JP6465421B1 - Structural deformation detector - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a structure deformation detection device for easily detecting even a minute scale deformation occurring in any part of any structure including a road.
A structure deformation detection system includes a measurement vehicle 10 that measures a road structure and its surrounding terrain and shape while traveling on a road, and various types based on the terrain and shape measured by the measurement vehicle 10. And a structure deformation detection device 20 that generates data and detects the damage of the road structure.
[Selection] Figure 1

Description

  The present invention relates to a structure deformation detection apparatus, and more particularly to a structure deformation detection apparatus using point cloud data.

Japan's road construction began in earnest since 1956 and peaked during the period of high economic growth. However, aging has become a problem at the present time after construction.
Especially in the pavement field, in recent years, the probability of accidents due to road surface deformation due to aging of pavement has increased, and this is a problem directly related to safety during driving, so it is an urgent need to respond to it. .
Such road surface deformations that can lead to accidents include potholes, rutting, reduced flatness, or cracks.
Of these, the potholes are local depressions caused by the scattering of aggregates, and there is a risk that the tires may puncture or the aggregates may be wound up and damage the vehicle body as the car travels over them. .
Since this pothole is a minute scale road surface deformation, it is difficult to find it without carefully checking the road surface condition. Therefore, at present, it is necessary for the person in charge of the road surface confirmation to perform a visual inspection on the site while moving at a low speed with a patrol car or on foot, which is problematic in terms of efficiency.
In addition, it was almost impossible to find a very small pothole that has not been deteriorated until it is visible.

As one of the conventional techniques for detecting the road surface deformation as described above, a structure deformation detection system disclosed in Patent Document 1 has been proposed.
The structure deformation detection system disclosed in Patent Document 1 includes an axle vibration sensor that detects vibration of an axle of each wheel, and at least two displacement detection sensors that detect displacement of the road surface by laser irradiation. Provided.
The structure deformation detection system disclosed in Patent Document 1 calculates the flatness and the level difference of the step particularly on the joint portion of the road surface based on the vibration data and the displacement data detected by the axle vibration sensor and the displacement detection sensor, respectively. Is.

JP2018-4469

However, in the structure deformation detection system of Patent Document 1, the detection target is a joint portion of the road surface, and the presence of the detection target itself can be easily confirmed. Since there is no configuration for detection, there is a problem that the detection is difficult.
For example, when a pothole is present on a road surface where a vehicle wheel does not travel, the structure deformation detection system of Patent Document 1 cannot detect vibration corresponding to the pothole, and the pothole. Is difficult to detect.

  The present invention has been made in view of the above problems, and provides a structure deformation detection device that easily detects even a minute scale deformation occurring in any part of any structure including a road. To do.

In order to achieve such an object, the present invention is a structure deformation detection device for detecting deformation on the surface of a structure, wherein the structure and the terrain are represented by a point cloud that is a set of a large number of points, A point cloud data input unit that inputs point cloud data including the spatial coordinates of each measurement point that constitutes, and a structure that extracts or calculates coordinates perpendicular to the surface of the structure based on the input point cloud data A coordinate calculation unit, a Fourier transform processing unit that performs a short-time Fourier transform on a signal indicated by a plurality of coordinates perpendicular to the surface of the structure, and converts the signal into a sum of periodic functions of each angular frequency, and a detection purpose extract the Fourier amplitude spectrum contained Deformation of the angular frequency band, when the extracted Fourier amplitude spectrum is equal to or larger than the threshold, and a variable shaped detecting unit for outputting information indicating that there is a variable-shaped detection purposes The Fourier transform processing unit From a plurality of coordinates in the direction perpendicular to the surface of the structure located on the line drawn on the surface of the object, a predetermined number of consecutive coordinate sets are different from each other in the combination of consecutive coordinates included in the set. A plurality of times, a short-time Fourier transform is performed on the signal indicated by the predetermined number of coordinate sets, and the set of coordinates extracted a plurality of times includes a set adjacent to the starting line side of the drawn line and A predetermined number of coordinates are common to the pair adjacent to the end line side .
Further, the present invention is a structure deformation detection device for detecting a structure surface deformation, wherein the structure and the terrain are represented by a point cloud which is a set of a large number of points, and each measurement point constituting the point cloud A point cloud data input unit that inputs point cloud data including the spatial coordinates of the structure, a structure coordinate calculation unit that extracts or calculates coordinates in a direction perpendicular to the surface of the structure based on the input point cloud data, A Fourier transform processing unit that performs a short-time Fourier transform on a signal indicated by a plurality of coordinates perpendicular to the surface of the structure and converts the signal into a sum of periodic functions of each angular frequency, and a deformed angle for detection purposes A Fourier amplitude spectrum included in the frequency band is extracted, and when the extracted Fourier amplitude spectrum is equal to or greater than a threshold value, a deformation detection unit that outputs information indicating that there is a deformation for detection purposes is included, and the structure coordinate calculation is performed. The inspection area on the surface of the structure Divide into a number of minute areas, and the representative value of the minute area, which is the average value of the coordinates in the perpendicular direction to the surface of the structure of the measurement points of the point group included in the minute area, is the coordinate perpendicular to the surface of the structure. As a feature of calculating for each minute region.

  In addition, according to the present invention, the deformation detection unit outputs information indicating that there is a deformation for detection purposes when the maximum value of the extracted Fourier amplitude spectrum is equal to or greater than a threshold value.

  In addition, according to the present invention, the deformation detection unit outputs information indicating that there is a deformation for detection purposes when the average value of the extracted Fourier amplitude spectrum is equal to or greater than a threshold value.

In addition, according to the present invention, the structure coordinate calculation unit divides the inspection area on the surface of the structure into a plurality of minute areas, and the measurement points of the point group included in the minute area are perpendicular to the surface of the structure. A representative value of a minute area, which is an average value of coordinates, is calculated for each minute area as a coordinate in a direction perpendicular to the surface of the structure.

  In addition, according to the present invention, image data colored stepwise based on a representative value of a minute region is generated.

Further, according to the present invention, in the representative value of the minute region located on the line drawn on the surface of the structure, the i-th, i + a-th, i + b-th (1 ≦ i) from the start line side of the drawn line. Assuming that the representative values of the microregions of ≦ n, 0 <a <b) are h _i ¹ , h _i ² , h _i ³ , the deformation detection unit calculates the value of Hi based on the following equation 1,
(Formula 1)


The flatness σ of the surface of the structure is calculated based on the calculated value of Hi and the following formula 2.
(Formula 2)

According to the present invention, the deformation detection unit calculates an IRI value based on the calculated flatness σ and the following expression 3.
(Formula 3)
IRI = 1.33σ + 0.24

  It should be noted that any combination of the above-described constituent elements, or those obtained by replacing the constituent elements and expressions of the present invention with each other among methods, apparatuses, systems, computer programs, recording media storing computer programs, and the like are also included in the present invention. It is effective as an embodiment of

  The present invention is a structure deformation detection device for detecting deformation of a structure surface, wherein the structure and the terrain are represented by a point cloud that is a set of a large number of points, and the space of each measurement point constituting the point cloud. A point cloud data input unit for inputting point cloud data including coordinates, a structure coordinate calculation unit for extracting or calculating coordinates in a direction perpendicular to the surface of the structure based on the input point cloud data, and a structure Included in the Fourier transform processing unit that performs Fourier transform on the signal indicated by multiple coordinates in the direction perpendicular to the surface of the surface and converts the signal to the sum of the periodic functions of each angular frequency, and the deformed angular frequency band for detection purposes If the extracted Fourier amplitude spectrum is greater than or equal to the threshold value, the deformation detection unit outputs information indicating that there is a deformation for detection purposes. That happened in a place Even Deformation of small scale can be easily detected.

It is a figure which shows the structure of the structure deformation detection system in the 1st Embodiment of this invention. It is a figure which shows the structure of the measurement vehicle in the 1st Embodiment of this invention. It is a figure which shows the external appearance of the measurement vehicle in the 1st Embodiment of this invention. It is a figure which shows the structure of the structure deformation detection apparatus in the 1st Embodiment of this invention. It is a figure which shows the database which the information storage part in the 1st Embodiment of this invention stores. It is a figure which shows an example of the data structure of point cloud DB in the 1st Embodiment of this invention. It is a functional block diagram of the structure change detection apparatus in the 1st Embodiment of this invention. It is a flowchart which shows the flow of the whole operation | movement by the structure information provision system in the 1st Embodiment of this invention. It is a figure for demonstrating that the space coordinate in the 1st Embodiment of this invention is projected on a plane, and a plane coordinate is calculated | required. It is a figure which shows the example of a display of the stereo image by the point cloud data in the 1st Embodiment of this invention. It is a flowchart which shows the flow of the production operation of the contour by the structure deformation detection apparatus in the 1st Embodiment of this invention. It is a figure which shows the specific example of area | region designation | designated of the sample in the 1st Embodiment of this invention. It is a figure for demonstrating the mesh in the 1st Embodiment of this invention. It is a figure which shows an example of the contour which the structure deformation detection apparatus produced in the 1st Embodiment of this invention. It is a flowchart which shows the flow of the calculation operation | movement of the flatness by the structure deformation detection apparatus in the 1st Embodiment of this invention. It is a figure which shows an example of the contour which the structure deformation detection apparatus in the 1st Embodiment of this invention displays, and the location which calculates flatness is shown. It is a figure for demonstrating the Fourier-transform in the 1st Embodiment of this invention. It is a flowchart which shows the flow of the detection operation of the pothole by the structure deformation detection apparatus in the 1st Embodiment of this invention. It is a figure which shows the detection method of the pothole using the short-time Fourier transform in the 1st Embodiment of this invention. It is a figure which shows the detection method of the pothole using the short-time Fourier transform in the 1st Embodiment of this invention. It is a flowchart which shows the flow of evaluation operation | movement of a road structure by the structure deformation detection apparatus in the 1st Embodiment of this invention. It is a flowchart which shows the flow of the deformation | transformation evaluation operation | movement in the road structure using the AA value in the 1st Embodiment of this invention. It is a figure which shows an example of the regression line of AA value and IRI in the 1st Embodiment of this invention. It is a figure which shows an example of the data structure of sample DB in the 1st Embodiment of this invention. It is a figure which shows an example of the data structure of structure inspection DB in the 1st Embodiment of this invention. It is a figure which shows an example of the data structure of deformation evaluation DB in the 1st Embodiment of this invention.

<First Embodiment>
(Configuration of the first embodiment)
(1) Overall Configuration of Structure Deformation Detection System FIG. 1 is a diagram showing a configuration of a structure deformation detection system according to the first embodiment of the present invention. Hereinafter, the structure of the structure deformation detection system will be described with reference to the drawings.
As shown in the figure, the structure deformation detection system is based on the measurement vehicle 10 that measures the road structure and the surrounding terrain and shape while traveling on the road, and the terrain and shape measured by the measurement vehicle 10. And a structure deformation detection device 20 that generates various data and detects the damage of the road structure.
In the present embodiment, the road structure refers to a road and structures around the road and other structures, such as pavements, roads, guardrails, median strips, tunnels, piers and other structures. .

(2) Configuration of the measurement vehicle 10 The measurement vehicle 10 obtains point cloud data about the road structure and surrounding buildings and installations while traveling on the road or stopped, and performs image capturing to obtain image data. It is a vehicle equipped with an acquisition device.
FIG. 2 is a diagram illustrating a configuration of the measurement vehicle 10 according to the first embodiment of the present invention, and FIG. 3 is a diagram illustrating an appearance of the measurement vehicle 10.
As shown in FIGS. 2 and 3, the measurement vehicle 10 is composed of a CPU or the like, and controls the entire point cloud data and image data acquisition operation in the measurement vehicle 10, and the acquired point cloud data and image data, etc. An information storage unit 12 for storing the laser, a laser scanner 13 for irradiating a laser around the road while the measuring vehicle 10 is running or stopped, receiving the reflected light and acquiring point cloud data, and the measuring vehicle 10 A GPS (Global Positioning System) 14 for acquiring current position information, an IMU (Internal Measurement Unit) 15 for acquiring information indicating the posture of the vehicle body of the measurement vehicle 10, a timing unit 16 for timing, a road and its surroundings A camera 17 for photographing the landscape, an odometer 18 for measuring the travel distance of the measurement vehicle 10, and a network And an information input / output unit 19 for inputting / outputting information connected to a network or an information recording medium.

The control unit 11 is a processing unit that controls the entire measurement vehicle 10, and includes, for example, an electronic circuit such as a CPU (Central Processing Unit) or an integrated circuit such as an FPGA (Field-Programmable Gate Array).
The control unit 11 reads information from the information storage unit 12 and writes information to the information storage unit 12.

The information storage unit 12 is a device that stores information such as a hard disk, a memory, or a semiconductor element.
The information storage unit 12 includes an area for storing a program executed by the control unit 11 and a work area (RAM or the like) temporarily used when the control unit 11 executes processing.
The control unit 11 reads out the program stored in the information storage unit 12, develops it in the work area, and executes various processes.

In the present embodiment, the measurement vehicle 10 uses the principle of a so-called mobile mapping system to acquire road and surrounding point cloud data while traveling or stopping.
While the measurement vehicle 10 is traveling or stopped on the road, the laser scanner 13 emits a laser to objects around the road (including road structures and buildings / installations around the road) and receives the reflected light. . At this time, the laser scanner 13 acquires the time of emission and light reception from the timer unit 16 and also detects the emission direction (angle) and acquires point cloud data.
The information storage unit 12 stores information such as the relationship of the position of the laser scanner 13 relative to the vehicle body of the measurement vehicle 10, the irradiation angle, and the light reception angle, and the like. Based on the current position of the measurement vehicle 10, The current position and posture of the laser scanner 13 can be specified.

At the same time as the point cloud data is acquired by the laser scanner 13, the camera 17 captures the scenery around the road in color and acquires image data.
The information storage unit 12 stores information such as a shooting angle of the camera 17, a field angle, and a relative position / angle relationship with the vehicle body of the measurement vehicle 10, and is based on the current position of the measurement vehicle 10. The current position and the shooting angle (posture) of the camera 17 can be specified.

The GPS 14 and the odometer 18 measure the current position of the vehicle body of the measurement vehicle 10.
The IMU 15 includes a gyro sensor and an acceleration sensor, and measures the posture of the vehicle body of the measurement vehicle 10. The gyro sensor detects angular velocities in each of the three axial directions xyz of the measurement vehicle 10, and the acceleration sensor detects acceleration in each of the three axial directions xyz of the measurement vehicle 10.
The IMU 15 generates data indicating the angular velocity and acceleration in each of the three axial directions xyz of the measurement vehicle 10 as inertia data. In the inertial data, an angular velocity and an acceleration are set for each detection time (acquisition time) acquired from the timer unit 16.

The acquired point cloud data and image data are stored in the information storage unit 12.
After the storage, the information input / output unit 19 outputs the point cloud data and the image data to the structure deformation detection device 20.

(3) Configuration of Structure Deformation Detection Device 20 FIG. 4 is a diagram illustrating a configuration of the structure deformation detection device 20 according to the first embodiment of the present invention.
The structure deformation detection device 20 is an information processing device such as a PC operated by a road management company or the like, and inputs point cloud data from the measurement vehicle 10 described above, and based on the input point cloud data. Thus, the deformation of a road structure such as a pothole is detected.

  The structure deformation detection device 20 includes a CPU and the like, and controls the operation of the entire structure deformation detection device 20, and an information storage unit that stores input information, information received via a network, and the like. 22, a communication unit 23 that transmits and receives information via a network, a display unit 24 that includes a display and displays information on a screen, and an operation unit 25 that includes a key and a mouse and inputs information. It is configured.

The control unit 21 is a processing unit that controls the entire structure deformation detection apparatus 20, and is configured by, for example, an electronic circuit such as a CPU or an integrated circuit such as an FPGA.
The control unit 21 reads information from the information storage unit 22 and writes information to the information storage unit 22.

The information storage unit 22 is a device that stores information such as a hard disk, a memory, or a semiconductor element.
The information storage unit 22 includes an area for storing a program executed by the control unit 21 and a work area (RAM or the like) temporarily used when the control unit 21 executes processing.
The control unit 21 reads out the program stored in the information storage unit 22, develops it in the work area, and executes various processes.

  In addition, the information storage unit 22 includes point cloud data, data related to the point cloud data, image data (including contours described later), map data, an analysis program for analyzing road structures, and the like. Stored.

The point cloud data means that when the measuring vehicle 10 receives the reflected light of the laser light emitted by the laser scanner 13, it is recognized that an object modeled in a point shape exists at the position estimated to be reflected. Information including spatial coordinate information (X, Y, Z) of the point (the point is hereinafter referred to as “measurement point”).
The set of measurement points is referred to as a point cloud, and road structures and objects such as components constituting the road structure are represented by point clouds. That is, the spatial positions of road structures and components can be determined by a plurality of point cloud data.

  The point cloud data is displayed as a point cloud which is a set of minute points on the screen. It is possible to display at any viewpoint and enlargement / reduction ratio. For example, it can be displayed as a three-dimensional image from the viewpoint of a passenger of a car traveling on a road, or a planar map-like image viewed from above is displayed. It can also be displayed.

  The image data is image data taken by the measurement vehicle 10 with the camera 17 and includes moving images and still images.

  The map data is map data including position information (coordinate information such as latitude and longitude, address information), and indicates the position of a road or a building (including a road structure).

The analysis program is a program for analyzing the detection of the deformation generated in the road structure based on the point cloud data input from the measurement vehicle 10 to the structure deformation detection device 20, and is executed by the control unit 21. Is done.
When the control unit 21 activates the analysis program to analyze the deformation of the road structure, the control unit 21 inputs data of each database described later and other information to the analysis program and executes analysis processing.

FIG. 5 is a diagram showing a database stored in the information storage unit 22 according to the first embodiment of this invention.
As shown in the figure, the information storage unit 22 manages a point cloud DB (Data Base) 221 that manages point cloud data acquired from the measurement vehicle 10 and data of a road structure sample that is subjected to deformation inspection. A sample DB 222, a structure inspection DB 223 that manages Fourier amplitude spectrum data of the Z coordinate (hole depth) in the sample, and a deformation evaluation DB 224 that manages an index for evaluating the deformation of each sample are stored.

FIG. 6 is a diagram illustrating an example of a data configuration of the point cloud DB 221 according to the first embodiment of this invention.
As shown in the figure, the point cloud DB 221 manages the spatial coordinates (X, Y, Z) of each measured coordinate point and the light intensity of the received reflected light in association with each other as point cloud data. Has been.
The data structure of the other sample DB 222, structure inspection DB 223, and deformation evaluation DB 224 will be described later.

The structure deformation detection device 20 will be described as an example of a PC, but may be a so-called server device. In this case, the structure deformation detection device 20 may be a PC or a portable device via a network. A terminal or the like is connected.
The user operates the terminal to transmit an acquisition request for information on the detection result of the road structure deformation to the structure deformation detection apparatus 20, and the structure deformation detection apparatus 20 responds to the acquisition request. Then, information on the detection result of the detected deformation of the road structure is transmitted to the terminal, and the terminal displays the information on the detection result on the screen.

FIG. 7 is a functional block diagram of the structure deformation detection apparatus 20 according to the first embodiment of the present invention.
As shown in the figure, the structure deformation detection device 20 includes a point cloud data input unit for inputting point cloud data, and coordinates in the direction perpendicular to the surface of the road structure based on the input point cloud data. A structure coordinate calculation unit that extracts or calculates, and a Fourier transform processing unit that performs Fourier transform on a signal indicated by a plurality of coordinates perpendicular to the surface of the structure, and converts the signal into a sum of periodic functions of each angular frequency And a deformation detection unit that extracts a Fourier amplitude spectrum included in the angular frequency band of the detection target and outputs information indicating that the detection target is deformed when the extracted Fourier amplitude spectrum is equal to or greater than a threshold value. And have.
The function of the point cloud data input unit is realized by the control unit 21 controlling the communication unit 23.
The functions of the structure coordinate calculation unit, the Fourier transform unit, and the deformation detection unit are realized when the control unit 21 reads out the program and data stored in the information storage unit 22 and performs calculations.

(Operation of the first embodiment)
[1] Outline of Operation FIG. 8 is a flowchart showing the flow of the entire operation by the structure information providing system in the first embodiment of the present invention.
As shown in the figure, first, the measurement vehicle 10 performs measurement by irradiating a terrain or a structure with a laser while traveling on a road, and acquires point cloud data (step S1).
Next, the structure deformation detection device 20 stores the point cloud data acquired by the measurement vehicle 10 and generates data related thereto using the acquired point cloud data (step S2).
Next, the structure deformation detection device 20 displays the point cloud data in a planar map shape or a three-dimensional image shape, and from the displayed point cloud data, a point corresponding to a road structure subject to deformation inspection or An area is designated, and the change of the road structure is detected using the designated point or area as a sample (step S3).
Hereinafter, each operation | movement of said step S1-3 is demonstrated in detail.

[2] Acquisition of point cloud data and the like (step S1)
(1) Acquisition of Point Cloud Data Hereinafter, an operation when the measurement vehicle 10 acquires point cloud data while traveling on a road or stopped will be described.

The measurement vehicle 10 irradiates the surroundings of the road with a laser by using a laser scanner 13 provided in the own vehicle during traveling or stopping. At this time, the optical axis of the laser is scanned in the vertical and horizontal directions by changing the elevation angle and the azimuth angle, and a laser pulse is emitted at every minute angle within the scanning range. The laser scanner 13 receives the reflected light of the emitted laser.
Further, the control unit 11 acquires the laser emission time and the light reception time of the reflected light of the object from the time measuring unit 16, and based on the time from the laser emission to the reception of the reflected light, the laser scanner 13 and the target Measure the distance between objects.
Further, the laser scanner 13 measures the light intensity of the received reflected light.

While the measurement vehicle 10 is traveling or stopped, information on the laser emission direction from the laser scanner 13, current position information of the measurement vehicle 10 by the GPS 14 or odometer 18, and information indicating the attitude of the vehicle body of the measurement vehicle 10 by the IMU 15. Are acquired respectively.
Each of these pieces of information is associated with time information measured by the timer unit 16 and stored in the information storage unit 12.

Since the position / posture of the laser scanner 13 is in a fixed relationship with the position / posture of the measurement vehicle 10, the control unit 11 determines the laser scanner 13 based on the information on the position / posture of the measurement vehicle 10 and the laser emission direction. Find the position / posture.
The control unit 11 determines the space of each coordinate point that constitutes the object that reflects the laser pulse based on each information such as the distance between the laser scanner 13 and the object and information indicating the position and orientation of the laser scanner 13. Point cloud data representing coordinates (three-dimensional coordinates) is calculated.
For example, it is assumed that the coordinates are numerical values that can be converted into actual distances such that “1” of the coordinates of each coordinate point indicates “10 km”.

The structure deformation detection device 20 stores the point cloud data stored in the information storage unit 12 of the measurement vehicle 10 in the information storage unit 22 when the point cloud data is acquired from the measurement vehicle 10.
The acquisition method is not particularly limited. For example, the structure deformation detection device 20 and the measurement vehicle 10 are connected by a wired or wireless network, and the communication unit 23 of the structure deformation detection device 20 is connected via the network. It may be obtained by receiving point cloud data from the measurement vehicle 10, or the structure deformation detection device 20 has a function of reading an information recording medium, and the information recording medium in which the point cloud data is written You may make it acquire by reading.

(2) Three-dimensional imaging of point cloud data The structure deformation detection device 20 can generate a stereoscopic image based on point cloud data as follows using the point cloud data acquired as described above. .
FIG. 9 is a diagram for explaining the calculation of the plane coordinates by projecting the spatial coordinates onto the plane according to the first embodiment of the present invention.
As shown in the figure, the structure deformation detection apparatus 20 uses the coordinate points (X, Y, Z) of the point cloud data as projected coordinate points when the viewpoint coordinates are (Px, Py, Pz). The coordinates projected on (x, y) are obtained.
In this way, the structure deformation detection device 20 can generate a stereoscopic image of a landscape viewed from a certain viewpoint based on each coordinate point (X, Y, Z) of the point cloud data.
FIG. 10 is a diagram illustrating a display example of a stereoscopic image based on the point cloud data according to the first embodiment of the present invention.
In the example shown in the figure, a stereoscopic image of a landscape viewed from a viewpoint close to a passenger of a car traveling on a road is represented by a point cloud, and a road management company uses the point cloud data in the same manner as a captured image. By confirming the landscape image, the state of the road structure or the like can be easily grasped.

(3) Point map data plane mapping As described above, when the spatial coordinates of point cloud data are projected onto a plane, an image of a point cloud of an arbitrary display magnification viewed from a viewpoint from the vertical direction on the ground is displayed. Thus, the image of the point cloud can be used in the same manner as a normal planar map (hereinafter, the point cloud data represented in the planar map shape is referred to as “point cloud map information”).

[3] Generation of data related to point cloud data (step S2)
(1) Association of map data and point cloud data As described above, the structure deformation detection device 20 has map data in which information on the coordinates (X, Y) of map coordinate points is associated with each point. Is stored. Each coordinate (X, Y) of the map coordinate point is set to be the same as or convertible with the XY coordinate of each coordinate point (X, Y, Z) of the point group data. Hereinafter, in the present embodiment, the description will be made on the assumption that the XY coordinates of both coordinates coincide.

The structure deformation detection device 20 stores the point cloud data and map data in association with each other based on the coordinates (X, Y) of each coordinate point.
As a result, when the map coordinate point (X, Y) is designated on the map data, the structure deformation detection device 20 coordinates the point cloud data associated with the map coordinate point (X, Y). The point (X, Y, Z) is extracted, and the extracted coordinate point (X, Y, Z) and point group data including each coordinate point (X, Y, Z) within a predetermined distance from the coordinate point are displayed. can do.

  Further, as described above, in the structure information providing system according to the present embodiment, the point cloud data and the image data are also linked, so that the coordinates (X, Y) of each coordinate point are used as a key. Group data, image data, and map data can be linked.

  In this way, the structure deformation detection device 20 manages various data related to the road management business by linking them with the coordinates (X, Y) of each coordinate point as a key. It is possible to support the efficiency of the inspection work in the management business, and, for example, can function as a platform for a geographic information system (GIS).

[4] Deformation detection of road structure (step S3)
(1) Contour Creation Operation FIG. 11 is a flowchart showing the flow of the contour creation operation by the structure deformation detection device 20 according to the first embodiment of the present invention.
Hereinafter, the contour creation operation, which is one of the deformation detection operations by the structure deformation detection device 20, will be described with reference to FIG.
The contour is image information indicating a plan view in which Z coordinates (coordinates in the height direction) of point group data in an arbitrary region are visualized.

  First, the road management business operator operates the operation unit 25 of the structure deformation detection device 20 to display a point image data, a three-dimensional image, a plane image, or map data associated with the point group data on the display unit 24. It is displayed (step S101).

  Next, the operator designates a predetermined point on the image displayed on the display unit 24 using the operation unit 25 or designates a region of the road structure to be detected by surrounding with a line. Then, the trimming process is performed so as to leave the designated area (step S102).

The point cloud data is given absolute coordinates by integration with GPS.
When performing the trimming process, the road management business operator operates the operation unit 25 to display the image displayed on the display unit 24 so that the surface of the road structure to be deformed is an XY plane. , Rotate around Y, Z axis.
In the case of map data, trimming may be performed without rotating the map data.

Further, when the road management business operator operates the operation unit 25 to specify a region when performing trimming processing, the control unit 21 uses the least square method using the absolute coordinates of the point cloud data in the specified region. For example, the least square plane may be calculated and the least square plane may be used as the surface of the road structure.
Similarly, in this case, the road management operator operates the operation unit 25 to rotate the X-, Y-, and Z-axes so that the surface of the road structure becomes the XY plane.
Moreover, you may set the surface of a road structure suitably by the other method instead of the least square method.

  The coordinate conversion method by rotation around the X, Y, and Z axes is based on the prior art and will not be described.

The designated region and the point cloud data included in the designated region are hereinafter referred to as “sample”.
At this time, the control unit 21 issues a sample ID and duplicates the coordinates (X, Y, Z) of each coordinate point of the point group data in the designated area in association with the sample ID. Register in the DB 222.

FIG. 12 is a diagram showing a specific example of sample area designation in the first embodiment of the present invention.
As shown in the figure, in the present embodiment, as an example, the designated sample area is rectangular, and the lateral width is the road width.

  Next, the control unit 21 of the structure deformation detection device 20 divides the sample designating the region as described above into a plurality of meshes (step S103).

FIG. 13 is a diagram for explaining a mesh in the first embodiment of the present invention.
(A) is a figure which shows the relationship between a sample and a mesh, (b) is a figure for demonstrating the intensity | strength of a mesh.
A mesh is a small square area in which a sample for deformation inspection (for example, a part of a road having a length of 5 m and a width of 3.5 m) is divided into n rows and m columns (n and m are positive numbers). Say.
Here, the relationship between the sample and the mesh will be described with reference to FIG.
In the figure, a region of a sample to be deformed is shown, and a plurality of measurement points constituting a point group are located in the sample region.
The road structure region is divided into 30 square meshes in 5 rows and 6 columns.

Although detailed contents will be described later, the strength is calculated for each mesh on the basis of the strength (Z coordinate value) of the measurement points of one or more point groups located in the region of each mesh.
The mesh strength information is information indicating the strength that is a representative value of the Z coordinate in each mesh. For example, the mesh where there is a pothole that is generated by deeply cutting the road is larger than the mesh where there is a pothole that is generated by shallowly cutting, so the Z coordinate becomes larger, The strength of the mesh also increases.
Such a strong mesh can be evaluated as having a serious pothole that is likely to cause an accident.
FIG. 13B shows that the strength is calculated for each mesh and is colored in different colors depending on the strength.

  When generating the mesh as described above, the control unit 21 writes the mesh position information in the sample region in the sample DB 222 in association with the sample ID of the sample (step S104).

Next, the control unit 21 calculates the average value of the Z coordinates of the point cloud data of the measurement points in each mesh as a representative value of the Z coordinates of the mesh (step S105).
Although the method for setting the reference point of the coordinate values of the point cloud data is not particularly limited, in this embodiment, as an example, the XY plane is set on the surface (road surface, etc.) of the road structure, and the Z axis is set vertically downward. To do. Therefore, when the road surface is shaved and a pothole is generated, the deeper the shaving, the larger the value of the mesh Z coordinate located on the bottom surface of the pothole.

  The control unit 21 writes the representative value of the calculated Z coordinate of the mesh in the sample DB 222 in association with the sample ID of the corresponding sample (step S106).

Next, the control unit 21 colors each mesh in the sample area based on the representative value of the Z coordinate of the mesh (step S107), and displays the generated contour on the display unit 24 (step S107). S108).
This completes the contour creation operation.

FIG. 14 is a diagram showing an example of a contour created by the structure deformation detection device 20 in the first embodiment of the present invention.
As a coloring method of each mesh at the time of contour creation in this example, the control unit 21 colors the lowest mesh among the representative values of the Z coordinates of the plurality of meshes to complete black, and the highest mesh Is colored completely white. In addition, coloring is performed in a gray scale in a stepwise manner (for example, the representative value is every 1 mm) from a high value mesh in the meantime to a low value mesh from black to white.
As shown in the figure, a place where road surface deformation such as a pothole is estimated is displayed in black.
As described above, the structure change detection device 20 performs coloring based on the Z coordinate of the point cloud data, so that the road management company can easily find the road surface change.

In the point cloud data, it is difficult to completely reproduce the position irradiated with the laser beam for each measurement opportunity. The reason is that even if the same target is measured twice, the position where the laser beam is irradiated is slightly different in each measurement, so an error of several millimeters occurs due to the influence of the texture of the road surface and slight foreign matter. It depends on what you do.
In this embodiment, as described above, after dividing the sample area into square meshes, the average value of the Z coordinate of each point in the mesh is taken, and the contour is created as the representative value of the Z coordinate of the mesh. It is possible to suppress errors during laser irradiation as described above.

(2) Flatness and IRI Calculation Operation Next, flatness and IRI (International Roughness Index) calculation operation using point cloud data, which is one of the deformation detection operations by the structural deformation detection device 20. explain.

The flatness is an index indicating the flatness of the road surface. The smaller the value, the flatter and better the road surface state, and this is hereinafter referred to as “high flatness”.
On the contrary, as the flatness value is larger, the road surface is deformed (unevenness), indicating that the road surface state is poor. This is hereinafter referred to as “low flatness”.

  IRI is an index proposed by the World Bank that objectively evaluates the degree of flatness of pavement as the “riding comfort” when driving by car. .

FIG. 15 is a flowchart showing a flow of a flatness calculation operation by the structure deformation detection apparatus 20 according to the first embodiment of the present invention.
Hereinafter, the description will be made along this drawing.

  First, the road management company operates the operation unit 25 of the structure deformation detection device 20 to display the created contour on the display unit 24 (step S201).

Next, the road management business operator operates the operation unit 25 to designate a place where the flatness on the contour is calculated (step S202).
FIG. 16 is a diagram showing an example of a contour displayed by the structure deformation detection device 20 according to the first embodiment of the present invention, and shows a place where flatness is calculated.
In the present embodiment, as an example, following the conventional method for calculating flatness, the location where flatness is measured is selected in parallel along the center line of the measurement lane, for example, from the center line of the measurement lane as shown in the figure. A portion of a predetermined distance (about 1 m) in the side road direction is the right or left rudder bottom.

  The control unit 21 extracts a mesh that is located on the location (line) where the specified flatness is calculated and is associated (step S203).

Next, the control unit 21 calculates a flatness value based on the representative value of the Z coordinate of the extracted mesh (step S204).
The calculation method will be specifically described below.
As described below, a conventional formula is used for calculating the flatness.
First, regarding the extracted columns, the representative values of the Z coordinates of the i-th, i + a-th, and i + b-th meshes from the start line side are set to h _i ¹ , h _i ² , and h _i ³ , respectively. However, 1 ≦ i ≦ n and 0 <a <b.
In the present embodiment, as an example, the size of the mesh constituting the sample is 5 cm × 5 cm. In this case, as an example, if a = 29 and b = 59, the i + a-th mesh is located at a point 1.5 m from the i-th mesh, and the i + b-th mesh is located at a point 3 m from the i-th point.
i is equal to or less than the number of vertical meshes in the contour. For example, when the number of vertical meshes is 105, if a = 29 and b = 59 as described above, i = 46 at the maximum.

Next, the control unit 21 substitutes the above h _i ¹ , h _i ² , and h _i ³ into the following formula 1 to obtain Hi.
(Formula 1)

Next, the control unit 21 obtains the flatness σ by substituting Hi into the following Equation 2.
(Formula 2)

  Next, the control unit 21 writes the calculated flatness σ value in the deformation evaluation DB 224 in association with the sample ID of the corresponding sample (step S205).

Next, the control unit 21 calculates IRI based on the calculated flatness σ (step S206).
The following expression 3 is used as an expression for converting the flatness value into IRI, but is merely an example, and another calculation expression may be used.
(Formula 3)
IRI = 1.33σ + 0.24

  The control unit 21 writes the calculated IRI in the deformation evaluation DB 224 in association with the sample ID of the corresponding sample (step S207).

The control unit 21 displays the calculated flatness and IRI on the display unit 24 (step S208).
The road management business operator can easily confirm the road surface condition in the contour area by confirming the flatness and IRI values displayed on the display unit 24.

  As described above, in the present embodiment, the control unit 21 of the structure deformation detection device 20 calculates and displays the flatness and IRI of the sample area using the point cloud data, and thus the road management operator It is possible to easily grasp the situation of the road structure subject to deformation detection without performing a troublesome operation such as going to the place where the road structure such as a road is installed and measuring.

(3) Pothole Detection Operation Next, a pothole detection operation using short-time Fourier transform (STFT) to which the technique of time-frequency analysis is applied will be described.

In general, the short-time Fourier transform for discrete time is expressed by the following (formula 4).

(Formula 4)


Here, w (n) is a window function, and x (n) is a function to be converted.
STFT _{x, w} (n, ω) is a complex number representing the spectrum at time n and angular frequency ω.

FIG. 17 is a diagram for explaining Fourier transform in the first embodiment of the present invention.
By using the short-time Fourier transform, the control unit 21 can express the original signal as shown in (a) of FIG. 17 as a sum of periodic functions as shown in FIG. And as shown in the following formula 5, the Fourier amplitude spectrum (the magnitude of the amplitude after Fourier transform) of the angular frequencies ω1, ω2, ω3,... Can be calculated.
(Formula 5)
(Original signal) = (Fourier amplitude spectrum of angular frequency ω1) × (periodic function of angular frequency ω1) + (Fourier amplitude spectrum of angular frequency ω2) × (periodic function of angular frequency ω2) + (Fourier of angular frequency ω3) Amplitude spectrum) x (periodic function of angular frequency ω3) + ...

In the present embodiment, as an example, it is assumed that the surface of a road structure to be detected for deformation is a road surface, and a part of the road surface is trimmed into a rectangular shape in the sample region.
Here, the long side direction of the rectangular sample region is defined as a vertical direction, and the direction orthogonal to the road surface is defined as a horizontal direction.
As described above, when the surface of the road structure is aligned with the XY plane, the vertical direction may be aligned with the Y axis and the horizontal direction may be aligned with the X axis.
Further, the mesh of the sample area is assumed to be a square minute area in which the sample area is divided into n rows and m columns (n and m are positive numbers).

The control unit 21 extracts the representative value of the Z coordinate of the continuous mesh in the sample region for one column in the vertical direction, and the unevenness on the road surface represented by the representative value of the Z coordinate of the extracted mesh is extracted as the “original signal”. As a short time Fourier transform.
Then, the control unit 21 performs a short-time Fourier transform on the representative value of the Z coordinate of the continuous mesh for one column in the vertical direction, and represents the sum of the periodic functions of the respective angular frequencies to calculate the Fourier amplitude spectrum. .

As a result of the short-time Fourier transform, each angular frequency of the represented periodic function corresponds to the vertical width of the depression (deformation) in the sample region.
When the information on the angular frequency indicating the width of the deformation intended for detection or the information on the width of the deformation is designated, the control unit 21 of the structure deformation detecting device 20 specifies the Fourier amplitude spectrum of the designated angular frequency. Based on the above, it is possible to detect a deformation of the width corresponding to the angular frequency, for example, a pothole.
As a result, the road management company can easily find a deformation such as a pothole, which has a small deformation scale and is difficult to detect visually.
When the Fourier amplitude spectrum of the frequency band corresponding to the pothole is equal to or greater than a predetermined value, the control unit 21 determines that a pothole is generated in the road structure to be inspected, and based on the Fourier amplitude spectrum. The scale (depth) of the pothole can be determined.

FIG. 18 is a flowchart showing the flow of the pothole detection operation by the structure deformation detection device 20 according to the first embodiment of the present invention.
Hereinafter, the pothole detection operation will be described with reference to FIG.

  First, the road management business operator operates the operation unit 25 of the structure deformation detection device 20 to select a sample to be subjected to deformation detection (step S301).

Next, the road management company uses the operation unit 25 to input the scale information of the deformation for the detection purpose and the scale information of the sample (step S302).
Here, the deformed scale information refers to the width of the deformed detection direction (vertical direction). Further, the scale information of the sample refers to the width of the deformation detection direction (vertical direction).
In this embodiment, as an example, scale information (maximum value “50 cm”, minimum value “20 cm”) and sample area scale information “5 m”, which are deformations for detection purposes, are input.

The control unit 21 calculates information on the angular frequency band of the detection target pothole based on the input scale information of the detection target deformation and the scale information of the sample (step S303).
As in the above example, when the maximum value “50 cm”, the minimum value “20 cm”, and the sample area scale information “5 m” of the pothole scale information are input, the control unit 21 calculates 5 m ÷ (50 cm × 2). ÷ 5 = 1 cycle / m, 5 m ÷ (20 cm × 2) ÷ 5 = 2.5 cycle / m, and 1 to 2.5 cycle / m is calculated as the frequency band of the pothole.
In the present embodiment, the control unit 21 calculates information on the angular frequency band of the detection target pothole. However, the road management company uses the operation unit 25 to determine the angular frequency band of the detection target pothole. Information may be directly input.

FIG. 19 is a diagram showing a pothole detection method using short-time Fourier transform in the first embodiment of the present invention.
As shown in the figure, when the intensity of one column in the vertical direction of the mesh (representative value of the Z coordinate) is taken as a signal, the vertical length of the sample region is about 5 m, and the vertical length of each mesh. Since the height is 5 cm, the intensity of one column in the mesh vertical direction can be regarded as a discrete signal having about 100 points.
Hereinafter, one column in the vertical direction of the mesh in the sample region is referred to as an “inspection column”.

As shown on the left side of FIG. 19, the control unit 21 sets 20 points of mesh strength continuous in the signal traveling direction (longitudinal direction) out of 100 points of mesh strength in one row of the sample in the sample. Cut out (extract) (step S304).
Hereinafter, the vertical column including the group of 20 mesh strengths cut out is referred to as “cutout column”.
At this time, the control unit 21 shifts the cutout start position by 5 points so that there is an overlap of 15 points between adjacent cutout rows.
Note that the number of points in the inspection row (100 points), the number of points in the cutout row (20 points), and the number of overlap points (15 points) are merely examples, and are not limited to these values. These points are appropriately changed in view of calculation efficiency and accuracy.

The strength of the mesh included in one cutout row is p point, the score of the strength of the mesh overlapping with the adjacent cutout row is q point, and the cutout start position of the first cutout row from the start line side is In the case of the rth mesh from the start line side, the cutout start position of the nth cutout row is the [r + (p−q) × (n−1)] th mesh.
For example, the strength of the mesh included in one cutout row is 20 points, the strength of the mesh that overlaps the adjacent cutout row is 15 points, and the cutout of the first cutout row from the start line side is started When the position is the first mesh from the start line side, the cutout start positions of the second, third, and fourth cutout rows are the sixth, eleventh, and sixteenth meshes, respectively.

As described above, the control unit 21 generates a cutout column by overlapping a predetermined number of cutout rows adjacent to each other, so even when a deformation such as a pothole is located on the boundary of the cutout row. The deformation can be appropriately evaluated.
If the overlap is not cut out as described above, the deformation on the boundary of the cut-out row is divided into two, which are regarded as two small-scale deformations. Such a problem can be prevented.

  Note that the control unit 21 applies a detrending to the about 100 signals to remove the linear trend before cutting out so that only local fluctuations in the data are reflected in the frequency analysis. May be.

Next, the control unit 21 performs a Fourier transform on the discrete signal indicating the strength of each mesh in each cutout row, and calculates a Fourier amplitude spectrum of each angular frequency in each cutout row (step S305). The control unit 21 executes the process of calculating the Fourier amplitude spectrum of each angular frequency in each cut-out row for all inspection rows.
At this time, the control unit 21 performs zero padding on the 20 consecutive signals in the cut-out row to increase the resolution in the angular frequency direction, and a predetermined number of signals are added to the rear part of the 20-point signal. A short-time Fourier transform may be executed over a hamming window after adding 0 to make a discrete signal of a predetermined length.
In this example, a Hamming window is used as the window function, but a Hanning window or other window functions may be used.

  The control unit 21 writes the Fourier amplitude spectrum of each angular frequency in each cut-out row of all the calculated inspection rows in the structure inspection DB 223 (step S306).

  Next, the control unit 21 generates pothole detection image data based on the calculated Fourier amplitude spectrum data of each angular frequency and displays it on the display unit 24 (step S307).

An example of the detection image of the pothole is shown on the right side of FIG.
As shown in the figure, in the detection image of the pothole, the Fourier amplitude spectrum of each angular frequency of each cut-out row is colored with a different color for each Fourier amplitude spectrum, and the road management company indicates the presence or absence of the pothole in the sample area. Easy to judge.

In the example of the figure, the detection image of the pothole has an angular frequency on the horizontal axis.
In the detection image of the pothole, the Fourier amplitude spectrum of each angular frequency for one cut-out row corresponds to one row in the horizontal direction of the bar-shaped image indicating the Fourier amplitude spectrum of each colored angular frequency. All the cut-out columns are displayed in the vertical direction in the order from the start line side to the end line side.
That is, in the example of this figure, in the detection image of the pothole, bar-shaped images indicating the Fourier amplitude spectra of each of the angular frequencies that are colored in 20 columns are arranged in the vertical direction corresponding to the 20 columns. Is displayed.

Further, as shown in the figure, an angular frequency band corresponding to the pothole is indicated by an arrow on the upper left side of the detection image of the pothole.
In this figure, the angular frequency band of the pothole shows a high frequency range of the Fourier amplitude spectrum, but there is a black portion, and there is a high possibility that a pothole exists.

FIG. 20 is a diagram illustrating a pothole detection method using short-time Fourier transform according to the first embodiment of the present invention.
In the example of the figure, a Fourier amplitude spectrum of each angular frequency is calculated for each inspection row, and a pothole detection image is shown for each inspection row.
As shown in the figure, the control unit 21 calculates the Fourier amplitude spectrum of all the inspection sequences in the sample region and each angular frequency, generates a detection image of all the inspection sequences and potholes, and displays them on the display unit 24. Display.
The road management company confirms the Fourier amplitude spectrum in the angular frequency band of the pothole in the pothole detection image of each displayed inspection row, and the pot that was difficult to find because the scale on the road surface was small. The presence or absence of holes can be easily found.

(4) Deformation evaluation operation in road structure using MA value As described above, after the structure deformation detection device 20 performs the pothole detection operation, the degree of deformation progress based on the detection result A value of an index indicating the above can be calculated, and the deformation of the road structure can be further evaluated along the index.
FIG. 21 is a flowchart showing the flow of the road structure evaluation operation by the structure deformation detection apparatus 20 according to the first embodiment of the present invention.
Hereinafter, the evaluation operation of the road structure will be described along this figure.

First, the road management company operates the operation unit 25 of the structure deformation detection device 20 and inputs a threshold value of an MA value (Maximum Amplitude) (step S401).
The control unit 21 writes the threshold value of the input MA value in the information storage unit 22 for setting.

The MA value is the highest value of the Fourier amplitude spectrum of the frequency band (1 to 2.5 cycle / m) of the pothole in all cut-out columns of all inspection columns in the structure inspection data, and the unit is length. (M).
When the MA value is high, it indicates that a deformed shape that protrudes compared to a normal road surface, for example, a deep pothole exists within the range of the sampled road structure.
When the road management business operator uses the operation unit 25 to set an arbitrary threshold value for the MA value in the structure deformation detection device 20, the structure deformation detection device 20 has a pothole having an MA value equal to or greater than the threshold value. Display about.

In the present embodiment, as an example, the road management business operator uses the operation unit 25 to set the thickness of the component part of the road structure to be inspected as the first threshold value of the MA value, and the general value as the second threshold value. The depth recognized as a pothole is input and set in the structure deformation detection device 20.
For example, when the road structure is an asphalt pavement, since the surface layer thickness of the surface asphalt of the road is generally 0.04 m, the first threshold value may be set to 0.04 m. By setting the first threshold in this way, it is possible to detect a dangerous pothole where the pothole depth may reach the base layer.
The second threshold value may be set to 0.02 m. By setting the second threshold in this way, it is possible to detect a pothole that adversely affects the running vehicle.

Next, the control unit 21 outputs an MA value based on the structure inspection data (step S402).
The MA value is the highest value of the Fourier amplitude spectrum of the frequency band (1 to 2.5 cycle / m) of the pothole in all cut-out columns of all inspection columns in the sample in the structure inspection data.
The control unit 21 refers to the Fourier amplitude spectrum of the frequency band of the potholes of all the cut-out columns of all the test columns in the sample managed in the structure inspection database stored in the information storage unit 22, and From the Fourier amplitude spectrum in the frequency band, the maximum value is extracted and output as the MA value.

  Next, the control unit 21 compares the output MA value with the first and second threshold values of the MA value set in the information storage unit 22, and the MA value is the first and second threshold values. It is determined whether or not the above is true (step S403).

And the control part 21 displays the information of the judgment result whether MA value is more than the 1st, 2nd threshold value on the display part 24 (step S404).
For example, here, in each sample, the display unit 24 has a sample with an MA value greater than or equal to the first threshold, a sample with an MA value greater than the first threshold, an MA value greater than or equal to the second threshold, and less than the second threshold. The contours are classified into samples of MA values, the contours associated with the respective sample IDs are extracted from the information storage unit 22, and each contour is classified and displayed.
The road management business operator confirms the contours displayed on the display unit 24 and repairs the area, such as repairing potholes displayed on the contours of the first threshold value or higher. It is possible to examine plans and the like.

(5) Deformation evaluation operation in road structure using AA value The deformation evaluation method using the MA value described above is based on a deformation that is extremely prominent compared to a normal road surface, that is, from a normal road surface. It was possible to accurately detect potholes that were deeply shaved.
On the other hand, when the road surface is not so deeply cut and the deformation spreads over the road structure, for example, the entire road surface, an AA (Average Amplitude) value is used to evaluate the deformation. Is appropriate.
The AA value is the average value of all Fourier amplitude spectra in the frequency band of the pothole (1 to 2.5 cycles / m) in all cut-out columns of all inspection columns in the structure inspection data, and the unit is long. (M).
When the AA value is high, it indicates that deformation such as a pothole is spread over the entire range (road surface) of the sampled road structure to be inspected.

FIG. 22 is a flowchart showing the flow of the deformation evaluation operation in the road structure using the AA value in the first embodiment of the present invention.
Hereinafter, the description will be made along this drawing.

  First, the control unit 21 refers to the Fourier amplitude spectrum of the frequency band of the potholes of all the cut-out columns of all the test columns in the sample managed in the structure inspection DB 223 stored in the information storage unit 22. The Fourier amplitude spectrum of the frequency band of the hall is extracted, and an average value of these extracted Fourier amplitude spectra is calculated as an AA value (step S501).

  The control unit 21 writes the calculated AA value in association with the sample ID in the deformation evaluation DB 224 in the information storage unit 21 (step S502).

  As described above, the AA value is calculated for each sample, and when the number of AA values written in the deformation evaluation DB 224 exceeds a predetermined number, the control unit 21 associates the AA value of each sample with the AA value. The IRI value measured by the same sample is extracted, and a regression line is generated by, for example, the least square method or the like (step S503).

FIG. 23 is a diagram illustrating an example of regression lines of AA values and IRIs according to the first embodiment of this invention.
In the example of the figure, the horizontal axis represents the AA value, and the vertical axis represents the IRI.
Further, IRI points with respect to AA values of a plurality of samples are plotted, and a regression curve based on the plurality of points is drawn.
In IRI, four or more are treated as damaged paved roads. In this example, the control unit 21 sets AA value = 0.002 (m) corresponding to IRI = 4 to AA based on the regression line. And is written in the information storage unit 22 (step S504).

Next, the road management business operator operates the operation unit 25 to designate a sample for performing deformation evaluation using the AA value (step S505).
For example, the road management company selects and designates one or a plurality of corresponding samples using the operation unit 25 from the sample list displayed on the display unit 24.

  When the designation information of the sample to be subjected to the deformation evaluation using the AA value is input, the control unit 21 extracts the AA value of the specified sample from the deformation evaluation DB 224 of the information storage unit 22, and A threshold value of the AA value is extracted from the information storage unit 22, the two are compared, and it is determined whether or not the AA value of the designated sample is equal to or greater than the threshold value (step S506).

Next, the control unit 21 causes the display unit 24 to display information on the determination result as to whether or not the AA value is greater than or equal to the threshold value (step S507), and ends the operation.
For example, here, the display unit 24 classifies each sample into a sample having an AA value equal to or greater than the threshold value and a sample having an AA value less than the threshold value, and extracts a contour associated with each sample ID from the information storage unit 22. Then, contours with an AA value that is greater than or equal to the threshold and less than the threshold are displayed separately.
The road management company confirms the contours displayed on the display unit 24, and in particular, for the contours exceeding the threshold value, it is possible to examine the repair plan for the area and perform the repairs. .

(About the database in the first embodiment)
As illustrated in FIG. 6, the information storage unit 22 stores a point cloud DB 221, a sample DB 222, a structure inspection DB 223, and a deformation evaluation DB 224.
Hereinafter, the data structure of the sample DB 222, the structure inspection DB 223, and the deformation evaluation DB 224 will be described.

FIG. 24 is a diagram illustrating an example of a data configuration of the sample DB 222 according to the first embodiment of this invention.
As shown in the figure, in the sample DB 222, the point cloud data of the point cloud included in the sample, the position information of a plurality of meshes generated by dividing the sample region, and the strength information (Z coordinate of each mesh) are generated. Representative value) is managed in association with a sample ID which is identification information of the sample.
The mesh will be described later.

The mesh position information is information indicating each mesh position in the sample region.
As shown in the example of FIG. 14, when lines are drawn in a grid pattern in the sample area and the sample area is partitioned into square-shaped meshes of the same size, for example, the mesh position information includes vertical and horizontal columns. Indicated by the number.

FIG. 25 is a diagram illustrating an example of a data configuration of the structure inspection DB 223 according to the first embodiment of this invention.
As shown in the figure, in the structure inspection DB 223, the Fourier amplitude of each angular frequency calculated as a result of performing Fourier transform based on the strength of a plurality of meshes included in each cut-out row in each inspection row of each sample is shown. The spectrum is managed.
As shown in the figure, the structure inspection DB 223 also manages the cutout start position of each cutout row.

FIG. 26 is a diagram illustrating an example of a data configuration of the deformation evaluation DB 224 according to the first embodiment of this invention.
As shown in the figure, the deformation evaluation DB 224 uses the flat value σ in the sample region, the mesh position in the sample region where the flatness is calculated, the IRI, the MA value, and the AA value as sample IDs. It is managed in association.

(Summary of the first embodiment)
As described above, according to the first embodiment of the present invention, the structure deformation detection device 20 reproduces the shape of the surface of the road structure using the spatial coordinates of the point cloud data, and the surface The shape of the irregularity of the signal is regarded as a signal, Fourier transform is performed, the result is converted into the sum of the periodic functions of each angular frequency, and the Fourier amplitude spectrum is detected to clearly visualize the deformation of an arbitrary scale. For example, road management operators can easily detect small-scale deformations that are difficult to detect visually, such as potholes, and can make appropriate repair plans for road structures. .

The measurement vehicle 10 and the structural deformation detection device 20 are realized mainly by a program loaded on the CPU and the memory. However, it is also possible to configure this apparatus or server by a combination of any other hardware and software, and the high degree of freedom of design will be easily understood by those skilled in the art.
When the measurement vehicle 10 or the structure deformation detection device 20 is configured as a software module group, the program is recorded on a recording medium such as an optical recording medium, a magnetic recording medium, a magneto-optical recording medium, or a semiconductor. Alternatively, it may be loaded from the above recording medium or may be loaded from an external device connected via a predetermined network.

  The above-described embodiment is an example of a preferred embodiment of the present invention, and the first embodiment of the present invention is not limited to this, and various modifications can be made without departing from the scope of the present invention. Can be implemented.

For example, in the first embodiment of the present invention, the road structure is a road. However, for example, it is possible to detect a deformation that occurs on a wall surface standing perpendicular to the ground.
In this case, as described above, the control unit 21 of the structure deformation detection device 20 is configured to rotate around the X, Y, and Z axes so that the wall surface that is the surface of the road structure becomes the XY plane when the sample is trimmed. Rotate the image.
In this way, by rotating the surface of the road structure so that it is in the XY plane, it is easy to deform the surface of the road structure at any angle with respect to the ground surface to form irregularities in the vertical direction of the surface. Can be detected.

DESCRIPTION OF SYMBOLS 10 Measurement vehicle 11,21 Control part 12,22 Information storage part 13 Laser scanner 14 GPS
15 IMU
16 Timekeeping Unit 17 Camera 18 Odometer 19 Information Input / Output Unit 20 Structure Deformation Detection Device 23 Communication Unit 24 Display Unit 25 Operation Unit 221 Point Cloud DB
222 Sample DB
223 Structure inspection DB
224 Deformation evaluation DB

Claims (8)

A structure deformation detection device for detecting a structure surface deformation,
A point cloud data input unit that represents the structure and the terrain with a point cloud that is a set of a large number of points, and that inputs point cloud data including spatial coordinates of each measurement point constituting the point cloud;
A structure coordinate calculation unit that extracts or calculates coordinates in a direction perpendicular to the surface of the structure based on the input point cloud data;
A Fourier transform processing unit that performs a short-time Fourier transform on a signal indicated by a plurality of coordinates perpendicular to the surface of the structure, and converts the signal into a sum of periodic functions of each angular frequency;
A deformation detection unit that extracts a Fourier amplitude spectrum included in an angular frequency band of a deformation intended for detection and outputs information indicating that there is a deformation of the detection target when the extracted Fourier amplitude spectrum is equal to or greater than a threshold value It has a door,
The Fourier transform processing unit sets a set of a predetermined number of coordinates from a plurality of coordinates perpendicular to the surface of the structure located on a line drawn on the surface of the structure. Extracting multiple times so that the combination of consecutive coordinates included is different from each other, performing a short-time Fourier transform on the signal indicated by the predetermined number of coordinate sets extracted multiple times,
The set of coordinates extracted a plurality of times has a predetermined number of coordinates in common with a set adjacent to the start line side and a set adjacent to the end line side of the drawn line. apparatus. The structure coordinate calculation unit divides the inspection area of the surface of the structure into a plurality of minute areas, and averages the coordinates in the direction perpendicular to the surface of the structure of the measurement points of the point group included in the minute area The structural deformation detection device according to claim 1 , wherein a representative value of the micro area, which is a value, is calculated for each micro area as coordinates in a direction perpendicular to the surface of the structure. A structure deformation detection device for detecting a structure surface deformation,
A point cloud data input unit that represents the structure and the terrain with a point cloud that is a set of a large number of points, and that inputs point cloud data including spatial coordinates of each measurement point constituting the point cloud;
A structure coordinate calculation unit that extracts or calculates coordinates in a direction perpendicular to the surface of the structure based on the input point cloud data;
A Fourier transform processing unit that performs a short-time Fourier transform on a signal indicated by a plurality of coordinates perpendicular to the surface of the structure, and converts the signal into a sum of periodic functions of each angular frequency;
A deformation detection unit that extracts a Fourier amplitude spectrum included in an angular frequency band of a deformation intended for detection and outputs information indicating that there is a deformation of the detection target when the extracted Fourier amplitude spectrum is equal to or greater than a threshold value It has a door,
The structure coordinate calculation unit divides the inspection area of the surface of the structure into a plurality of minute areas, and averages the coordinates in the direction perpendicular to the surface of the structure of the measurement points of the point group included in the minute area A structural deformation detection device that calculates a representative value of the micro area, which is a value, for each micro area as coordinates in a direction perpendicular to the surface of the structure. 4. The structural deformation detection device according to claim 2 , wherein image data colored stepwise is generated based on a representative value of the minute area. In the representative value of the minute region located on the line drawn on the surface of the structure, the i-th, i + a-th, i + b-th (1 ≦ i ≦ n, 0 < If the representative values of the minute region of a <b) are h _i ¹ , h _i ² and h _i ³ ,
The deformation detection unit calculates the value of Hi based on the following formula 1,
(Formula 1)


The structural deformation detection device according to claim 2 or 3, wherein a flatness σ of the surface of the structure is calculated based on the calculated value of Hi and the following expression (2) .
(Formula 2)
The structure deformation detection device according to claim 5, wherein the deformation detection unit calculates an IRI value based on the calculated flatness σ and the following Equation 3.
(Formula 3)
IRI = 1.33σ + 0.24 The said deformation | transformation detection part outputs the information that there exists a deformation | transformation of the said detection objective, when the maximum value of the said extracted Fourier amplitude spectrum is more than a threshold value . The structure deformation detection device according to item 1 . The said deformation | transformation detection part outputs the information that there exists a deformation | transformation for the said detection objective, when the average value of the said extracted Fourier amplitude spectrum is more than a threshold value . The structure deformation detection device according to item 1 .