting the inclination of the road data.

According to an aspect of the present invention, there is provided a system for surveying road facilities using a dron according to the present invention, which is capable of searching for a route to a destination by itself or by using a maneuvering vehicle capable of short- A drone system for photographing road facilities and transmitting the photographed image data in real time; And a road information building system for building a road layer using image data of road facilities transmitted from the drones system and building a road model.

The present invention has the technical effect of producing a more accurate digital map using airborne laser survey data and generating a more accurate road model reflecting the inclination of the road data.

1 shows a main configuration of a road facility surveying system using a drone according to the present invention.
2 shows a main configuration of the drones system according to the present invention.
FIG. 3 shows a main configuration of a road information construction system according to the present invention.
4A is a block diagram of the data classification unit and a data classification process of the configuration of FIG.
FIG. 4B is a block diagram and road data acquisition process of the road data acquiring unit in the configuration of FIG. 3. FIG.
5 shows a main configuration of a road information construction system according to the present invention.
6 is a block diagram of an unpacked road recognition system using a recursive adaptive filter according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

1 shows a main configuration of a road facility surveying system using a drone according to the present invention.

Referring to FIG. 1, a road facility surveying system using a dron according to the present invention includes a dron system 100 and a road information management system 200.

The drones system 100 is an unmanned aerial vehicle (UAV) capable of locating a route to a destination by itself or by a radio navigator within a radio control range without boarding a pilot, The first road facilities 10-1 to the nth road facilities 10-n are photographed and the photographed images are transmitted to the road information management system 200 in real time, which will be described later in detail with reference to FIG.

In this case, the first road facilities 10-1 to the nth road facilities 10-n include roads, bridges, tunnels, parks, and other infrastructure facilities.

The image data photographed by the drone system 100 is transmitted to the road information management system 200 on the basis of AVB (Audio Video Bridge), and the road information management system 200 transmits the received image data to the block- And stores it in a storage mode.

In addition, the block parallel direct storage method can selectively control each disk by using the unique MAC address information of the board, and it is possible to control each disk in parallel without decreasing the throughput and the frame size The storage speed can be increased.

In the case of the file unit storage method, unnecessary header information of a packet and a continuous input / output (I / O) signal are generated, a transmission amount and a frame are reduced while a file system transmits a packet, By storing in a sequential manner rather than in a sequential manner, the storage speed is slow compared to the slow.

Next, the road information management system 200 includes a road information construction system 300 and a road information update system 400.

The road information construction system 300 generates a road layer using the image data of the road facilities transmitted from the drone system 100, and a detailed description thereof will be given later with reference to FIGS. 3 to 4B.

The road information update system 400 reliably updates related data of the corresponding road infrastructure in the digital topographic map according to the change of the road infrastructure using the image data of the road facilities transmitted from the dron system 100, A detailed description will be given later with reference to FIG.

2 shows a main configuration of the drones system according to the present invention.

Referring to FIG. 2, the dron system 100 according to the present invention includes a battery measuring unit 110, a charging facility searching unit 120, a wireless charging unit 130, a flight adjusting unit 140, a photographing unit 150, A measurement unit 160, a communication unit 170, a calculation unit 180, and a dron control unit 190.

The battery measuring unit 110 measures in real time how much the remaining amount of the battery in the UAV is.

The charging facility searching unit 120 searches for a charging station (not shown) located within a predetermined radius area from the position information measured by the position measuring unit 160.

Here, the charging facility searching unit 120 controls the communication unit 170 to receive a message transmitted from a charging station (not shown) located within a predetermined radial area from the location information to search for the charging station (not shown) can do.

The wireless charging unit 130 is for charging the battery in the UAV in a wireless manner. When the UAV is placed in any one of the stations provided in the charging station (not shown) The battery in the UAV can be charged.

In addition, the wireless charging unit 130 may include a predetermined internal coil, and may be configured to wirelessly charge the battery using an induced current generated in the internal coil due to an electromagnetic field generated by a station of a charging station Charging.

The flight coordinator 140 allows the flight vehicle to automatically implement a plurality of flight patterns in the air through a multi-command mode, where the flight patterns can be, for example, acro flight, stabilize flight, circle ), Follow me flight, geo fence and auto flight, and also receives the position information from the GPS satellites to make the flight within the pre-set cost zone. You can also adjust it.

The photographing unit 150 may include at least one of an infrared camera, an infrared camera, an image camera, and a fish-eye lens camera on one side of the UAV body, -1 to 10-n.

In this case, the camera module is a module for processing a photographed image or a moving image so as to be stored or transmitted, such as a lens, an image sensor, an amplifier, an analog-to-digital converter and an image signal processing processor have.

 The position measuring unit 160 receives a GPS position information about a point currently flying from the satellite by mounting a GPS receiver, and transmits the GPS position information to the outside to track the current flying position. The GPS (Global Positioning System) The location information includes information about the latitude, longitude, and altitude of the current flight spot.

The communication unit 170 provides an interface for performing remote or short-range communication with the charging station (not shown). For example, the communication unit 170 may be a 3G, 4G, LTE, LTE-A, WiBro, (WiFi) or the like may be used. As the short distance communication module, NFC (Near Field Communication), Bluetooth, Zigbee, UWB (Ultra-wideband) .

In this case, the communication unit 170 transmits the location information of the charging station (not shown) and the identification information of each of at least one or more stations provided to the charging station (not shown) , The output voltage, and the current.

The calculation unit 180 calculates the estimated charging time until the charging of the battery is completed based on the message received from the communication unit 170 and the capacity of the battery to be charged and the charging terminal voltage of the UAV do.

Also, the calculation unit 180 may calculate the estimated (estimated) time to arrive at the charging station (not shown) using the predetermined speed value of the unmanned aerial vehicle (UAV) and the location information of the charging station You can also calculate flight times.

The drone control unit 190 includes a battery measuring unit 110, a charging facility searching unit 120, a wireless charging unit 130, a flight adjusting unit 140, a photographing unit 150, a position measuring unit 160, a communication unit 170, And the calculation unit 180 are controlled.

 FIG. 3 shows a main configuration of a road information construction system according to the present invention.

3, the road information construction system 300 includes an air data reception unit 310, a data processing unit 320, a database unit 330, and a display unit 340.

The air data receiving unit 310 receives the airborne laser measurement data and the aerial photograph data from the image data photographed by the dronesystem 100.

Here, the aerial laser measurement data refers to information relating to a point where an airborne laser surveying system is mounted on an airplane, a laser pulse is scanned on the ground surface, and reflection is performed using the arrival time of the reflected laser wave.

For example, the positional coordinate value by the laser measurement, the X-axis or Y-axis rotation amount error information generated in the airborne laser measurement, the distance information between the coordinate value of the entry point of the airborne laser surveying data and the building outermost ground survey coordinate value, Factor, a constant height difference information between the ground reference point and the aerial laser surveying data, and the like.

The airborne laser surveying data is acquired in the form of a point cloud, where a point cloud is a set of tens or millions of points representing an object. In the present invention, the term point cloud data refers to the airborne laser surveying data have.

 The aerial photograph data refers to two-dimensional coordinate information and attribute information that can be obtained from an aerial photograph.

For example, in the case of calculating the ground coordinates with respect to the position in the aerial photograph, the inter-facial expression information, which is a variable used in the creation of the three-dimensional model, and the inner facial expression point, which is a variable used in calculating the physical coordinate system of the camera, An external facial expression element which is a constant of a mathematical expression used when calculating the ground coordinate value corresponding to an arbitrary position on the aerial photograph when the absolute facial expression information, the mutual facial expression and the absolute facial expression operation are completed is used.

The data processing unit 320 receives the at least one of the aerial photograph data or the aerial laser surveying data from the air data receiving unit 310 to generate more accurate and efficient spatial information, Data, and generates the urban space information through image processing on the classified road data and the building data, and includes a data classifying unit 321 and a road data acquiring unit 322 for realizing the urban space information.

Here, the data classification unit 321 can perform image processing according to each data characteristic when classified according to the characteristics of input data, so that more accurate data can be obtained. For example, And building data, which can be used for digital mapping.

Meanwhile, the data classification unit 321 can classify the road data and the building data by removing the error from the data received from the air data reception unit 310 and applying a filtering process, which will be described in detail with reference to FIG. 4A.

4A is a block diagram of the data classification unit and a data classification process of the configuration of FIG.

Referring to FIG. 4A, the data classifier 321 includes an error removing unit 321a, a filtering unit 321b, and a data extracting unit 321c.

The error removing unit 321a may set the search area in consideration of the average height of the target area and the regular lattice spacing.

After setting the search area, data having a large error can be removed by searching for and removing excessively high or low points by comparing with surrounding points in the search area.

For example, points having coordinate values that deviate from a predetermined error value range based on the coordinate values of neighboring points can be removed at the time of data classification, and by removing data having a large error value in advance, more accurate data can be obtained .

The filtering unit 321b may classify the data into road data and building data by performing a first filtering process on the data from which the error is removed.

For example, a method of searching a minimum value among height values of a point within a specific region, setting only a point having a height value greater than the minimum value as a comparison target value, and comparing the comparison target value with a predetermined threshold value .

The points where the comparison target value is larger than the threshold value may be classified as building data and the points where the comparison target value is less than or equal to the threshold value may be classified as road data. have.

[Equation 1]

Here, R _ij denotes an arbitrary cell in a specific area, R l - _min denotes a minimum height value in a specific area at the l-th repetition number, and T denotes a threshold value.

Here, the threshold value may be a value measured by an experiment or may mean the minimum value. The threshold value T may be determined by the following equation (2).

&Quot; (2) "

Here, s represents the maximum slope of the region, M _l represents the window size of the number of repetitions l second.

By repeating this process, the data extracting unit 321c can gradually classify and extract road data and building data.

Next, the road data acquiring unit 322 distinguishes the road data in the flat area from the road data in the inclined area, sets the variables for continuity search of the road data by reflecting the inclination of the inclined area, Which will be described below with reference to FIG. 4B.

FIG. 4B is a block diagram and road data acquisition process of the road data acquiring unit in the configuration of FIG. 3. FIG.

Referring to FIG. 4B, the road data obtaining unit 322 of the present invention includes a variable calculating unit 322a, a threshold comparing unit 322b, an inclination determining unit 322c, and a road data determining unit 322d.

The variable calculating unit 322a calculates a variable used for searching for an area that violates the continuity of the points constituting the paper surface among the road data classified from the data classifying unit 321. [

For example, the data on the ground in the area divided by the predetermined size of the gratings has a similar distribution of the height difference with respect to the other data in the lattice.

In the case of the present invention, experiments were conducted using real data to determine the allowable height difference in a flat area, and in the case of constructing the road data with data having a height difference difference of at least 8% .

Therefore, the height parameter H can be obtained by dividing the difference between the height value of the highest point and the height value of the lowest point in one lattice by the lattice size as shown in the following equation (3).

&Quot; (3) "

Here, H denotes the height variable, Max (h) denotes the height value of the highest point in the lattice, Min (h) denotes the height value of the lowest point, and L denotes the lattice size.

The threshold value comparator 322b can obtain more precise road data by comparing the obtained height variable H with a predetermined threshold value.

For example, when the optimal threshold value of 8% according to the experimental result is applied, if H > 0.08 in Equation (3), the grid is determined to be unsuitable for classification into road data, and if H & It can be judged to be suitable for classification.

If it is determined that the grid data is suitable for classification into road data, the threshold value comparator 322b transmits the data determined to be appropriate to the road data determiner 322d.

On the other hand, if it is determined that the data in the corresponding grid is not suitable for classifying into the road data, the data in the corresponding grid is transmitted to the tilt determination unit 322c.

The slope judging unit 322c calculates the slope of the data judged as unsuitable, and transmits the calculated slope to the variable calculating unit 322a.

The modified variable height mod_H is calculated by the variable calculating unit 322a using the inclination input from the inclination determining unit 322c. In this case, the modified height variable mod_H is calculated by the following equation (4) Lt; / RTI >

&Quot; (4) "

Here, mod_H denotes a modified height variable, H denotes an already obtained height variable,? Denotes an inclination angle, and C denotes an inclination factor.

At this time, the slope factor C may be a value considering a slope of a general actual slope topography, for example, a value of 0.7 according to the present experiment.

The modified variable mod_H can be obtained by reflecting the inclination of the slope terrain by the above Equation 4. The modified variable mod_H thus obtained is transmitted again to the threshold value comparator 322b, The loop of the previous process is turned again according to the comparison result of the threshold value comparator 322b, or is finally transmitted to the road data decision unit 322d.

The road data determination unit 322d receives only the data determined as suitable road data through the threshold comparison unit 322b, thereby determining the finally received data as the road data.

3, the database unit 330 stores aerial laser measurement data and aerial photograph data, and provides information necessary when the data processing unit 320 generates two-dimensional or three-dimensional spatial information, And stores the two-dimensional or three-dimensional spatial information generated from the data processing unit 320.

The display unit 340 outputs the two-dimensional or three-dimensional spatial information obtained from the data processing unit 320.

For example, the display unit 340 may output the result of the two-dimensional or three-dimensional numerical value display to the user.

In this case, the numerical illustration refers to the work of measuring the topographic objects of surveying aerial photographs or satellite images as numerical data by an analytical drawing system and recording them on a computer. The aerial photographing system is a digital photogrammetric It refers to a device that reads aerial photographs taken using equipment.

Also, the digital map refers to a digital map obtained by digitizing the information held by a paper map into a set of geometric graphic elements or pixels of a point, line, and surface form, and is a 2D map or a digital map obtained by the present invention. Three-dimensional spatial information can be used for numerical representation.

5 shows a main configuration of a road information construction system according to the present invention.

5, the road information construction system 400 according to the present invention includes a facility storage module 410, a digital terrain storage module 420, a data collection module 430, an update object confirmation module 440, Module 450, a map module 460, and a reporting module 470.

The facility storage module 410 stores and manages information data composed of names, objects, uses, locations, specifications, and facility history of roads, bridges, tunnels, parks, and other infrastructure.

The digital terrain storage module 420 stores and manages the road infrastructure-related map data linked with the information data.

The data collection module 430 interlocks with the road information construction system 300 and searches and collects the facility data stored in the database DB of the road information construction system 300.

The update target confirmation module 440 compares the facility data received by the data collection module 430 with the information data of the facility storage module 410 to confirm the update target information data, And the digital terrain storage module 420 searches.

In other words, the update target confirmation module 440 identifies the name of the road infrastructure, the unique code, and the like from the facility data, and searches the information storage data of the facility storage module 410 based on the name.

Next, the identified and searched facility data and information data are grasped and the information of the corresponding road infrastructure is compared, and the difference is confirmed.

Next, when the difference between them is confirmed, the corresponding information data is determined as the update object information data, and the update object confirmation module 440 updates the road infrastructure related map data confirmed by the update object information data to the digital terrain storage module 420).

In addition, the update object confirmation module 440 processes the digital terrain image, which is the map data retrieved from the digital terrain storage module 420, for the source image analysis to identify the road state, Furthermore, the road is divided into the unpacked road and the unpacked road. In this case, the unpacked road recognition method using the recursive adaptive filter will be described later with reference to FIG.

To be more specific, the update target confirmation module 440 processes the digital terrain image so that brightness can be confirmed for each color, and converts the digital terrain image into a contrast image.

Next, the update target confirmation module 440 identifies the lightness of the portions of the light and dark areas, determines the lightness within the reference value range to be the same lightness, and identifies the same lightness range as the same lightness region.

Here, the update target confirmation module 440 divides the grade of lightness to distinguish the pavement road from the unpaved road.

For example, the renewal object confirmation module 440 regards the interval having the brightness of '35' or less as a pavement and the brightness having a brightness exceeding '35' based on the relative value classified as '0 to 100' The sections are distinguished by considering them as unpaved roads.

The update processing module 450 updates the information data and the map data to be updated.

The map module 460 edits the map image of the map data updated by the update processing module 450 so that the operator can confirm the map image, and outputs the edited map image through a designated terminal (not shown).

The reporting module 470 automatically generates a list of road infrastructure related to the updated information data, and transmits the list to the designated terminal (not shown) so that the operator can confirm the list.

Thus, the present invention can reliably update the corresponding road infrastructure in the digital topographic map according to the change of the road infrastructure.

6 is a block diagram of an unpacked road recognition system using a recursive adaptive filter according to an embodiment of the present invention.

Referring to FIG. 6, an unpackaged road recognition system using a recursive adaptive filter according to the present invention includes a segmentation block 50, a road boundary linearisation block 60, a road prediction block 70, and a parameter update block 80 .

In this case, the segmentation block 50 is made up of a neural network structure (MLP), for example, and first uses the sample pixels f (n) provided from the camera 11 via the sample delay block 14 to perform image segmentation (K) for the red (RED (RXY), green (GREEN), and blue (BLUE (BXY)) from the camera 11, ), And then separates the road and non-road areas using the learned neural network structure (MLP) using only the road prediction shape area provided by the road prediction block 70 through the sample delay block And obtains the image segmentation result w (k) and transmits it to the road boundary linearisation block 60. [

Since the image segmentation result w (k) received from the segmentation block 50 is not generally linear, the road boundary linearisation block 60 performs linear segmentation that satisfies Least Mean Square Estimation (LMSE) for linear trapezoidal modeling The road model y (k) is approximated to transmit the model, which is corner points, to the road prediction block 70 and the sample delay block 14.

The road prediction block 70 is made up of a Kalman filter and is provided with a linear road model y (k) from the road boundary linearisation block 60 and receives the following data from this linear road model y (k) (K + 1 / k) and the error range S (k + 1) for the delay block 12 and transmits it to the delay block 12.

And when the next data comes to the segmentation block 50, this data is used for the search area to recognize the unpaved road.

The road prediction block 70 generates a road observation update result ((k (k)) which is an optimized approximation of the road segmentation result w (k) road model which is the current result of the previous road prediction model and x (k / k)).

This road model is the last result of the system, that is, the road shape retrieved, and the difference between the results of the last result and the linear part is calculated after the sample delay.

This error is then used to adapt the learned neural network structure (MLP) by using standard back-propagation, and the result of the linearization block and the final result are also passed through the sample delay to the parameter update block 80 .

The road prediction block 70 also transmits the linear road model y (t) and the road shape x (t / t) in units of time to the parameter update block 80 through the visual delay block 13 , And transmits the sample road prediction shape (x (n + 1 / n + 1)) to the sample delay block 14. [

The parameter update block 80 periodically updates the system parameters by applying an Expectation Maximization (EM) algorithm to optimize the system performance, or updates the system parameter from the road prediction block 70 to the linear road model y ) And the road shape x (t / t) to calculate and update the local optimal parameter p (t) and transmit it to the road prediction block 70.

Thus, the present invention estimates the road shape of the next frame from the data up to the previous frame and the image data of the current frame using the Kalman filter and the maximum prediction (Expectation Maximization) algorithm of the image signal provided from the camera, It is possible to reduce the amount of calculations and to update the system parameters periodically to visually track the pavement or the unpaved road so that it is possible to realize a system that can be applied to various road environments, .

While the present invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiments. It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit of the invention.

100: Dron system
200: Road information management system
300: road information building system
400: road information update system
10: Road Facilities Group
10-1 ~ 10-n: First road facility ~ nth road facility

Claims (4)

A drone system for photographing road facilities and transmitting the photographed image data in real time using a manned unmanned aerial vehicle capable of navigating the route to the destination by himself or by a radio controllable range within a radio control range without boarding the pilot;
A road information building system for building a road layer using image data of road facilities transmitted from the drone system and building a road model; And
And a road information updating system for updating the data of the road infrastructure in the digital map according to the change of the road infrastructure using the image data of the road facility transmitted from the drone system in cooperation with the road information building system,
The drones system,
A photographing unit for photographing the road facilities by providing at least one camera out of an infrared camera, an infrared camera, an image camera, and a fish-eye lens camera on the front, rear, top, and bottom sides of the unmanned aerial vehicle body;
A battery measuring unit for measuring in real time how much the remaining amount of the battery in the unmanned air vehicle exists;
A position measuring unit for mounting a GPS receiver in the unmanned aerial vehicle to receive GPS position information about a current flying position from the satellite and transmitting the GPS position information to the outside to track the current flying position;
A charging facility searching unit searching for a charging station located within a predetermined radius area based on the position information measured by the position measuring unit;
A wireless charging unit that wirelessly charges the battery in the unmanned air vehicle from the station where the unmanned air vehicle stays in any of the stations provided in the charging station;
A flight control unit for allowing a flight vehicle to automatically implement a plurality of flight patterns in the air through a multi-command mode;
And a control unit for controlling the charging and discharging of the battery pack based on the estimated charging time until the charging of the battery is completed based on the charging terminal voltage of the unmanned air vehicle and the capacity of the battery to be charged, And a calculation unit for calculating an estimated time of flight until arriving at the station,
The image data photographed by the drone system is transmitted to the road information building system based on an AVB (Audio Video Bridge), and the road information building system stores the received image data in a block unit parallel direct store manner. Surveying system for road facilities using drones. delete The road information system according to claim 1,
An air data receiving unit for receiving the airborne laser measurement data and the aerial photograph data from the image data photographed by the drone system;
A data classifying unit for removing an error from the aerial laser survey data and the aerial photograph data received from the aerial data receiver and classifying the aerial image data into road data and building data by applying a filtering process; And
A road data acquiring unit for distinguishing road data on a flat area from road data on a sloping area and setting a variable for continuity search of the road data reflecting the inclination of the inclined area so as to acquire more accurate road data Wherein the drones are installed in the vicinity of the road. 4. The apparatus according to claim 3,
An error eliminating unit for setting a search area in consideration of an average elevation of a target area and a regular lattice spacing and then searching abnormally high or low points in comparison with neighboring points in the search area to remove data with large errors;
A filtering unit that performs a first filtering process on the data from which the error is removed through the error eliminating unit to classify the data into road data and building data; And
And a data extracting unit for extracting road data and building data classified through the filtering unit.

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