JPWO2021044628A1 - Deformity detection system and deformation detection method - Google Patents

Abstract

The deformation detection system (100) includes an acquisition unit (21), a data conversion unit (22), and a step detection unit (23). The acquisition unit (21) acquires three-dimensional point cloud data, which is data representing the surface of the structure as a three-dimensional point cloud, and shape data indicating the shape of the structure. The data conversion unit (22) sets a reference plane corresponding to the surface of the structure based on the shape data, and selects the three-dimensional point cloud data from a plurality of points arranged in a matrix on the reference plane. It is converted into element point cloud data including data of a plurality of element points represented by the position of one point and the amount of deviation from the reference plane. The step detection unit (23) detects the position of the step on the surface of the structure based on the result obtained by applying a Gaussian difference filter to a plurality of element points included in the element point cloud data.

Description

The present invention relates to a deformation detection system and a deformation detection method capable of detecting a step generated on the surface of a structure.

In order to efficiently inspect structures such as tunnels, bridges, and roads, a traveling type measuring device called MMS (Mobile Mapping System) is used. The MMS measures the surrounding structure with a laser scanner device or the like while traveling, and generates data showing the measurement result. Such data includes three-dimensional point cloud data which is data of a three-dimensional point cloud representing the surface of the structure by a plurality of three-dimensional points, and the three-dimensional point cloud data is used for inspection of the structure. In the inspection of the structure, for example, deformation such as cracks or steps generated on the surface of the structure is detected.

For example, Patent Document 1 proposes a technique for detecting a step formed on the surface of an embankment based on three-dimensional point cloud data representing the surface of the embankment with a plurality of three-dimensional points. The technique described in Patent Document 1 is characterized in that, among a plurality of three-dimensional points representing the surface of an embankment, three-dimensional points in which other three-dimensional points are included in a predetermined number or more in a nearby space are derived from a step. It is extracted as a point, and the position of the step is detected from the position of the extracted feature point.

JP 2015-004588

However, the technique described in Patent Document 1 extracts three-dimensional points as feature points derived from a step, provided that the neighboring space includes a predetermined number or more of other three-dimensional points. Therefore, if the region having a step on the surface of the structure does not satisfy the above conditions, the position of the step generated on the surface of the structure cannot be detected accurately.

The present invention has been made in view of the above, and obtains a deformation detection system capable of accurately detecting the position of a step generated on the surface of a structure from a three-dimensional point cloud representing the surface of the structure. With the goal.

In order to solve the above-mentioned problems and achieve the object, the deformation detection system of the present invention includes an acquisition unit, a data conversion unit, and a step detection unit. The acquisition unit acquires three-dimensional point cloud data, which is data representing the surface of the structure as a three-dimensional point cloud, and shape data indicating the shape of the structure. The data conversion unit sets a reference plane corresponding to the surface of the structure based on the shape data, and sets the three-dimensional point cloud data of one point selected from a plurality of points arranged in a matrix on the reference plane. It is converted into element point cloud data including data of a plurality of element points represented by the position and the amount of deviation from the reference plane. The step detection unit detects the position of the step on the surface of the structure based on the result obtained by applying a Gaussian difference filter to a plurality of element points included in the element point cloud data.

According to the present invention, there is an effect that the position of a step generated on the surface of the structure can be accurately detected from the three-dimensional point cloud representing the surface of the structure.

The figure which shows the structural example of the deformation detection system which concerns on Embodiment 1 of this invention. The figure for demonstrating the flow of the measurement process and the data conversion process by the deformation detection system which concerns on Embodiment 1. The figure for demonstrating the flow of the step detection process by the deformation detection system which concerns on Embodiment 1. The figure which shows the configuration example which further refined the step detection apparatus shown in FIG. The figure for demonstrating the conversion method of the 3D point cloud data into the element point cloud data by the data conversion part which concerns on Embodiment 1. The figure for demonstrating the first method of determining the amount of deviation from a reference plane at an element point by the data conversion part which concerns on Embodiment 1. The figure for demonstrating the second method of determining the amount of deviation from a reference plane at an element point by the data conversion part which concerns on Embodiment 1. The figure for demonstrating the 3rd method of determining the amount of deviation from a reference plane at an element point by the data conversion part which concerns on Embodiment 1. The figure for demonstrating the Gaussian difference processing which concerns on Embodiment 1. The figure which shows an example of the method of detecting the position of a step by the step position detection part which concerns on Embodiment 1. The figure which shows an example of the method of detecting the height of a step by the step height detection part which concerns on Embodiment 1. The figure which shows an example of the method of generating the step vector by the vector generation part which concerns on Embodiment 1. The figure which shows an example of the transformation development drawing which concerns on Embodiment 1. The figure which shows an example of the 3D image which superposed the 3D step vector on the 3D point cloud which represents the inner wall surface of the tunnel which concerns on Embodiment 1. A flowchart showing an example of a processing procedure by the processing unit of the step detection device according to the first embodiment. A flowchart showing an example of step detection processing by the processing unit of the step detection device according to the first embodiment. The figure which shows an example of the hardware composition of the step detection apparatus which concerns on Embodiment 1. The figure which shows the structural example of the step detection apparatus in the deformation detection system which concerns on Embodiment 2 of this invention. The figure which shows an example of the result calculated by the statistical processing part which concerns on Embodiment 2. A flowchart showing an example of a processing procedure by the processing unit of the step detection device according to the second embodiment. The figure which shows the 1st configuration example of the deformation detection system which concerns on Embodiment 3 of this invention. The figure which shows the 2nd configuration example of the deformation detection system which concerns on Embodiment 3. The figure which shows the 3rd configuration example of the deformation detection system which concerns on Embodiment 3. The figure which shows the 4th configuration example of the deformation detection system which concerns on Embodiment 3.

Hereinafter, the deformation detection system and the deformation detection method according to the embodiment of the present invention will be described in detail with reference to the drawings. The present invention is not limited to this embodiment.

Embodiment 1.
FIG. 1 is a diagram showing a configuration example of a deformation detection system according to a first embodiment of the present invention. As shown in FIG. 1, the deformation detection system 100 according to the first embodiment is a traveling type that generates a three-dimensional point cloud data Dpg, which is data of a three-dimensional point cloud representing the surface of a structure as a three-dimensional point cloud. A measuring device 1 and a step detecting device 2 for detecting a step generated on the surface of the structure based on the three-dimensional point cloud data Dpg are provided. In the following, the surface of the structure will be described as the inner wall surface 5a of the tunnel 5, but the surface of the structure is not limited to the inner wall surface 5a of the tunnel 5, and is the surface of a bridge, the surface of a road, or a building. It may be the surface of.

The traveling type measuring device 1 has a laser scanner device (not shown), and generates a three-dimensional point cloud data Dpg of the inner wall surface 5a of the tunnel 5 from the data obtained by the laser scanner device while traveling. The traveling type measuring device 1 is also called MMS. The 3D point cloud data Dpg includes measurement data of a plurality of 3D points, and the measurement data of each 3D point includes data indicating the position of the 3D point in the 3D Cartesian coordinate system. ..

The step detection device 2 includes a communication unit 10, a storage unit 11, and a processing unit 12. The communication unit 10 is communicably connected to the traveling type measuring device 1 via the network 3. The network 3 is, for example, a WAN (Wide Area Network) such as the Internet. The communication unit 10 receives the three-dimensional point cloud data Dpg transmitted from the traveling type measuring device 1 via the network 3.

The storage unit 11 stores the shape data Df of the tunnel 5. The shape data Df is, for example, data showing the cross-sectional shape of the inner wall surface 5a of the tunnel 5. The cross-sectional shape of the inner wall surface 5a is the shape of a line indicating the inner wall surface 5a when it is assumed that the inner wall of the tunnel 5 is cut at a plane orthogonal to the tunnel axial direction. The tunnel axial direction is the direction in which the tunnel 5 extends.

The processing unit 12 includes an acquisition unit 21, a data conversion unit 22, and a step detection unit 23. The acquisition unit 21 acquires the three-dimensional point cloud data Dpg from the traveling type measuring device 1 via the communication unit 10, and acquires the shape data Df from the storage unit 11. The data conversion unit 22 sets a reference plane corresponding to the inner wall surface 5a of the tunnel 5 based on the shape data Df acquired by the acquisition unit 21, and uses the set reference plane to acquire 3 by the acquisition unit 21. The dimension point cloud data Dpg is converted into the element point cloud data De. The element point cloud data De includes data of a plurality of element points represented by the position of one point selected from a plurality of points arranged in a matrix on the reference plane and the amount of deviation from the reference plane.

A plurality of points arranged in a matrix on the reference plane can be said to be grid points when it is assumed that a grid is formed on the reference plane. Hereinafter, each of the plurality of points arranged in a matrix on the reference plane is a grid point. May be described as. The distance between two grid points adjacent to each other in the row direction is the same as the distance between two grid points adjacent to each other in the column direction. One of the row direction and the column direction is the tunnel axial direction, and the other is the tunnel circumferential direction. The tunnel circumferential direction is a direction along a line indicating the inner wall surface 5a when the inner wall surface 5a of the tunnel 5 is cut at a plane orthogonal to the tunnel axis direction.

The step detection unit 23 detects the position of the step generated on the inner wall surface 5a based on the result obtained by applying a Gaussian difference filter to a plurality of element points included in the element point cloud data De. For example, the step detection unit 23 specifies a position where a line connecting two adjacent element points among a plurality of element points after applying a Gaussian difference filter intersects a reference plane, and the specified position is set as an inner wall surface 5a. It is detected as the position of the step that occurs in.

FIG. 2 is a diagram for explaining a flow of measurement processing and data conversion processing by the deformation detection system according to the first embodiment. As shown in FIG. 2, the traveling type measuring device 1 measures the inner wall surface 5a of the tunnel 5 and generates three-dimensional point cloud data Dpg (step S1). The acquisition unit 21 of the step detection device 2 acquires the three-dimensional point cloud data Dpg transmitted from the traveling type measurement device 1 (step S2).

The data conversion unit 22 of the step detection device 2 sets a reference surface Swr corresponding to the inner wall surface 5a of the tunnel 5 based on the shape data Df stored in the storage unit 11, and the three-dimensional point acquired by the acquisition unit 21. A data conversion process for converting the group data Dpg into the element point cloud data De is performed (step S3). The element point cloud data De is a plurality of elements represented by the position of one grid point Pg selected from a plurality of grid points Pg arranged in a matrix on the reference plane Swr and the amount of deviation from the reference plane Swr. It is the data including the data of the point Pe. The reference surface Swr is a surface showing the inner wall surface 5a of the tunnel 5 obtained based on the shape data Df. The reference surface Swr is an inner wall surface 5a of the tunnel 5 assuming that the inner wall surface 5a of the tunnel 5 has no unevenness, and is a virtual surface.

In FIG. 2, in the diagram showing the element point cloud data De, in order to make the position of the element point Pe easy to understand, the element points Pe adjacent to each other in the tunnel axial direction and the element points Pe adjacent to each other in the tunnel circumferential direction are respectively. It is connected by a broken line. Further, the reference plane Swr shown in FIG. 2 is converted into a plane of the development view coordinate system which is an orthogonal coordinate system in which the position in the tunnel axial direction, the position in the tunnel circumferential direction, and the amount of deviation from the reference plane Swr are different axes. It is a reference plane after being made. In the following, for convenience of explanation, the reference plane Swr before conversion to the developed view coordinate system may be described as the reference plane Swr'.

In step S3, the data conversion unit 22 calculates the position of each three-dimensional point included in the three-dimensional point cloud data Dpg with respect to the reference plane Swr'. The calculated position of the three-dimensional point is represented by the position in the tunnel axial direction, the position in the tunnel circumferential direction, and the amount of deviation from the reference plane Swr'. The position in the tunnel axial direction and the position in the tunnel circumferential direction are positions on the reference plane Swr'. Further, the amount of deviation from the reference plane Swr'is expressed by the distance from the reference plane Swr', and the direction orthogonal to the reference plane Swr'and the direction seen from the reference plane Swr'is expressed as the polarity. NS.

Next, the data conversion unit 22 is based on the positions of the three-dimensional points with respect to the reference plane Swr'and the plurality of lattice points Pg arranged in a matrix on the reference plane Swr after conversion to the developed view coordinate system. The 3D point cloud data Dpg is converted into the element point cloud data De of the developed view coordinate system.

For example, the data conversion unit 22 converts the three-dimensional point cloud data Dpg into the development map point cloud data Dpg1 which is the data of the development view coordinate system. The data conversion unit 22 converts the position of each three-dimensional point included in the three-dimensional point cloud data Dpg in the tunnel circumferential direction into a position on one axis indicating the tunnel circumferential direction in the developed view coordinate system. The 3D point cloud data Dpg is converted into the developed map point cloud data Dpg1.

The data conversion unit 22 has a position in the tunnel axial direction and a position in the tunnel circumferential direction among a plurality of three-dimensional points included in the developed map point cloud data Dpg1 within a predetermined range from the position of one lattice point Pg. One or more three-dimensional points are assigned to one lattice point Pg. The data conversion unit 22 determines the position of one assigned grid point Pg in the tunnel axial direction and the position in the tunnel circumferential direction as the position of the element point Pe in the tunnel axial direction and the position in the tunnel circumferential direction.

Further, the data conversion unit 22 calculates the amount of deviation from the reference surface Swr at the element point Pe from the amount of deviation of one or more three-dimensional points assigned to one grid point Pg from the reference surface Swr. For example, the data conversion unit 22 averages the deviation amount of one or more three-dimensional points assigned to one grid point Pg from the reference plane Swr, and sets the value obtained by averaging the deviation from the reference plane Swr at the element point Pe. Can be quantity.

The data conversion unit 22 performs the process of allocating one or more three-dimensional points to one grid point Pg and calculating the element point Pe for each grid point Pg in this way, so that the developed map point cloud data Dpg1 is used as an element. Convert to point cloud data De.

FIG. 3 is a diagram for explaining the flow of the step detection process by the deformation detection system according to the first embodiment. In the figure showing the element point cloud data De in FIG. 3, similarly to the element point cloud data De shown in FIG. 2, the element points Pe are connected by a broken line in order to make the position of the element point Pe easy to understand. ..

As shown in FIG. 3, the step detection unit 23 performs Gaussian difference processing, which is a process of applying a Gaussian difference filter to a plurality of element points Pe included in the element point cloud data De converted by the data conversion unit 22. .. The Gaussian difference filter is also called DoG (Difference of Gaussian). The Gaussian differential process includes a first filter process (step S4), a second filter process (step S5), and a difference calculation process (step S6).

The step detection unit 23 performs a process of applying a first Gaussian filter to a plurality of element points Pe included in the element point cloud data De as the first filter process of step S4. Further, the step detection unit 23 performs a process of applying a second Gaussian filter to a plurality of element points Pe included in the element point cloud data De as the second filter process of step S5. The first Gaussian filter and the second Gaussian filter perform smoothing processing on the amount of deviation of the element point Pe from the reference plane Swr.

The first Gaussian filter and the second Gaussian filter are Gaussian filters having different characteristics from each other, and the values of at least some parameters are different from each other. The step detection unit 23 calculates the difference between the output of the first Gaussian filter obtained by performing the first filter processing and the output of the second Gaussian filter obtained by performing the second filter processing in the difference calculation process of step S6. do.

By applying the Gaussian difference filter to the plurality of element points Pe, the difference in the amount of deviation of the plurality of element points Pe from the reference plane Swr is output from the Gaussian difference filter. The difference in the amount of deviation from the reference surface Swr is the amount of deviation from the reference surface Swr obtained by applying the first Gaussian filter to the amount of deviation from the reference surface Swr at the element point Pe and the reference surface Swr at the element point Pe. It is a difference from the deviation amount from the reference plane Swr obtained by applying the second Gaussian filter to the deviation amount from.

The difference in the amount of deviation from the reference surface Swr is obtained, for example, by applying the first Gaussian filter to the element point Pe from the amount of deviation from the reference surface Swr obtained by applying the second Gaussian filter to the element point Pe. It is obtained by subtracting the amount of deviation from the reference plane Swr. The position of the element point Pe after applying the Gaussian difference filter is represented by the position in the tunnel axial direction, the position in the tunnel circumferential direction, and the difference in the amount of deviation from the reference plane Swr.

Next, the step detection unit 23 detects the position of the step based on the result obtained by applying the Gaussian difference filter to the plurality of element points Pe of the element point cloud data De (step S7). In step S7, the step detection unit 23 identifies the position where the zero intersection occurs based on the difference in the amount of deviation from the reference plane Swr for each element point Pe obtained as a result of the difference calculation processing. The position where the zero intersection occurs is the position where the difference in the amount of deviation from the reference plane Swr becomes zero, and is represented by the position in the tunnel axial direction and the position in the tunnel circumferential direction.

Specifically, the step detection unit 23 handles the element point Pe in an orthogonal coordinate system in which the position in the tunnel axial direction, the position in the tunnel circumferential direction, and the difference in the amount of deviation from the reference plane Swr are different axes. In the step detection unit 23, the line connecting two element points Pe that are adjacent to each other in the tunnel axial direction or the tunnel circumferential direction and have different polarities in the difference value of the amount of deviation from the reference plane Swr obtained by applying the Gaussian difference filter is zero. Identify the zero intersection, which is the intersection position. The zero intersection is represented by a position in the tunnel axial direction and a position in the tunnel circumferential direction. The step detection unit 23 detects the position of the zero intersection as the position of the step generated on the inner wall surface 5a of the tunnel 5.

When the traveling type measuring device 1 measures the inner wall surface 5a of the tunnel 5 with the laser scanner device while traveling in the tunnel 5, the laser from the laser scanner device of the traveling type measuring device 1 is applied to the inner wall surface 5a of the tunnel 5. It is irradiated facing directly. Therefore, the irradiation direction of the laser becomes parallel to the step generated on the inner wall surface 5a of the tunnel 5, and the difference in density of the three-dimensional points due to the step in the three-dimensional point group of the tunnel 5 obtained by the measurement of the traveling type measuring device 1 becomes large. Hard to appear. Further, since the step on the inner wall surface 5a of the tunnel 5 is a step due to floating or peeling and its height is relatively low, this also means that the step on the inner wall surface 5a of the tunnel 5 is a step in the three-dimensional point cloud. The difference in density of three-dimensional points is unlikely to occur.

The processing unit 12 of the step detection device 2 detects the position of the step on the inner wall surface 5a of the tunnel 5 based on the result obtained by applying the Gaussian difference filter to the plurality of element points Pe. Therefore, the processing unit 12 accurately determines the position of the step generated on the inner wall surface 5a of the tunnel 5 even when the difference in density of the three-dimensional points due to the step is small in the three-dimensional point cloud representing the inner wall surface 5a of the tunnel 5. Can be detected.

Further, the element points Pe are arranged at equal intervals in each of the tunnel axial direction and the tunnel circumferential direction, and one or more three-dimensional points are assigned to each element point Pe. Therefore, a plurality of element points Pe are arranged at equal intervals in each of the tunnel axial direction and the tunnel circumferential direction. As a result, the processing unit 12 can detect the step with the same accuracy over the entire inner wall surface 5a of the tunnel 5.

Further, since the processing unit 12 can assign a plurality of three-dimensional points to one element point Pe, the number of three-dimensional points included in the three-dimensional point cloud represented by the three-dimensional point cloud data Dpg is compared with the number of three-dimensional points. The number of element points Pe included in the element point cloud represented by the element point cloud data De can be reduced. Therefore, the load on the processing unit 12 in the processing of the Gaussian difference filter can be reduced.

Hereinafter, the deformation detection system 100 according to the first embodiment will be described in more detail. In addition to the laser scanner device, the traveling type measuring device 1 shown in FIG. 1 has an imaging unit that captures a two-dimensional image, and an image of the inner wall surface 5a of the tunnel 5 is obtained from the imaging data obtained by the imaging unit while traveling. Generate data Dimg. The image data Dimg is data showing the entire image of the inner wall surface 5a of the tunnel 5 obtained by connecting the captured images, which are the images captured by the imaging unit.

The traveling type measuring device 1 periodically measures the inner wall surface 5a of the tunnel 5, and generates three-dimensional point cloud data Dpg and image data Dimg each time the measurement is performed. The traveling type measuring device 1 transmits the three-dimensional point cloud data Dpg and the image data Dimg to the step detecting device 2 via the network 3 every time the inner wall surface 5a of the tunnel 5 is measured. The three-dimensional point cloud data Dpg and the image data Dimg each include information on the measurement time, which is the time measured by the traveling type measuring device 1.

Next, the configuration of the step detection device 2 will be described more specifically. FIG. 4 is a diagram showing a configuration example in which the step detection device shown in FIG. 1 is further refined. As shown in FIG. 4, the step detection device 2 includes an input unit 13 and a display unit 14 in addition to the configuration of the step detection device 2 shown in FIG.

The communication unit 10 receives the data generated by the traveling type measuring device 1 shown in FIG. 1 via the network 3. Specifically, the communication unit 10 receives the three-dimensional point cloud data Dpg and the image data Dimg from the traveling type measuring device 1 via the network 3.

The storage unit 11 stores the shape data Df, the three-dimensional point cloud data Dpg, the image data Dimg, and the deformation data Ddf. The shape data Df is data indicating the cross-sectional shape of the tunnel 5. The shape data Df may be data on the cross-sectional shape of the inner wall surface 5a obtained from the three-dimensional point cloud data Dpg instead of the data on the cross-sectional shape of the inner wall surface 5a obtained from the design drawing.

The processing unit 12 can generate shape data Df by, for example, estimating the cross-sectional shape of the inner wall surface 5a by applying a pillar to the three-dimensional point cloud of the inner wall surface 5a indicated by the three-dimensional point cloud data Dpg. .. Further, the processing unit 12 sets a plurality of three-dimensional points within a set distance from the orthogonal plane, which is a plane orthogonal to the tunnel axis direction, among the plurality of three-dimensional points included in the three-dimensional point group data Dpg. It is also possible to generate shape data Df by projecting onto a curve and approximating a plurality of projected points with a curve.

Further, the shape data Df may be data indicating the overall shape of the inner wall surface 5a of the tunnel 5. The processing unit 12 can generate shape data Df by, for example, applying a pillar to the three-dimensional point cloud of the inner wall surface 5a represented by the three-dimensional point cloud data Dpg and estimating the overall shape of the inner wall surface 5a. ..

As described above, the three-dimensional point cloud data Dpg and the image data Dimg are data generated by the traveling type measuring device 1 and received by the communication unit 10, and include the time information described above.

The deformation data Ddf includes data indicating the type, range, time, and the like of the deformation that occurred on the inner wall surface 5a. Types of deformation include cracks, steps, peeling, and water leaks. The range of deformation is the range of deformation on the inner wall surface 5a, for example, the range on the reference plane Swr'or the range on the reference plane Swr. The time of the deformation is the time when the deformation is detected, for example, the time indicated by the time information included in the three-dimensional point cloud data Dpg in which the deformation is detected.

The processing unit 12 includes an acquisition unit 21, a data conversion unit 22, a step detection unit 23, a step evaluation unit 24, an attribute setting unit 25, a coordinate conversion unit 26, and a display processing unit 27. The acquisition unit 21 acquires the three-dimensional point cloud data Dpg and the image data Dimg from the traveling type measuring device 1 via the network 3, and stores the acquired three-dimensional point cloud data Dpg and the image data Dimg in the storage unit 11. The acquisition unit 21 can also acquire the three-dimensional point cloud data Dpg and the image data Dimg from a recording medium (not shown) in which the three-dimensional point cloud data Dpg and the image data Dimg are recorded.

The data conversion unit 22 sets the reference plane Swr corresponding to the inner wall surface 5a based on the shape data Df, and converts the three-dimensional point cloud data Dpg into the element point cloud data De. As described above, the element point cloud data De is represented by the position of one grid point Pg selected from a plurality of grid points Pg arranged in a matrix on the reference plane Swr and the amount of deviation from the reference plane Swr. The data of a plurality of element points Pe to be performed is included. The data conversion unit 22 can perform the above-described processing for generating the shape data Df from the three-dimensional point cloud data Dpg.

FIG. 5 is a diagram for explaining a method of converting the three-dimensional point cloud data into the element point cloud data by the data conversion unit according to the first embodiment. FIG. 6 is a diagram for explaining a first method of determining the amount of deviation from the reference plane at the element point by the data conversion unit according to the first embodiment. FIG. 7 is a diagram for explaining a second method of determining the amount of deviation from the reference plane at the element point by the data conversion unit according to the first embodiment. FIG. 8 is a diagram for explaining a third method of determining the amount of deviation from the reference plane at the element point by the data conversion unit according to the first embodiment.

The data conversion unit 22 specifies the overall shape of the inner wall surface 5a of the tunnel 5 shown in FIG. 5 based on the shape data Df. For example, the data conversion unit 22 specifies the overall shape of the inner wall surface 5a by making the cross-sectional shape of the inner wall surface 5a continuous in the tunnel axial direction. Further, when the shape data Df is three-dimensional data indicating the overall shape of the inner wall surface 5a, the data conversion unit 22 can specify the entire shape of the inner wall surface 5a from the three-dimensional data included in the shape data Df.

Then, the data conversion unit 22 sets the entire shape of the specified inner wall surface 5a as the reference surface Swr'. The data conversion unit 22 projects each three-dimensional point included in the three-dimensional point cloud data Dpg from a direction orthogonal to the reference plane Swr'to the reference plane Swr'. The data conversion unit 22 calculates the position of each three-dimensional point projected on the reference plane Swr'on the reference plane Swr'and the amount of deviation of each three-dimensional point before projection from the reference plane Swr'. The magnitude of the amount of deviation from the reference plane Swr'represents the distance from the reference plane Swr' at the three-dimensional point, and the polarity of the amount of deviation from the reference plane Swr'is in the direction orthogonal to the reference plane Swr' and is the reference plane. Represents the direction seen from Swr'.

Next, the data conversion unit 22 develops the three-dimensional point cloud data Dpg based on the position on the reference plane Swr'after projection and the deviation amount from the reference plane Swr' before projection at each three-dimensional point. It is converted into the development map point cloud data Dpg1 which is the system data. The development view coordinate system is a Cartesian coordinate system in which the position in the tunnel axial direction, the position in the tunnel circumferential direction, and the amount of deviation from the reference plane Swr are different axes. The development view point cloud data Dpg1 includes data of a plurality of three-dimensional points represented by a position in the tunnel axial direction, a position in the tunnel circumferential direction, and a deviation amount from the reference plane Swr.

The data conversion unit 22 converts the position on the reference plane Swr'after the projection of the three-dimensional point to the position on the reference plane Swr'of the three-dimensional point, and the amount of deviation from the reference plane Swr' before projection is the reference plane Swr. The three-dimensional point cloud data Dpg is converted into the developed map point cloud data Dpg1 by setting the amount of deviation from. The magnitude of the amount of deviation from the reference plane Swr represents the distance from the reference plane Swr at the three-dimensional point, and the polarity of the amount of deviation from the reference plane Swr is in the direction orthogonal to the reference plane Swr and is viewed from the reference plane Swr. Indicates the direction.

Next, in the data conversion unit 22, the position in the tunnel axial direction and the position in the tunnel circumferential direction among the plurality of three-dimensional points included in the developed map point cloud data Dpg1 are predetermined from the position of one lattice point Pg. One or more 3D points within the range are assigned to one grid point Pg. The data conversion unit 22 sets the position of one assigned grid point Pg in the tunnel axial direction and the position in the tunnel circumferential direction to the position of the element point Pe in the tunnel axial direction and the position in the tunnel circumferential direction.

Further, the data conversion unit 22 determines the amount of deviation of the element point Pe from the reference surface Swr from the amount of deviation of one or more three-dimensional points assigned to one lattice point Pg from the reference surface Swr. For example, as shown in FIG. 6, the data conversion unit 22 can determine the amount of deviation of the three-dimensional point closest to the grid point Pg from the reference surface Swr as the amount of deviation of the element point Pe from the reference surface Swr. can. The three-dimensional point closest to the grid point Pg is the three-dimensional point having the shortest linear distance from the grid point Pg.

Further, as shown in FIG. 7, the data conversion unit 22 sets the value obtained by averaging the amount of deviation of the three-dimensional point within the predetermined range from the reference plane Swr from the grid point Pg as the element point Pe. The amount of deviation from the reference plane Swr can be determined. In the example shown in FIG. 7, hatching is attached to the grid point Pg within a predetermined range from the grid point Pg. As shown in FIG. 7, since there are seven three-dimensional points within a predetermined range from the grid point Pg, the average value of the deviation amounts of these seven three-dimensional points from the reference plane Swr is an element. It is determined by the amount of deviation of the point Pe from the reference plane Swr.

Further, as shown in FIG. 8, when a plurality of three-dimensional points within a predetermined range from the grid point Pg are arranged in a direction orthogonal to the reference plane Swr, the data conversion unit 22 is in the middle of the arrangement order. The value of the three-dimensional point located at can be determined as the amount of deviation of the element point Pe from the reference plane Swr. In the example shown in FIG. 8, hatching is attached to the grid points Pg within a predetermined range from the grid points Pg. As shown in FIG. 8, there are seven three-dimensional points within a predetermined range from the lattice point Pg, and when the seven three-dimensional points are arranged in the direction orthogonal to the reference plane Swr, the arrangement order is as follows. The amount of deviation of the three-dimensional point at the fourth position from the reference surface Swr is determined by the amount of deviation of the element point Pe from the reference surface Swr.

The data conversion unit 22 performs the process of allocating one or more three-dimensional points to one lattice point Pg and calculating the element point Pe for each lattice point Pg, thereby generating the developed map point cloud data Dpg1. Convert to element point cloud data De. The data conversion unit 22 is configured to directly convert the three-dimensional point cloud data Dpg to the element point cloud data De without, for example, converting the three-dimensional point cloud data Dpg to the developed view point cloud data Dpg1. You may.

The data conversion unit 22 can also arrange a plurality of grid points Pg on the reference plane Swr'. In this case, the data conversion unit 22 performs a process of assigning one or more three-dimensional points to one grid point Pg on the reference plane Swr'for each grid point Pg, and then converts the data into the data of the developed view coordinate system. By doing so, the element point cloud data De is generated.

Returning to FIG. 4, the description of the processing unit 12 will be continued. The step detection unit 23 of the processing unit 12 performs Gaussian difference processing, which is a process of applying a Gaussian difference filter to the element point cloud data De converted by the data conversion unit 22. FIG. 9 is a diagram for explaining the Gaussian difference processing according to the first embodiment. Note that the y-axis is omitted in the figures other than the figure showing the step function in FIG.

The Gaussian difference filter is a filter that outputs the difference between the outputs of two different first Gaussian filters and the second Gaussian filter using the Gaussian function shown in the following equation (1). In the following equation (1), "x" is the coordinate of the x-axis in the xy coordinate system, "y" is the coordinate of the y-axis in the xy coordinate system, and "σ" is the parameter.

Here, it is assumed that the step generated on the inner wall surface 5a of the tunnel 5 is represented by the step function s (x, y) represented by the following equation (2). In the following formula (2), "a" indicates the size of the step, that is, the height of the step. The step function s (x, y) shown in the following equation (2) is represented as shown in FIG. Further, in the following equation (2), the position where the x-axis coordinate is zero is the edge position, and the position where the x-axis coordinate is zero is the step position. The x-coordinate indicates the position of one of the tunnel axial position and the tunnel circumferential position at the element point Pe, and the y-coordinate indicates the other of the tunnel axial position and the tunnel circumferential position at the element point Pe. Indicates the position. In the following, it is assumed that the x-coordinate is the position in the tunnel axis direction and the y-coordinate is the position in the tunnel circumferential direction.

The first Gaussian filter is represented by the following equation (3). In the following equation (3), "S _σ1 (x, y)" indicates the output of the first Gaussian filter at an arbitrary position in the xy coordinate system.

In the above equation (3), "*" is a convolution, "σ ₁ " is a parameter of the first Gaussian filter, and "u" is a tunnel axis at an element point Pe other than the element point Pe that is the point of interest. The position of the direction. Further, in the above equation (3), "v" is a position in the tunnel circumferential direction at an element point Pe other than the element point Pe which is the point of interest, and "a" is the step function s shown in the above equation (2). The size of the step (x, y).

The second Gaussian filter is expressed by the following equation (4). In the following equation (4), "S _{σ 2} (x, y)" indicates the output of the second Gaussian filter at an arbitrary position in the xy coordinate system, and "σ ₂ " is a parameter of the second Gaussian filter. Since the parameter values of the first Gaussian filter and the second Gaussian filter are different from each other, the characteristics of the first Gaussian filter and the characteristics of the second Gaussian filter are different from each other as shown in FIG. In the example shown in FIG. 9, σ ₂ > σ ₁ . Further, in the figure showing the characteristics of the first Gaussian filter in FIG. 9, the horizontal axis shows the coordinates of the x-axis and the vertical axis shows the output of the first Gaussian filter. Further, in the figure showing the characteristics of the second Gaussian filter in FIG. 9, the horizontal axis shows the coordinates of the x-axis and the vertical axis shows the output of the second Gaussian filter.

Then, the Gaussian difference filter is expressed by the following equation (5). In the following equation (5), "H (x, y)" indicates the output of the Gaussian difference filter. As shown in the following equation (5) and FIG. 9, the characteristic of the Gaussian difference filter is zero when the operation result of the step function s (x, y) changes to "a", that is, x = 0. Cross. Since the step position is the position of x = 0 as described above, the step detection unit 23 detects the position where the zero intersection occurs as the position of the step. In the figure showing the characteristics of the Gaussian difference filter in FIG. 9, the horizontal axis shows the coordinates of the x-axis and the vertical axis shows the output of the Gaussian difference filter.

As shown in FIG. 4, the step detection unit 23 includes a step position detection unit 31, a step height detection unit 32, and a vector generation unit 33. The step position detection unit 31 of the step detection unit 23 calculates the Gaussian difference filter represented by the above equation (5) on the element point cloud data De. Specifically, the step position detection unit 31 performs the calculation of the above equation (5) for each element point Pe included in the element point cloud data De. Then, the step position detection unit 31 determines the position where the zero intersection occurs as the step position based on the output of the Gaussian difference filter.

Further, the slope ∇H (x, y) at the position where the zero intersection occurs in the curve showing the characteristics of the Gaussian difference filter shown in FIG. 9 can be obtained by the calculation of the following equation (6). In the following equation (6), when the position where the zero intersection occurs is x = 0, it is expressed as the following equation (7).

Then, the absolute value of ∇H (0, y) can be expressed by the following equation (8). By substituting the absolute value of ∇H (0, y), the value of "σ ₁ ", and the value of "σ ₂ " into the following formula (8), the height of the step "a" is calculated. Can be done.

FIG. 10 is a diagram showing an example of a method of detecting the position of a step by the step position detecting unit according to the first embodiment. In FIG. 10, the positions of the four element points Pe after the Gaussian difference filter is applied are indicated by black circles. The position of the element point Pe shown in FIG. 10 is a position in a Cartesian coordinate system in which the position in the tunnel axial direction, the position in the tunnel circumferential direction, and the difference in the amount of deviation from the reference plane Swr are different axes. As described above, the difference in the amount of deviation from the reference surface Swr is the amount of deviation from the reference surface Swr obtained by applying the first Gaussian filter to the element point Pe and the second Gaussian filter with respect to the element point Pe. It is a difference from the amount of deviation from the reference plane Swr obtained by multiplying by.

_{“H (x n} , y _n )”, “H (x _{n + 1} , y _n )”, “H (x _n , y _{n + 1} )”, and “H (x _{n + 1} , y _{n + 1} )” shown in FIG. 10 are The difference in the amount of deviation from the reference plane Swr obtained by applying a Gaussian difference filter to the four element points Pe is shown. n is an integer. Specifically, "H (x _n , y _n )" is an element point Pe (x _n , y) whose position in the tunnel axial direction is "x _n " and whose position in the tunnel circumferential direction is "y _n". It is the difference of the amount of deviation from the reference plane Swr obtained by applying the Gaussian difference filter to _n). “H (x _{n + 1} , y _n )” is relative to the element point Pe (x _{n + 1} , y _n ) whose position in the tunnel axial direction is “x _{n + 1} ” and whose position in the tunnel circumferential direction is “y _n”. It is the difference of the amount of deviation from the reference plane Swr obtained by applying the Gaussian difference filter.

“H (x _n , y _{n + 1} )” is relative to the element point Pe (x _n , y _{n + 1} ) whose position in the tunnel axial direction is “x _n ” and whose position in the tunnel circumferential direction is “y _{n + 1”.} It is the difference of the amount of deviation from the reference plane Swr obtained by applying the Gaussian difference filter. “H (x _{n + 1} , y _{n + 1} )” is for the element point Pe (x _{n + 1} , y _{n + 1} ) whose position in the tunnel axis direction is “x _{n + 1} ” and whose position in the tunnel circumferential direction is “y _{n + 1”.} It is the difference of the amount of deviation from the reference plane Swr obtained by applying the Gaussian difference filter. “X _n ” and “x _{n + 1} ” indicate the positions of two element points Pe whose positions in the tunnel axial direction are adjacent to each other, and “y _n ” and “y _{n + 1} ” indicate positions in the tunnel circumferential direction are adjacent to each other. The positions of the two element points Pe are shown.

The step position detection unit 31 determines the zero intersection, which is the position where the line connecting the two element points Pe having adjacent positions in the tunnel axis direction intersects zero among the plurality of element points Pe obtained by applying the Gaussian difference filter. do. For example, the step position detection unit 31 determines the difference in the amount of deviation from the reference plane Swr between _{the element points Pe (x n} , y _n ) and the element points Pe (x _{n + 1} , y _n ) whose positions in the tunnel axial direction are adjacent to each other. Determine if the polarities are different. That is, the step position detection unit 31 determines whether or not the polarities of _{"H (x n} , y _n )" and "H (x _{n + 1} , y _{n)" are different.} It stepped position detection unit 31, element point Pe _{(x n, y} n) _{"H (x n, y} n)" in the element point _{Pe (x n + 1, y} n) in the _{"H (x n + 1, y} n) " When it is determined that the polarities are different from each other, the element point Pe (x _n , y _n ) and the element point Pe (x _{n + 1} , y _n ) are internally divided to obtain the zero intersection. _{For example, the position x c} of the zero intersection, which is the position where the zero intersection occurs in the tunnel axis direction, can be calculated by the calculation of the following equation (9).

Further, the step position detection unit 31 is a zero intersection where the lines connecting the two element points Pe that are adjacent to each other in the tunnel circumferential direction among the plurality of element points Pe obtained by applying the Gaussian difference filter intersect at zero. To judge. For example, the step position detection unit 31 determines the difference in the amount of deviation from the reference plane Swr between _{the element points Pe (x n} , y _n ) and the element points Pe (x _n , y _{n + 1} ) whose positions in the tunnel circumferential direction are adjacent to each other. Determine if the polarities are different. That is, the step position detection unit 31 determines whether or not the polarities of _{"H (x n} , y _n )" and "H (x _n , y _{n + 1)" are different.} It stepped position detection unit 31, element point Pe _{(x n, y} n) _{"H (x n, y} n)" in the element point _{Pe (x n, y n +} 1) in the _{"H (x n, y n +} 1) " When it is determined that the polarities are different, the element point Pe (x _n , y _n ) and the element point Pe (x _n , y _{n + 1} ) are internally divided to obtain the zero intersection. _{For example, the position y c} of the zero intersection, which is the position where the zero intersection occurs in the tunnel circumferential direction, can be calculated by the calculation of the following equation (10).

The step position detection unit 31 sets the position of the element point Pe having the smaller absolute value of the difference in the amount of deviation from the reference surface Swr among the two element points Pe having different polarities of the difference in the amount of deviation from the reference surface Swr. Can be detected as the position of. In this way, the step position detection unit 31 can also detect a position close to the zero intersection as the step position.

Next, the step height detection unit 32 shown in FIG. 4 will be described. The step height detecting unit 32 detects the height of the step generated on the inner wall surface 5a. FIG. 11 is a diagram showing an example of a method of detecting the height of a step by the step height detecting unit according to the first embodiment. In FIG. 11, the positions of the four element points Pe after the Gaussian difference filter is applied as in FIG. 10 are indicated by black circles.

The step height detection unit 32 obtains the slope ∇H (x, y) at the zero intersection at the output H (x, y) of the Gaussian difference filter. The step height detection unit 32 calculates the step height based on the slope ∇H (x, y) at the zero intersection.

In the example shown in FIG. 11, the step height detection unit 32 sets the inclination ∇H (x, y) at the zero intersection P _{(x c} , y _n ) and the zero intersection P (x _{n + 1} , y _{c) to the zero intersection P.} It is calculated for each step vector described later obtained by connecting (x _c , y _n ) and the zero intersection P (x _{n + 1} , y _c). For example, the step height detection unit 32 has _{an element point Pe (x n} , y) as an inclination ∇H (x, y) at _{the zero intersection P (x c} , y _n ) and the zero intersection P (x _{n + 1} , y _{c). Find the slope of the plane containing n} ), the element point Pe (x _{n + 1} , y _n ), and the element point Pe (x _{n + 1} , y _{n + 1} ) with respect to the zero plane. The zero plane is the plane where the output H (x, y) of the Gaussian difference filter becomes zero.

Further, the step height detection unit 32 has _{an element point Pe (x n} , y) as an inclination ∇H (x, y) at _{the zero intersection P (x c} , y _n ) and the zero intersection P (x _{n + 1} , y _c). It is also possible to obtain the slope of the surface including _n ), element point Pe (x _{n + 1} , y _n ), element point Pe (x _{n + 1} , y _{n + 1} ), and element point Pe (x _{n + 1} , y _{n + 1) with respect to the zero plane.} ..

As described above, the step height detection unit 32 is three or more adjacent element points Pe among the plurality of element points Pe after the Gaussian difference filter is applied, and is at least one element point Pe and other and remaining elements. The height of the step is detected by setting the slope of the surface including the three or more element points Pe having different polarities of the values obtained by applying the Gaussian difference filter with the point Pe as the slope ∇H (x, y).

Further, the step height detection unit 32 determines the slope of the line connecting the two element points Pe having different polarities of the output H (x, y) of the Gaussian difference filter, which is a value obtained by applying the Gaussian difference filter, with respect to the zero plane. The height of the step can be detected as the slope ∇H (x, y). In the example shown in FIG. 11, the step height detection unit 32 has _{an element point Pe (x n} , y _n ) and an element point Pe as an inclination ∇ H (x, y) at the _{zero intersection P (x c} , y _n). The slope of the line connecting (x _{n + 1} , y _n ) with respect to the zero plane can be obtained as the slope ∇H (x, y).

Further, in the example shown in FIG. 11, the step height detection unit 32 has _{an element point Pe (x n + 1} , y _n ) and an element as an inclination ∇ H (x, y) at the _{zero intersection P (x n + 1} , y _c). The slope of the line connecting the points Pe (x _{n + 1} , y _{n + 1} ) with respect to the zero plane can be obtained as the slope ∇H (x, y). Further, the step height detection unit 32 has _{an element point Pe (x n} , y) as an inclination ∇H (x, y) at _{the zero intersection P (x c} , y _{n + 1} ) and the zero intersection P (x _{n + 1} , y _{c). The} slope of the line connecting n) and the element point Pe (x _{n + 1} , y _n ) with respect to the zero plane, and the zero of the line connecting the element point Pe (x _{n + 1} , y _n ) and the element point Pe (x _{n + 1} , y _{n + 1} ). The average value of the slope with respect to the plane can also be obtained as the slope ∇H (x, y).

Then, the step height detection unit 32 detects the step height “a” by the calculation of the following equation (11). The following formula (11) can be obtained based on the above formula (8).

Next, the vector generation unit 33 will be described. The vector generation unit 33 detects the height of the step generated on the inner wall surface 5a. FIG. 12 is a diagram showing an example of a method of generating a step vector by the vector generation unit according to the first embodiment. In FIG. 12, the positions of the four element points Pe after the Gaussian difference filter is applied as in FIG. 10 are indicated by black circles.

When there are two or more zero intersections in the mesh formed by connecting four adjacent element points Pe with a straight line, the vector generation unit 33 generates a line connecting the zero intersections as a unit vector of the step vector. When 10 element points Pe are arranged in each of the tunnel axial direction and the tunnel circumferential direction, the number of meshes formed by connecting four adjacent element points Pe with a straight line is 81. The vector generation unit 33 performs a process of generating a line connecting the zero intersections as a unit vector when there are two or more zero intersections for each mesh. In the example shown in FIG. 12, the vector generation unit 33 _{generates a line connecting the zero intersection P (x c} , y _n ) and the zero intersection P (x _{n + 1} , y _c ) as a unit vector.

The vector generation unit 33 connects the unit vectors of the adjacent meshes that share the zero intersection among the adjacent meshes that are adjacent to the mesh that generated the unit vector as continuous unit vectors. As a result, the vector generation unit 33 can generate a step vector corresponding to a continuous step. If there is no unit vector in any of the adjacent meshes, the vector generation unit 33 treats one unit vector as a step vector.

The vector generation unit 33 can also simplify the step vector by reducing the nodes of the step vector. For example, the vector generation unit 33 can reduce the number of steps of the step vector by using the Douglas-Pucker method or the like for the generated step vector. The node of the step vector is formed at the position of the zero intersection when the angle formed by the two consecutive unit vectors constituting the step vector is an angle other than 0 degrees.

The step detection unit 23 stores the step data including the information indicating the position of the detected step, the information indicating the height of the detected step, and the information indicating the generated step vector in the storage unit Ddf. The variant data Ddf is updated by adding to. Further, the step detection unit 23 can add the time information included in the three-dimensional point cloud data Dpg used for detecting the step to the step data.

Returning to FIG. 4, the description of the processing unit 12 will be continued. The step evaluation unit 24 of the processing unit 12 determines a state level representing the state of the step based on the height of the step and the spread of the step detected by the step detection unit 23. The step evaluation unit 24 can add information indicating the determined step level to the step data.

The step evaluation unit 24 calculates the spread on the reference surface Swr as the spread of the step. The spread of the step vector is calculated from, for example, at least one of the length of the step vector and the magnitude of the two-dimensional range in which the step vector exists. The magnitude of the two-dimensional range in which the step vector exists is, for example, the area of the smallest rectangle that can enclose the step vector.

The step evaluation unit 24 can determine the state level of the step by using an arithmetic expression or a table in which the height of the step and the spread of the step are parameters. The state level indicates, for example, the degree of risk of the step or the degree of deterioration of the step. For example, the step evaluation unit 24 can determine one level from three levels including the first level, the second level, and the third level for each step vector generated by the step detection unit 23. The first level is, for example, a level indicating that the step is not in a dangerous state. The second level is, for example, a level indicating that the step is not a dangerous state but requires attention. The third level is, for example, a level indicating that the step is in a dangerous state.

The step evaluation unit 24 can determine the state level of the step from two levels, and can also determine the state level of the step from four or more levels. The step evaluation unit 24 can add information indicating the state level of the determined step to the step data. In this case, the step data includes information indicating the position of the detected step, information indicating the height of the detected step, information indicating the generated step vector, and information indicating the state level of the step.

The attribute setting unit 25 adds information such as the length of the step vector and the spread of the step calculated by the step evaluation unit 24 to the step data stored in the storage unit 11 as the attribute information of the step. Further, the attribute setting unit 25 adds the step data in which the attribute information of the step input by the input unit 13 is stored in the storage unit 11. The step attribute information input by the input unit 13 is information indicating the step attribute such as an identification number and a comment. The attribute setting unit 25 associates the attribute information of the step with the information of the step vector included in the step data. The input unit 13 is, for example, a keyboard or the like. In this way, the attribute setting unit 25 can include the attribute information of the step in the step data.

The coordinate conversion unit 26 uses the step vector information included in the step data of the deformation data Ddf stored in the storage unit 11 to obtain the step vector from the coordinates of the above-mentioned developed view coordinate system to the above-mentioned three-dimensional orthogonal coordinate system. Convert to the coordinates of. The coordinate conversion unit 26 can add information indicating the coordinates of the step vector of the converted three-dimensional Cartesian coordinate system to the step data stored in the storage unit 11 as three-dimensional step vector data. As a result, the step data includes the step vector information of the developed view coordinate system and the step vector information of the three-dimensional Cartesian coordinate system.

Further, the coordinate conversion unit 26 performs coordinate conversion of the image of the tunnel 5 so as to follow the shape of the inner wall surface 5a of the tunnel 5 based on the deformation data Ddf and the image data Dimg stored in the storage unit 11, and the tunnel. It is also possible to generate a three-dimensional image of 5. Further, the coordinate conversion unit 26 converts the three-dimensional point cloud of the inner wall surface 5a of the tunnel 5 from the three-dimensional coordinate system to the developed view coordinate system based on the three-dimensional point cloud data Dpg stored in the storage unit 11. The converted three-dimensional point cloud is projected onto the developed view plane, and the two-dimensional point cloud data in which the projected plurality of points are the two-dimensional point cloud can be stored in the storage unit 11.

Based on the shape data Df and the deformation data Ddf stored in the storage unit 11, the display processing unit 27 displays a figure showing the step vector and information showing the step attribute with respect to the developed view represented by the reference plane Swr. A two-dimensional image showing a modified development view, which is an added figure, can be displayed on the display unit 14. The display processing unit 27 superimposes an image showing a deformation development diagram on the captured image of the inner wall surface 5a of the tunnel 5 based on the shape data Df, the deformation data Ddf, and the image data Dimg stored in the storage unit 11. The two-dimensional image can be displayed on the display unit 14. Further, the display processing unit 27 displays a two-dimensional image in which the two-dimensional point group is superimposed on the deformation development diagram based on the shape data Df, the deformation data Ddf, and the two-dimensional point group data stored in the storage unit 11. It can also be displayed on the unit 14.

FIG. 13 is a diagram showing an example of a modified development diagram according to the first embodiment. The modified development view 80 shown in FIG. 13 includes an outer frame 81 in which the outer shape of the inner wall surface 5a of the tunnel 5 is developed, step vectors 82a, 82b, 82c, 82d, and attribute information 83a, 83b, 83c, 83d. The step vectors 82a, 82b, 82c, and 82d are two-dimensional figures showing the step vectors.

The attribute information 83a, 83b, 83c, and 83d are information indicating the length of the step vector. The attribute information 83a, 83b, 83c, 83d may include the identification number and the comment input from the input unit 13 in place of or in addition to the length of the step vector.

Further, the display processing unit 27 adds a three-dimensional step to the three-dimensional point cloud representing the inner wall surface 5a of the tunnel 5 based on the three-dimensional point cloud data Dpg stored in the storage unit 11 and the three-dimensional step vector data. The three-dimensional image on which the vector is superimposed can be displayed on the display unit 14. Further, the display processing unit 27 adds a three-dimensional image of the tunnel 5 and three to the three-dimensional point cloud representing the inner wall surface 5a of the tunnel 5 based on the three-dimensional point cloud data Dpg, the three-dimensional step vector data, and the image data Dimg. It is also possible to display the three-dimensional image on which the dimensional step vector is superimposed on the display unit 14.

FIG. 14 is a diagram showing an example of a three-dimensional image in which a three-dimensional step vector is superimposed on a three-dimensional point cloud representing the inner wall surface of the tunnel according to the first embodiment. The three-dimensional image 90 shown in FIG. 14 includes a three-dimensional point cloud 91 representing the inner wall surface 5a of the tunnel 5 and step vectors 92a, 92b, 92c, 92d. The step vectors 92a, 92b, 92c, and 92d are three-dimensional figures showing the step vectors. The three-dimensional image 90 can also include the attribute information of the step vector.

Further, the display processing unit 27 can display the step information detected by the step detection unit 23 on the display unit 14 in chronological order based on the time information. For example, the display processing unit 27 can display the step vector detected at each time on the display unit 14 while switching the step vector detected at each time in the order of the detection time, based on the time information included in the step data. As a result, the user of the deformation detection system 100 can grasp the state change of the step. For example, the display processing unit 27 can display the step vectors 82a, 82b, 82c, 82d detected at each time on the display unit 14 while switching in the order of the detection time. Further, the display processing unit 27 can display the step vectors 92a, 92b, 92c, 92d detected at each time on the display unit 14 while switching in the order of the detection time.

FIG. 15 is a flowchart showing an example of a processing procedure by the processing unit of the step detection device according to the first embodiment. The process shown in FIG. 15 is repeatedly executed by the processing unit 12. As shown in FIG. 15, the processing unit 12 of the step detection device 2 determines whether or not the step detection timing has been reached (step S10). For example, the processing unit 12 determines that the step detection timing has come when the three-dimensional point cloud data Dpg is acquired from the traveling type measuring device 1 or when there is a specific operation on the input unit 13.

When the processing unit 12 determines that the step detection timing has come (step S10: Yes), the processing unit 12 performs the step detection process (step S11). The step detection process in step S11 is the process of steps S20 to S23 shown in FIG. 16, which will be described in detail later. Next, the processing unit 12 performs coordinate conversion processing for converting the step vector of the step detected in step S11 into the step vector of the three-dimensional Cartesian coordinate system (step S12). Further, the processing unit 12 performs a step evaluation process for determining the state level of the step by using an arithmetic expression or a table in which the height of the step and the spread of the step detected in step S11 are parameters (step S13).

When the processing of step S13 is completed, or when it is determined that the step detection timing has not been reached (step S10: No), the processing unit 12 determines whether or not there is an attribute input (step S14). In step S14, the processing unit 12 determines that there is an attribute input when the attribute information of the step is input to the input unit 13. When the processing unit 12 determines that there is an attribute input (step S14: Yes), the processing unit 12 stores the input attribute information in the storage unit 11 (step S15).

When the processing of step S15 is completed or when it is determined that there is no attribute input (step S14: No), the processing unit 12 determines whether or not there is a display request (step S16). In step S16, the processing unit 12 determines that there is a display request, for example, when there is a specific operation on the input unit 13. When the processing unit 12 determines that there is a display request (step S16: Yes), the processing unit 12 causes the display unit 14 to display the target of the display request (step S17). In step S17, when the display request is a display request for the modified development diagram, the processing unit 12 causes the display unit 14 to display a two-dimensional image showing the modified development diagram. Further, when the display request is a time-series display request, the processing unit 12 causes the display unit 14 to display the step information detected by the step detection unit 23 in time series based on the time information.

When the process of step S17 is completed or when it is determined that there is no display request (step S16: No), the processing unit 12 ends the process shown in FIG.

FIG. 16 is a flowchart showing an example of the step detection process by the processing unit of the step detection device according to the first embodiment. As shown in FIG. 16, the processing unit 12 of the step detection device 2 converts the three-dimensional point cloud data Dpg into the element point cloud data De (step S20). Next, the processing unit 12 performs Gaussian difference processing that applies the element point cloud data De to the Gaussian difference filter (step S21). The processing unit 12 detects a step based on the output of the Gaussian difference filter (step S22). Then, the processing unit 12 stores the step data indicating the detected step information in the storage unit 11 (step S23), and ends the process shown in FIG.

FIG. 17 is a diagram showing an example of the hardware configuration of the step detection device according to the first embodiment. As shown in FIG. 17, the step detection device 2 includes a computer including a processor 101, a memory 102, a communication device 103, an input / output circuit 104, and a display device 105.

The processor 101, the memory 102, the communication device 103, the input / output circuit 104, and the display device 105 can transmit and receive data to and from each other by, for example, the bus 106. The communication unit 10 is realized by the communication device 103. The storage unit 11 is realized by the memory 102. The input unit 13 is realized by the input / output circuit 104. The display unit 14 is realized by the display device 105. By reading and executing the program stored in the memory 102, the processor 101 reads and executes the acquisition unit 21, the data conversion unit 22, the step detection unit 23, the step evaluation unit 24, the attribute setting unit 25, the coordinate conversion unit 26, and the display. The function of the processing unit 27 is executed. The processor 101 is, for example, an example of a processing circuit, and includes one or more of a CPU (Central Processing Unit), a DSP (Digital Signal Processer), and a system LSI (Large Scale Integration).

The memory 102 includes one or more of RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EEPROM (Erasable Programmable Read Only Memory), and EEPROM (registered trademark) (Electrically Erasable Programmable Read Only Memory). include. The memory 102 also includes a recording medium on which a computer-readable program is recorded. Such recording media include one or more of non-volatile or volatile semiconductor memories, magnetic disks, flexible memories, optical disks, compact discs, and DVDs (Digital Versatile Discs). The step detection device 2 may include integrated circuits such as an ASIC (Application Specific Integrated Circuit) and an FPGA (Field Programmable Gate Array).

In the above example, the surface of the structure in which the step is detected by the step detection unit 23 of the step detection device 2 is the inner wall surface 5a of the tunnel 5, but the step detection unit 23 of the step detection device 2 is a tunnel. It is possible to detect a step generated in other than the inner wall surface 5a of 5. For example, the step detection unit 23 can detect a step by targeting the surface of a bridge, the surface of a road, and the surface of a building as detection targets. Further, when the surface of the structure represented by the three-dimensional point cloud is a plane, the step detection unit 23 converts the three-dimensional point cloud data Dpg into the element point cloud data De without converting it into the developed view coordinate system. can do.

As described above, the step detection device 2 of the deformation detection system 100 according to the first embodiment includes an acquisition unit 21, a data conversion unit 22, and a step detection unit 23. The acquisition unit 21 acquires the three-dimensional point cloud data Dpg, which is data representing the inner wall surface 5a of the tunnel 5 as a three-dimensional point cloud, and the shape data Df indicating the shape of the tunnel 5. The data conversion unit 22 sets a reference plane Swr corresponding to the inner wall surface 5a of the tunnel 5 based on the shape data Df, and arranges the three-dimensional point cloud data Dpg in a matrix on the reference plane Swr. It is converted into element point cloud data De including data of a plurality of element points Pe represented by the position of one point Pg selected from and the amount of deviation from the reference plane Swr. The step detection unit 23 detects the position of the step on the inner wall surface 5a of the tunnel 5 based on the result obtained by applying the Gaussian difference filter to the plurality of element points Pe included in the element point cloud data De. The tunnel 5 is an example of a structure, and the inner wall surface 5a of the tunnel 5 is an example of the surface of the structure. Thereby, for example, even when the difference in density of the three-dimensional points due to the step is small in the three-dimensional point cloud representing the inner wall surface 5a of the tunnel 5, the position of the step generated on the inner wall surface 5a of the tunnel 5 can be detected. can.

Further, the step detection unit 23 is formed on a line connecting two element points Pe that are adjacent to each other and have different polarities in the values obtained by applying the Gaussian difference filter among the plurality of element points Pe after the Gaussian difference filter is applied. The position where the zero intersection occurs is specified, and the specified position is detected as the position of the step. As a result, the position of the step generated on the inner wall surface 5a of the tunnel 5 can be accurately detected from the result obtained from the Gaussian difference filter.

Further, the step detection unit 23 is a Gaussian difference between three or more adjacent element points Pe among the plurality of element points Pe after the Gaussian difference filter is applied, and at least one element point Pe and the remaining element points Pe. A step height detecting unit 32 that detects the height of a step based on the inclination of a surface including three or more element points Pe having different polarities obtained by filtering is provided. As a result, the height of the step can be detected from the result obtained from the Gaussian difference filter.

Further, the step detection unit 23 detects the step height based on the inclination at the zero intersection of the line connecting the two adjacent element points Pe from the result obtained by applying the Gaussian difference filter. A unit 32 is provided. As a result, the height of the step can be accurately detected from the result obtained from the Gaussian difference filter.

Further, the step detection unit 23 includes a vector generation unit 33 that generates a step vector, which is a vector obtained by connecting the positions of the steps. As a result, it is possible to generate a step vector representing a continuous step.

Further, the step detection device 2 calculates the spread of the step based on the step vector generated by the vector generation unit 33, and based on the calculated spread of the step and the height of the step detected by the step height detection unit 32. The step evaluation unit 24 for determining the state level representing the state of the step is provided. As a result, the state of the step can be easily grasped.

Further, the step detection device 2 includes a display processing unit 27 that displays the step vector generated by the vector generation unit 33 on the display unit 14. As a result, the state of the step can be easily grasped.

Further, the display processing unit 27 causes the display unit 14 to display an image in which the step vectors 82a, 82b, 82c, 82d are superimposed on at least one of the developed view of the tunnel 5 and the captured image. Thereby, for example, the position on the inner wall surface 5a of the step and the spread of the step can be easily grasped.

Further, the step detection device 2 includes a coordinate conversion unit 26 that converts the step vectors 82a, 82b, 82c, 82d into data in a three-dimensional coordinate system. The display processing unit 27 causes the display unit 14 to display the step vectors 92a, 92b, 92c, 92d converted into the data of the three-dimensional coordinate system. As a result, the state of the step can be grasped in three dimensions.

Further, the display processing unit 27 displays an image in which the step vectors 92a, 92b, 92c, 92d converted into the data of the three-dimensional coordinate system are superimposed on the image of the three-dimensional point cloud representing the inner wall surface 5a of the tunnel 5. To display. Thereby, the position of the step and the spread of the step on the inner wall surface 5a of the tunnel 5 can be easily grasped from the images corresponding to the step vectors 92a, 92b, 92c, 92d.

Further, the acquisition unit 21 acquires a plurality of three-dimensional point cloud data Dpg obtained by measuring the inner wall surface 5a of the tunnel 5 at different times. The plurality of three-dimensional point cloud data Dpgs include time information indicating the time when the inner wall surface 5a of the tunnel 5 is measured. The step detection unit 23 detects a step from each of the plurality of three-dimensional point cloud data Dpgs, and associates time information with the detected step. The display processing unit 27 causes the display unit 14 to display the step information detected by the step detection unit 23 in chronological order based on the time information. As a result, the state of the step can be grasped in chronological order.

Embodiment 2.
The deformation detection system according to the second embodiment is different from the deformation detection system 100 according to the first embodiment in that the step detection device detects the secular change of the step and calculates the distribution of the step. In the following, the components having the same functions as those of the first embodiment will be designated by the same reference numerals and the description thereof will be omitted, and the differences from the deformation detection system 100 of the first embodiment will be mainly described.

FIG. 18 is a diagram showing a configuration example of a step detection device in the deformation detection system according to the second embodiment of the present invention. As shown in FIG. 18, the step detection device 2A of the deformation detection system according to the second embodiment is different from the step detection device 2 in that the processing unit 12A is provided instead of the processing unit 12. The processing unit 12A is different from the processing unit 12 in that the comparison unit 28 and the statistical processing unit 29 are further provided.

The comparison unit 28 compares a plurality of step data detected by the step detection unit 23 from a plurality of three-dimensional point cloud data Dpg obtained by each measurement at different times in the traveling type measuring device 1. For example, the comparison unit 28 compares the heights of the steps detected by the step detection unit 23 from the plurality of three-dimensional point cloud data Dpg obtained by the traveling type measuring device 1 at different times, and compares the heights of the steps. Judge the secular change of the height of. Further, the comparison unit 28 compares the spreads of the steps detected by the step detection unit 23 from the plurality of three-dimensional point cloud data Dpg obtained by measuring at different times, and determines the secular change of the spread of the steps. do. The display processing unit 27 can display the information indicating the secular change in the height of the step and the secular change in the spread of the step determined by the comparison unit 28 on the display unit 14.

The comparison unit 28 compares, for example, the height of the step detected using the previously measured 3D point cloud data Dpg with the height of the step detected using the 3D point cloud data Dpg measured this time. Then, the step whose height has changed is determined. Further, in the comparison unit 28, for example, the spread of the step detected using the previously measured three-dimensional point cloud data Dpg and the spread of the step detected using the three-dimensional point cloud data Dpg measured this time. To determine the level difference in which the spread has changed. The display processing unit 27 can highlight and display the step determined by the comparison unit 28 that the height has changed and the step determined by the comparison unit 28 that the spread has changed.

In the comparison unit 28, for example, the difference between the height of the step detected using the previously measured 3D point cloud data Dpg and the height of the step detected using the 3D point cloud data Dpg measured this time. Can be calculated. Further, in the comparison unit 28, for example, the spread of the step detected using the previously measured three-dimensional point cloud data Dpg and the spread of the step detected using the three-dimensional point cloud data Dpg measured this time. The difference between can be calculated. The display processing unit 27 can display the information indicating the difference in the height of the steps calculated by the comparison unit 28 and the information indicating the difference in the spread of the steps on the display unit 14. The display processing unit 27 highlights and displays the difference in the height of the step determined by the comparison unit 28 to change the height and the difference in the spread of the step determined by the comparison unit 28 to change the spread. It can also be displayed on the unit 14.

The statistical processing unit 29 performs statistical processing for calculating at least one of the number and length of the step vectors as step vector information for each region set on the inner wall surface 5a of the tunnel 5. For example, the statistical processing unit 29 sets the number of step vectors included in the tunnel span, the total length of the step vectors included in the tunnel span, and the step vector included in the tunnel span for each tunnel span which is the span of the tunnel 5. Calculate the height of the step and the spread of the step vector included in the tunnel span. The height of the step in the step vector included in the tunnel span is, for example, the average value and the maximum value of the heights of the plurality of steps used for generating the step vector when the step vector is generated from the steps of a plurality of heights. , And at least one of the minimum values.

In addition, the statistical processing unit 29 can calculate the distribution of the step vector for each tunnel span. The distribution of the step vector means, for example, the ratio of the step vectors existing in each of the divided regions when it is assumed that the tunnel span is divided into a plurality of regions in the tunnel circumferential direction.

The display processing unit 27 can display the result calculated by the statistical processing unit 29 on the display unit 14. FIG. 19 is a diagram showing an example of the result calculated by the statistical processing unit according to the second embodiment. As shown in FIG. 19, the statistical processing unit 29 can display a graph showing the number of step vectors of each tunnel span calculated by the statistical processing unit 29 on the display unit 14. In the graph shown in FIG. 19, the number of step vectors generated in 2017, the number of step vectors generated in 2018, and the number of step vectors generated in 2019 are shown for each tunnel span.

Further, the statistical processing unit 29 displays a graph showing the total length of the step vectors of each tunnel span, a graph showing the distribution of the step vectors of each tunnel span, etc., instead of the number of step vectors of each tunnel span. It can be displayed on the unit 14.

FIG. 20 is a flowchart showing an example of a processing procedure by the processing unit of the step detection device according to the second embodiment. Since the processes of steps S30 to S37 of FIG. 20 are the same as the processes of steps S10 to S17 shown in FIG. 15, the description thereof will be omitted. The process shown in FIG. 20 is repeatedly executed by the processing unit 12A.

As shown in FIG. 20, the processing unit 12A of the step detection device 2A determines whether or not the comparison processing timing has been reached (step S38). For example, the processing unit 12A determines that the comparison processing timing has come when there is a specific operation on the input unit 13. When the processing unit 12A determines that the comparison processing timing has come (step S38: Yes), a plurality of three-dimensional point cloud data Dpg detected by the step detection unit 23 from the plurality of three-dimensional point cloud data Dpg obtained by measuring at different times. A comparison process for comparing the step data is performed, and a display process for displaying the result of the comparison process on the display unit 14 is performed (step S39).

When the processing of step S39 is completed or when it is determined that the comparison processing timing has not been reached (step S38: No), the processing unit 12A determines whether or not the statistical processing timing has been reached (step S40). For example, the processing unit 12A determines that the statistical processing timing has come when there is a specific operation on the input unit 13. When the processing unit 12A determines that the statistical processing timing has come (step S40: Yes), at least one of the number and length of the step vectors is used as the step vector information for each set area of the inner wall surface 5a of the tunnel 5. The calculated statistical processing is performed, and the display processing for displaying the result of the statistical processing on the display unit 14 is performed (step S41). The processing unit 12A ends the processing shown in FIG. 20 when the processing in step S41 is completed or when it is determined that the statistical processing timing has not been reached (step S40: No).

The hardware configuration example of the step detection device 2A according to the second embodiment is the same as the hardware configuration of the step detection device 2 shown in FIG. The processor 101 can execute the functions of the comparison unit 28 and the statistical processing unit 29 by reading and executing the program stored in the memory 102.

As described above, the step detection device 2A of the deformation detection system 100A according to the second embodiment includes a statistical processing unit 29. The statistical processing unit 29 calculates at least one of the number and length of the step vectors 82a, 82b, 82c, 82d as the step vector information for each tunnel span of the inner wall surface 5a of the tunnel 5. The tunnel span is an example of a predetermined area. The display processing unit 27 graphs the step vector information calculated by the statistical processing unit 29 and displays it on the display unit 14. As a result, the state of the step can be grasped for each tunnel span.

Further, the step detection device 2A includes a comparison unit 28 that compares the data of the plurality of steps detected by the step detection unit 23 from the plurality of three-dimensional point cloud data Dpg. Thereby, for example, it is possible to grasp the secular change of the step.

Embodiment 3.
The deformation detection system according to the third embodiment is different from the deformation detection system 100 according to the first embodiment in that a deformation server, a data server, and the like are used. In the following, the same components as those of the deformation detection system 100 according to the first embodiment are designated by the same reference numerals and the description thereof will be omitted, and the configurations different from the deformation detection system 100 according to the first embodiment will be mainly described. It shall be.

FIG. 21 is a diagram showing a first configuration example of the deformation detection system according to the third embodiment of the present invention. The deformation detection system 100B shown in FIG. 21 includes a traveling type measuring device 1, a step detection device 2B, and a deformation server 4. The transformation server 4 includes a communication unit 40, a storage unit 41, and a processing unit 42. The processing unit 42 acquires the three-dimensional point cloud data Dpg and the image data Dimg from the traveling type measuring device 1 via the network 3 and the communication unit 40, and stores the acquired three-dimensional point cloud data Dpg and the image data Dimg in the storage unit 41. To memorize. The storage unit 41 stores the shape data Df and the deformation data Ddf in addition to the three-dimensional point cloud data Dpg and the image data Dimg acquired by the acquisition unit 21B.

The step detection device 2B includes a processing unit 12B having an acquisition unit 21B instead of the processing unit 12 having the acquisition unit 21. The acquisition unit 21B is different from the acquisition unit 21 in that the three-dimensional point cloud data Dpg, the image data Dimg, the shape data Df, and the deformation data Ddf are acquired from the deformation server 4 via the network 3 and the communication unit 10. different. Further, the acquisition unit 21B transmits the level difference data generated by the level difference detection unit 23 and the level difference evaluation unit 24 from the communication unit 10 to the transformation server 4 via the network 3. The deformation server 4 adds the step data acquired from the step detection device 2B to the deformation data Ddf stored in the storage unit 41, and updates the deformation data Ddf.

As described above, in the deformation detection system 100B shown in FIG. 21, the three-dimensional point cloud data Dpg and the like having a large amount of data are stored in the deformation server 4. As a result, the storage capacity of the storage unit 11 of the step detection device 2B can be reduced as compared with the storage capacity of the storage unit 11 of the step detection device 2 according to the first embodiment.

FIG. 22 is a diagram showing a second configuration example of the deformation detection system according to the third embodiment. The deformation detection system 100C shown in FIG. 22 includes a traveling type measuring device 1, a step detection device 2C, and a deformation server 4C. The transformation server 4C includes a communication unit 40, a storage unit 41, and a processing unit 42C. The processing unit 42C includes a data conversion unit 51, and in addition to the functions of the processing unit 42 described above, the processing unit 42C has a function of converting the three-dimensional point cloud data Dpg into the element point cloud data De by the data conversion unit 51. The function of the data conversion unit 51 is the same as the function of the data conversion unit 22 shown in FIG.

The step detection device 2C includes a processing unit 12C having an acquisition unit 21C instead of the processing unit 12B having the acquisition unit 21B. The acquisition unit 21C differs from the acquisition unit 21B in that the element point cloud data De is acquired from the transformation server 4C via the network 3 and the communication unit 10 instead of the three-dimensional point cloud data Dpg and the shape data Df. .. The step detection device 2C transmits the generated step data to the deformation server 4C in the same manner as the step detection device 2B.

As described above, in the deformation detection system 100C shown in FIG. 22, the three-dimensional point cloud data Dpg having a large amount of data is stored in the deformation server 4C, and the three-dimensional point cloud data Dpg is transformed into the element point cloud data De. Converted by server 4C. As a result, the storage capacity of the storage unit 11 of the step detection device 2C can be reduced. Further, since the element point cloud data De has a smaller data capacity than the three-dimensional point cloud data Dpg, the deformation detection system 100C has a step detection device 2C and a deformation server 4C as compared with the deformation detection system 100B. The amount of communication between the two can be reduced.

FIG. 23 is a diagram showing a third configuration example of the deformation detection system according to the third embodiment. The deformation detection system 100D shown in FIG. 23 includes a traveling type measuring device 1, a deformation server 4D, and a terminal device 6. The deformation server 4D executes functions other than the display function and the input reception function among the functions of the step detection device 2. The transformation server 4D has a processing unit 42D including a data conversion unit 51, a step detection unit 52, a step evaluation unit 53, an attribute setting unit 54, and a coordinate conversion unit 55.

The data conversion unit 51 has the same function as the data conversion unit 22, and uses the three-dimensional point cloud data Dpg as an element point based on the shape data Df and the three-dimensional point cloud data Dpg stored in the storage unit 41. Convert to group data De. Similar to the step detection unit 23, the step detection unit 52 detects the step from the element point cloud data De and generates step data including information on the detected step. The step detection unit 52 adds the generated step data to the deformation data Ddf.

Similar to the step evaluation unit 24, the step evaluation unit 53 determines the state level of the step and adds information indicating the determined step level to the step data. Based on the request from the terminal device 6, the attribute setting unit 54 adds information indicating the step attribute to the step data, similarly to the attribute setting unit 25. Similar to the coordinate conversion unit 26, the coordinate conversion unit 55 converts the two-dimensional coordinate data stored in the storage unit 41 into three-dimensional coordinate data, and converts the three-dimensional coordinate data stored in the storage unit 41 into two. Convert to dimensional coordinate data. The coordinate conversion unit 55 stores the converted data in the storage unit 41.

The terminal device 6 includes a communication unit 60, a processing unit 61, an input unit 62, and a display unit 63. The processing unit 61 includes a request processing unit 70, a display processing unit 71, and an output processing unit 72. When there is a specific operation to the input unit 62, the request processing unit 70 requests the data corresponding to the specific operation to the transformation server 4D via the communication unit 60. The request processing unit 70 acquires data according to a request from the transformation server 4D via the communication unit 60.

The display processing unit 71 causes the display unit 63 to display the data acquired by the request processing unit 70. The data displayed by the display processing unit 71 is the same as the data displayed by the display processing unit 27. As a result, the terminal device 6 can display the same data as the step detection device 2 on the display unit 63. Further, the output processing unit 72 transmits the attribute information of the step input by the input unit 62 from the communication unit 60 to the transformation server 4D. As a result, the deformation server 4D adds the step attribute information to the step data.

As described above, in the deformation detection system 100D, since the main processing is performed by the deformation server 4D, the processing load of the terminal device 6 can be reduced.

FIG. 24 is a diagram showing a fourth configuration example of the deformation detection system according to the third embodiment. The deformation detection system 100E shown in FIG. 24 includes a traveling type measuring device 1, a deformation server 4E, a terminal device 6, and a data server 7. The deformation detection system 100E differs from the deformation detection system 100D in that the shape data Df, the three-dimensional point cloud data Dpg, the image data Dimg, and the deformation data Ddf are stored in the data server 7 instead of the deformation server 4E. ..

The transformation server 4E transmits / receives shape data Df, three-dimensional point cloud data Dpg, image data Dimg, and transformation data Ddf to / from the data server 7 via the network 3. As described above, the deformation detection system 100E is used as a server for processing by the deformation server 4E, and the data server 7 is used as a data server for storing data used in the deformation server 4E or the terminal device 6.

As described above, in the deformation detection systems 100B, 100C, 100D, and 100E according to the third embodiment, some or all of the plurality of functions of the step detection device 2 according to the first embodiment are distributed to the plurality of devices. I'm letting you. As a result, the degree of freedom in system design can be increased. Similarly, the functions of the comparison unit 28 and the statistical processing unit 29 included in the step detection device 2A according to the second embodiment can be provided in the transformation servers 4, 4C, 4D, and 4E.

Each of the hardware configuration examples of the step detection devices 2B and 2C and the terminal device 6 according to the third embodiment is the same as the hardware configuration of the step detection device 2 shown in FIG. The processor 101 can execute each function of the step detection devices 2B and 2C and the terminal device 6 by reading and executing the program stored in the memory 102.

Further, in the hardware configuration examples of the transformation servers 4, 4C, 4D, 4E and the data server 7 according to the third embodiment, the display device 105 is excluded from the hardware configuration of the step detection device 2 shown in FIG. It is a configuration. The processor 101 can execute each function of the transformation server 4, 4C, 4D, 4E and the data server 7 by reading and executing the program stored in the memory 102.

The configuration shown in the above-described embodiment shows an example of the content of the present invention, can be combined with another known technique, and is one of the configurations without departing from the gist of the present invention. It is also possible to omit or change the part.

1 Travel type measuring device, 2,2A, 2B, 2C step detection device, 3 network, 4,4C, 4D, 4E deformation server, 5 tunnel, 5a inner wall surface, 6 terminal device, 7 data server, 10,40, 60 Communication unit, 11,41 Storage unit, 12,12A, 12B, 12C, 42, 42C, 42D, 61 Processing unit, 13,62 Input unit, 14,63 Display unit, 21,21B, 21C Acquisition unit, 22, 51 Data conversion unit, 23,52 Step detection unit, 24,53 Step evaluation unit, 25,54 Attribute setting unit, 26,55 Coordinate conversion unit, 27,71 Display processing unit, 28 Comparison unit, 29 Statistical processing unit, 31 Step position detection unit, 32 step height detection unit, 33 vector generation unit, 70 request processing unit, 72 output processing unit, 80 deformation development drawing, 81 outline frame, 82a, 82b, 82c, 82d, 92a, 92b, 92c, 92d step vector, 83a, 83b, 83c, 83d attribute information, 90 3D image, 91 3D point cloud, 100, 100A, 100B, 100C, 100D, 100E Deformity detection system.

Claims (14)

An acquisition unit that acquires three-dimensional point cloud data, which is data representing the surface of a structure as a three-dimensional point cloud, and shape data indicating the shape of the structure.
A reference plane corresponding to the surface of the structure is set based on the shape data, and the position of one point selected from a plurality of points arranged in a matrix on the reference plane for the three-dimensional point cloud data. And a data conversion unit that converts data into element point cloud data including data of a plurality of element points represented by the amount of deviation from the reference plane.
It is provided with a step detection unit that detects the position of a step on the surface of the structure based on the result obtained by applying a Gaussian difference filter to the plurality of element points included in the element point cloud data. Characteristic deformation detection system. The step detection unit
Specify the position where zero intersection occurs in the line connecting two element points that are adjacent to each other and have different polarities in the values obtained by applying the Gaussian difference filter among the plurality of element points after the Gaussian difference filter is applied. The deformation detection system according to claim 1, wherein the specified position is detected as the position of the step. The step detection unit
Polarity of a value obtained by applying the Gaussian difference filter to at least one element point and the remaining element points of three or more adjacent element points among the plurality of element points after the Gaussian difference filter is applied. The deformation detection system according to claim 1 or 2, further comprising a step height detection unit that detects the height of the step based on the inclination of a surface including three or more element points having different values. The step detection unit
The deformation detection system according to claim 2, further comprising a step height detecting unit that detects the height of the step based on the inclination of a line connecting the two element points. The step detection unit
The deformation detection system according to any one of claims 1 to 4, further comprising a vector generation unit that generates a step vector, which is a vector obtained by connecting the positions of the steps. The deformation detection system according to claim 5, further comprising a display processing unit that displays the step vector generated by the vector generation unit on the display unit. The display processing unit
The deformation detection system according to claim 6, wherein an image in which the step vector is superimposed on at least one of the developed view of the structure and the captured image is displayed on the display unit. A coordinate conversion unit that converts the step vector into data in a three-dimensional coordinate system is provided.
The display processing unit
The deformation detection system according to claim 6 or 7, wherein the step vector converted into data of the three-dimensional coordinate system is displayed on the display unit. The display processing unit
The eighth aspect of claim 8, wherein the display unit displays an image in which the step vector converted into the data of the three-dimensional coordinate system is superimposed on the image of the three-dimensional point cloud representing the surface of the structure. Deformity detection system. A statistical processing unit that calculates at least one of the number and length of the step vector as step vector information for each predetermined region on the surface of the structure is provided.
The display processing unit
The deformation detection system according to claim 9, wherein the step vector information calculated by the statistical processing unit is graphed and displayed on the display unit. The acquisition unit
A plurality of three-dimensional point cloud data obtained by measuring the surface of the structure at different times are acquired.
The plurality of three-dimensional point cloud data are
Includes time information indicating the time when the surface of the structure was measured.
The step detection unit
The step is detected from each of the plurality of three-dimensional point cloud data, and the time information is associated with the detected step.
The display processing unit
The deformation detection system according to any one of claims 6 to 10, wherein the information on the step detected by the step detection unit is displayed on the display unit in chronological order based on the time information. .. The deformation detection system according to claim 11, further comprising a comparison unit that compares the data of the plurality of steps detected by the step detection unit from the plurality of three-dimensional point cloud data. One of claims 1 to 12, wherein a step evaluation unit for determining a state level representing the state of the step based on the state of the step detected by the step detection unit is provided. Deformation detection system described. It ’s a computer-executed error detection method.
An acquisition step for acquiring 3D point cloud data, which is data representing the surface of a structure in a 3D point cloud, and shape data indicating the shape of the structure, and
A reference plane corresponding to the surface of the structure is set based on the shape data, and the position of one point selected from a plurality of points arranged in a matrix on the reference plane for the three-dimensional point cloud data. And a data conversion step of converting to element point cloud data including data of a plurality of element points represented by the amount of deviation from the reference plane.
It includes a step detection step of detecting the position of a step on the surface of the structure based on the result obtained by applying a Gaussian difference filter to the plurality of element points included in the element point cloud data. A characteristic deformation detection method.

Images