JP2019020352A - Method and device for detecting inclination of iron tower - Google Patents

Abstract

To provide a method and a device for detecting the inclination of an iron tower, the method and the device allowing a quantitative and direct measurement of the inclination of the iron tower and suppression of processing load of acquired data.SOLUTION: The method for detecting the inclination of an iron tower according to the present invention typically includes the steps of: acquiring point group data by scanning an existing iron tower 140 by a ground-type laser device 142; extracting the respective feature lines L1 to L4 of a plurality of main column materials of the iron tower 140 from the point group data; calculating the center line C1 of the iron tower 140 from the feature lines L1 to L4; and calculating the inclination of the iron tower 140 from the center line C1.SELECTED DRAWING: Figure 2

Description

  The present invention relates to an inclination detection method and an inclination detection apparatus for a steel tower for calculating the inclination of an existing steel tower.

  Many of the power transmission towers currently used in Japan (hereinafter simply referred to as steel towers) have been several decades since construction. Regardless of how robust the steel tower is, it is subject to climatic phenomena after construction and is subject to changes in the ground that accompany it. For this reason, there is an increasing need to check the soundness of existing steel towers.

  When checking the soundness of a steel tower, it is important to examine its inclination. Steel towers that have passed through construction can change in shape and attitude (displacement). However, it is not easy to directly measure the inclination of a steel tower having a height of 50 m or more. Therefore, the measurement of the distance between the legs is mainly performed as the measurement of the displacement of the steel tower.

  In many cases, the conventional displacement measurement of steel towers has been performed by the operator manually measuring the horizontal distance between the legs using a tape measure. In some cases, a laser measuring instrument or the like is used instead of a tape measure to prevent individual differences due to manual work. In addition, as an example of the inhomogeneous displacement measurement of a steel tower, according to Patent Document 1, for example, a measuring device using a wire has been proposed. If these methods reveal that the discontinuous displacement of the steel tower is increasing, measures such as the addition of reinforcing members are taken. In addition, when adding the reinforcing member, a process called a member sketch for actually measuring the dimensions of the steel tower member is also performed.

  The above-described conventional methods for measuring the displacement of the steel tower are performed by measuring only a part of the whole steel tower, such as the distance between legs. However, originally, it is desirable to directly measure the inclination of the entire tower. Therefore, in recent years, a method for directly measuring the shape of the entire tower using a laser scanner (LiDAR: Light Detection And Ranging) has been studied as a measurement of the displacement of the tower.

  A laser scanner is a device that measures the three-dimensional coordinates of an object in mm using the time difference or phase difference between emitted light and reflected light. In particular, in recent years, the performance of phase difference type terrestrial laser scanners has dramatically improved. The terrestrial laser scanner irradiates a laser beam from a main body supported by a tripod or the like, and at the same time, the main body rotates to acquire three-dimensional coordinates of surrounding terrain and structures as point clouds. The latest terrestrial laser scanners are capable of measuring distances of 300 m or more, and not only can acquire a wide range of three-dimensional coordinates with high accuracy and high density in a short measurement time, but also the price is lower than before. It's getting on. By using such a terrestrial laser scanner, it is considered that the shape of the steel tower can be efficiently and quantitatively measured in the above-described process of measuring the non-uniform displacement and sketching the members.

JP 2017-90397 A

  However, the terrestrial laser scanner acquires tertiary coordinates including not only the steel tower but also the surrounding topography and structures as a large amount of point cloud data in which tens of millions to hundreds of millions of coordinates are gathered. In order to obtain the tower inclination and member dimensions from such point cloud data, for example, it is conceivable to use processing software dedicated to point cloud analysis such as CAD of point cloud data. However, such a process is not easy to adopt from the viewpoint of cost and labor, because even a skilled person operates it, it is predicted that several dozens of work will take several days.

  In addition, since the laser beam from the terrestrial laser scanner is diffused in proportion to the distance, it is necessary to perform measurement with a high point group density in order to accurately examine a distant object. If the object is a plant or the like, it is possible to measure from multiple points on the floor and synthesize the measurement results later, so that a high point cloud density is not required for a single measurement. However, when measuring a tower that is 50 m or higher from the ground alone, it is necessary to perform a single measurement with as high a point cloud density as possible in order to sufficiently grasp the shape of the high place. Therefore, when the object is a steel tower, the number of point clouds acquired by one measurement is enormous, and the processing load of the acquired data is very large compared to other objects.

  In view of such problems, the present invention provides a tower inclination detection method and an inclination detection apparatus capable of directly and quantitatively measuring the inclination of a steel tower and further suppressing the processing load of acquired data. It is an object.

  In order to solve the above-described problem, a typical configuration of the tilt detection method of a tower according to the present invention is to scan an existing tower with a ground-type laser scanner to acquire point cloud data, and the tower is provided with the point cloud data. For each of a plurality of main pillar materials, a feature line that is a line segment representing the main pillar material is extracted, the center line of the steel tower is calculated from the plurality of feature lines, and the inclination of the steel tower is calculated from the center line. .

  According to the said structure, it becomes possible to measure the inclination of a steel tower directly and quantitatively, without resorting to an operator's technique and experience. In particular, it is possible to efficiently process a large amount of point cloud data and perform simple measurement with reduced costs and labor.

  The point cloud data may be acquired by scanning from the center of a plurality of leg portions of the steel tower on the ground. The main pillar material of the steel tower extends from the legs (mainly four) of the steel tower. Therefore, it is possible to efficiently acquire the three-dimensional coordinates of each main pillar by scanning from the center of the leg of the steel tower.

  The steel tower inclination detection method divides point cloud data into a predetermined grid in the horizontal direction, and in each divided area, the height difference with respect to the coordinates regarded as the ground surface is a predetermined value or more. Extract the area containing the point as the tower installation area, remove the measurement points near the ground surface from the point cloud data of the tower installation area, and use the point cloud data indicating the tower as point cloud data. A feature line may be extracted.

  The point cloud data acquired by the terrestrial laser scanner includes coordinates related to topography and structures other than the steel tower. Therefore, in the above configuration, the installation area of the steel tower is extracted and only the point cloud data of the steel tower is processed. For example, only the installation points where the measurement points where the height difference is greater than or equal to a predetermined value, that is, the measurement points of the steel tower are included, are extracted. Furthermore, only the point cloud data indicating the steel tower is extracted by removing the data near the ground surface. As a result, the processing load can be reduced.

  When the main pillar material is an angle material, a valley line of the angle material may be extracted as a feature line. With this configuration, the characteristic lines of the main pillar material can be acquired efficiently.

  When the main pillar material is an angle material, a plurality of planes are detected from the point cloud data, adjacent planes among the plurality of planes are extracted, an intersection line between the planes is calculated, and a vertical line among the intersection lines is calculated. The line segments close to are extracted, and the line segments close to vertical are grouped for each area where the main column material exists, and the line segments that are on the same straight line in each group are divided as a set of line segments. The adjacent line segments may be connected to each other, and the line segment set having the longest total length of the element line segments may be the valley line of the angle member and the characteristic line of the main column member. Also with this configuration, it is possible to efficiently acquire the characteristic lines of the main pillar material.

  When the above main pillar is a steel pipe, the center line of the steel pipe is detected from the point cloud data in a predetermined range, the line segment near the vertical is extracted from the center line, and the main pillar material exists in the line near the vertical. The line segments that are on the same straight line in each group are divided into line segment sets, the line segments that are close to each other in each line segment set are connected, and the total length of the line segments that are elements The longest line segment set may be used as the characteristic line of the main pillar material. Also with this configuration, it is possible to efficiently acquire the characteristic lines of the main pillar material.

  In order to solve the above-described problems, a typical configuration of the tower inclination detection device according to the present invention is the main pillar for each of a plurality of main pillar materials provided in the tower from the point cloud data of the tower acquired by the ground-type laser scanner. A feature line extraction unit that extracts a feature line that is a line segment representing a material, a center line calculation unit that calculates a center line of a tower from a plurality of extracted feature lines, and an inclination that calculates the inclination of the tower from the calculated center line And a calculating unit.

  The technical idea and description of the above-described steel tower inclination detection method can be applied to the steel tower inclination detection apparatus.

  ADVANTAGE OF THE INVENTION According to this invention, it becomes possible to provide the inclination detection method and inclination detection apparatus of a steel tower which can measure the inclination of a steel tower directly and quantitatively, and also can suppress the processing burden of the acquired data.

It is a block diagram explaining the inclination detection apparatus of the steel tower concerning embodiment of this invention. It is the flowchart which showed each process of the inclination detection method of the steel tower concerning embodiment of this invention. It is the figure which showed the outline | summary of the measurement by a ground type laser scanner. It is a subroutine which shows the detail of a data edit process. It is the figure which illustrated the outline | summary of the data editing process. It is a subroutine which shows the detail of the extraction process of a feature line. It is the figure which illustrated the plane which the steel tower extracted. It is the figure which illustrated the characteristic line of the main pillar material of a steel tower. It is the figure which illustrated a mode that the centerline of a steel tower was calculated. It is the figure which illustrated the inclination of the steel tower in each horizontal surface.

  Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. The dimensions, materials, and other specific numerical values shown in the embodiments are merely examples for facilitating understanding of the invention, and do not limit the present invention unless otherwise specified. In the present specification and drawings, elements having substantially the same function and configuration are denoted by the same reference numerals, and redundant description is omitted, and elements not directly related to the present invention are not illustrated. To do.

  FIG. 1 is a block diagram for explaining a tower inclination detection device (detection device 160) according to an embodiment of the present invention. FIG. 2 is a flowchart showing each process of the steel tower inclination detection method according to the embodiment of the present invention. In addition to directly and quantitatively measuring the inclination of the steel tower, the detection device 160 and the detection method take into consideration the suppression of the processing load of acquired data.

  An outline of the detection device 160 of FIG. 1 will be described. The detection device 160 roughly includes a memory 162 that stores programs and data, a CPU 164 that performs central processing, and an input / output unit 166 that exchanges data with the laser scanner 142. The CPU 164 functions as a control unit 168 that performs control such as execution of a program, and a calculation unit 170 that performs various calculations.

  In the detection method shown in FIG. 2, a three-dimensional main pillar material is represented by a line segment. A line segment representing the main pillar material is referred to as a feature line. The main outline of the processing performed by the detection method is as follows. First, in step 100, an existing steel tower 140 (see FIG. 3A) is scanned with a ground type laser scanner (laser scanner 142), and the main pillar material of the steel tower 140 is scanned. The feature line is extracted (step 104), the center line of the steel tower is calculated from the feature line of the main pillar (step 106), and the inclination is calculated from the center line of the steel tower (step 108). In the following, each process shown in the flowchart of FIG. 2 will be described in detail with the components of FIG.

  FIG. 3 is a diagram showing an outline of measurement by the terrestrial laser scanner 142. FIG. 3A shows an example of use of the laser scanner 142. In this embodiment, the point cloud data which is a three-dimensional coordinate is acquired from the existing steel tower 140 using the laser scanner 142. FIG.

  As a specific example of the laser scanner 142, for example, a phase difference type laser scanner FARO Focus 3D X330 (hereinafter abbreviated as X330) is used. The X330 includes a main body 144 that emits laser light and a tripod 146 that supports the main body 144. The main body 144 rotates in the horizontal direction while rotating the laser beam in the vertical direction, and acquires the three-dimensional coordinates of the surrounding terrain and structures as point cloud data. In particular, the X330 has a measurement range of 0.6 to 330 m and a maximum measurement speed of 976,00 points / second, which is superior to conventional terrestrial laser scanners, so it can be suitably used for the detection method. .

  In the detection method, the point cloud data is acquired by scanning from the center of the plurality of leg portions 148 of the steel tower 140 on the ground. In the detection method, when detecting the inclination of the steel tower 140, the characteristic line of the main pillar material extending from the leg portion 148 of the steel tower 140 is used in step 104 of FIG. Therefore, by scanning from the center of the leg portion 148, it is possible to efficiently acquire the three-dimensional coordinates of each main pillar material.

  In this embodiment, scanning is performed from one place inside (center) of the steel tower 140. On the other hand, scanning from the outside of the steel tower 140 is also conceivable. However, in order to acquire point cloud data from the outside of the tower 140, scanning must be performed from two or more locations. Then, it is necessary to align the coordinates of a plurality of point group data, but when an inclination of several millimeters is to be detected, an unacceptable error occurs. Therefore, it is important to scan from one place this time, and it is preferable to scan inside the steel tower 140, particularly at the center.

  FIG. 3B is an example of point cloud data acquired by the laser scanner 142. In addition to the steel tower 140, so-called raw data acquired by the laser scanner 142 includes point clouds indicating surrounding terrain and structures. These point clouds other than the steel tower 140 increase the processing load and may lead to a decrease in the accuracy of the inclination detection of the steel tower 140. Therefore, in the present embodiment, data editing processing of the acquired point cloud data is performed in step 102 of FIG.

  FIG. 4 is a subroutine showing details of the data editing process (step 102 in FIG. 2). The data editing process is performed in order to extract only the point cloud indicating the steel tower 140 from the raw data acquired by the laser scanner 142. This processing can be performed mainly by the calculation unit 170 in FIG. The process of FIG. 4 will be described below with reference to FIG.

  FIG. 5 is a diagram illustrating an outline of the data editing process. FIG. 5A is a diagram mainly illustrating the processing of step 110 in FIG. In step 110, the point cloud data acquired by the laser scanner 142 is divided into a predetermined grid pattern in the horizontal direction.

  In step 112 of FIG. 4, the installation area of the steel tower 140 is extracted from each area divided into a grid. In this embodiment, since the laser scanner 142 is placed at the center of the steel tower 140, only a point cloud within a certain distance from the center of the laser scanner 142 is acquired, thereby removing a distant apartment. Therefore, the first grating is set in a range including a circle having a constant distance from the center of the laser scanner 142 as shown in FIG.

  Moreover, since the steel tower 140 is narrowed toward the upper part, the point cloud only of the part below the power transmission line 150 is acquired, and it is set as the object area | region extraction. Then, the installation area is extracted by determining whether or not each area includes a measurement point where the difference in height of the coordinate data (the difference in height between the highest point and the lowest point) is a predetermined value or more. This is done by extracting the region where the measurement points with the above height exist.

  As described above, the measurement point at which the height difference is equal to or greater than the predetermined value is a measurement point obtained from a structure having a certain height. For example, in a certain area, the minimum vertical coordinate is regarded as the ground surface. And the area | region where the structure 5 m or more in height exists can be acquired by extracting the area | region containing the measurement point from which the height difference with the ground surface becomes 5 m or more. Then, as shown in FIG.5 (b), the grid | lattice of the position where a steel frame exists can be extracted. Here, since only the point group below the transmission line 150 is targeted, there is a gap in the center.

  In step 112, first, as shown in FIG. 5C, the hollow lattice is filled to make an installation area of the tower 140. Next, a process of filling the concave portion of the outer contour is performed. For this process, for example, a morphology operation used in existing image processing can be applied. Morphology calculation is a process of smoothing unevenness of an image or removing isolated points by performing expansion and contraction a plurality of times. As a result, as shown in FIG. 5D, there can be obtained a generally rectangular region having no concave portion.

  After extracting the installation area of the tower in step 112 of FIG. 4, the point cloud data above the installation area is reacquired (including the area above the transmission line 150), and then in subsequent step 114. Remove measurement points near the ground surface. FIG. 5E is a diagram illustrating only point cloud data indicating the steel tower 140. Thereby, the subroutine of FIG. 4 (step 102 of FIG. 2) ends.

  Returning again to the flowchart of FIG. After the data editing process in step 102 is completed, in step 104, a characteristic line indicating the main pillar material of the steel tower 140 is extracted. The characteristic line is a line indicating a corner or a ridge line of the shape. Generally, the main pillar material of the steel tower extends upward from four legs so as to constitute four corners of the steel tower. By extracting the characteristic lines of each of the plurality of main column members provided in the tower 140 from the point cloud data indicating the tower 140, the center line of the tower 140 can be calculated in the subsequent step 106.

  As the main pillar material of the steel tower 140, an L-shaped angle material is used in the angle steel tower, and a steel pipe is used in the steel pipe steel tower. In the case of an angle material, it is assembled with the inner angle side facing the center of the steel tower. Therefore, in step 104, it is possible to efficiently acquire the characteristic line of the main pillar material by extracting the inner corner valley line of the angle material. These processes can be performed by the calculation unit 170 of FIG. 1 functioning as the feature line extraction unit 172. Hereinafter, an example of the processing in step 104 will be described with reference to FIG.

  FIG. 6 is a subroutine showing details of the feature line extraction processing (step 104 in FIG. 2). FIG. 7 is a diagram illustrating a plane extracted by the steel tower, and FIG. 8 is a diagram illustrating feature lines L1 to L4 of the main pillar material of the steel tower. The subroutine of FIG. 6 is a process for efficiently acquiring the characteristic lines L1 to L4 of the main pillar material shown in FIG. 8 from the point cloud data of the steel tower shown in FIG.

  In extracting the valley line from the angle material as the main pillar material, first, in step 120 of FIG. 6, a plurality of planes are detected from the point cloud data of the steel tower 140. This means that the surface of each member of the steel tower 140 is acquired. As a method for detecting a plane, an existing method such as a RANSAC method (abbreviation of RANdom Sample Consensus; hereinafter referred to as a RANSAC method) or a region growth method can be used.

  When using the RANSAC method, the plane is calculated by the following procedure. Since three planes are uniquely determined, three of N points are taken at random, and a plane equation is calculated from the three points. Among N points, the number of distances from the plane within the allowable error range is counted. The above procedure is repeated a number of times, and the plane type with the largest number of points is adopted.

  The region growing method is a method for detecting a region satisfying a certain curved surface expression from point cloud data. First, an appropriate point group is selected by the user's selection or the like and used as a seed region. In addition, the surface equation is calculated using the points in the seed region. Next, the points near the seed region are examined, and if they are on the curved surface, the range is expanded in addition to the region. This process is continued until no points can be added. As a result, a cylindrical region or a planar region can be detected.

  By detecting the plane from the point group, the surfaces of the main pillar material, the brace material, the horizontal material, etc. are detected as shown in FIG. In step 122, planes adjacent to other planes are extracted from the plurality of planes. By this processing, a plurality of intersecting planes including the two inner surfaces of each angle member can be acquired.

  In step 124, an intersection line between adjacent planes is calculated. When the range of the plane is shifted, the common range is made an intersection line. The intersection lines calculated at this stage still include corners and ridge lines other than the main pillar material valley lines, and also the brace valley lines and various line segments extending diagonally and horizontally. Therefore, in step 126, for example, a line segment close to the vertical is extracted from the intersecting lines with a predetermined angle with respect to the vertical direction as a threshold value. This process can remove braided material, horizontal material valleys, and other miscellaneous lines to some extent.

  Then, at step 128, for each region where the main pillar material exists, for example, for each of the four corners of the steel tower 140, line segments close to vertical are grouped for each region where the main pillar material exists. Of line segments close to the vertical extracted in each group, line segments on the same straight line are divided as line segment sets, and line segments whose ends are close to each other are connected to each other. The determination on the same straight line is performed based on whether or not the angle of the direction vector of the line segment is equal to or less than a predetermined threshold (for example, 5 degrees or less). For example, when the distance between the ends of line segments is equal to or less than a predetermined threshold (for example, 10 cm or less), these line segments are connected as indicating a continuous shape.

  Even at the stage of step 128, not only the valleys of the main pillar material, but also the line segments acquired from other parts and accessories are included. However, at this stage, it is expected that the valley line, which is the intersection line of the two surfaces of the main pillar material, is most accurately acquired. Therefore, in step 130, in the line segment set that is on the same straight line and subjected to the connection process, the total lengths of the line segments are compared, and the longest line segment set is the valley line of the angle material. , The characteristic line. Thereby, the characteristic lines L1-L4 of the main pillar material shown in FIG. 8 can be acquired. Thus, the subroutine of FIG. 6 (step 104 of FIG. 2) is completed.

  When a steel pipe is used instead of an angle material as the main pillar material of the steel tower, the center line of the steel pipe may be extracted as a feature line in step 104 of FIG. Also with this configuration, it is possible to efficiently acquire the characteristic lines of the main pillar material.

  Returning again to the flowchart of FIG. After the extraction processing of the characteristic lines L1 to L4 is completed in step 104, the center line C1 (see FIG. 9) of the steel tower 140 is acquired in step 106. This processing is performed by the calculation unit 170 of FIG. 1 functioning as the center line calculation unit 174.

  FIG. 9 is a diagram illustrating a state in which the center line C1 of the steel tower 140 is calculated. The center point of the steel tower 140 can be calculated by calculating four intersections between the horizontal plane S1 and the characteristic lines L1 to L4 of the main pillar material, and obtaining the centers of these four intersections. And a center point can be calculated in various places of the tower 140 while changing the height of the horizontal plane S1, and a line segment connecting these center points can be calculated as the center line C1 of the tower 140.

  As the remaining processing, the inclination of the steel tower 140 is calculated in step 108 of FIG. This processing is performed by the calculation unit 170 of FIG. 1 functioning as the inclination calculation unit 176. FIG. 10 is a diagram illustrating the inclination of the steel tower 140 on each horizontal plane. The inclination of the steel tower 140 is calculated by obtaining a deviation of the center line C1 from a predetermined vertical axis. For example, FIG. 10A shows a calculation result of the inclination from a normal steel tower. The center line C1 has a small deviation with respect to the vertical axis and is detected in a dot shape. On the other hand, FIG.10 (b) is a calculation result of the inclination from the steel tower in which the non-uniform displacement occurred. The center line C2 has a large deviation from the vertical axis and is detected in a linear shape. By calculating the inclination of the steel tower 140 in this way, it is possible to achieve inconsistent displacement measurement of the steel tower 140. Thus, the processing of the detection method shown in FIG.

  According to the tower inclination detection device 160 and the inclination detection method in these embodiments, the inclination of the tower 140 can be directly and quantitatively measured without depending on the skill and experience of the worker. In particular, by performing processing based on the characteristic lines L1 to L4 (step 104) of the main column material, it is possible to efficiently process a large amount of point cloud data and to perform simple measurement with reduced costs and labor. .

  The data editing process in step 102 is also useful. Usually, the point cloud data acquired by the terrestrial laser scanner includes coordinates related to topography and structures other than the steel tower. Therefore, in the data editing process in step 102 of this embodiment, the installation area of the steel tower 140 is extracted (step 112 in FIG. 4), and only the point cloud data of the steel tower 140 can be processed. By these processes, it is possible to achieve a significant reduction in processing load such as human labor and working time.

  As mentioned above, although preferred embodiment of this invention was described referring an accompanying drawing, it cannot be overemphasized that this invention is not limited to the example which concerns. It will be apparent to those skilled in the art that various changes and modifications can be made within the scope of the claims, and these are naturally within the technical scope of the present invention. Understood.

  INDUSTRIAL APPLICABILITY The present invention can be used as a steel tower inclination detection method and an inclination detection apparatus that calculate the inclination of an existing steel tower.

L1 to L4 ... characteristic lines of main pillar material, S1 ... horizontal plane, C1 ... center line of normal steel tower, C2 ... center line of steel tower where non-uniform displacement occurs, 140 ... iron tower, 142 ... laser scanner, 144 ... main body part, 146: Tripod, 148: Leg, 150: Transmission line, 160: Detection device, 164 ... Memory, 166 ... Input / output unit, 168 ... Control unit, 170 ... Calculation unit, 172 ... Feature line extraction unit, 174 ... Center line Calculation unit, 176 ... inclination calculation unit

Claims (7)

The existing steel tower is scanned with a ground-type laser scanner to acquire point cloud data,
For each of a plurality of main pillar materials provided in the steel tower from the point cloud data, a feature line that is a line segment representing the main pillar material is extracted,
The center line of the tower is calculated from a plurality of the characteristic lines,
An inclination detection method for a tower, wherein the inclination of the tower is calculated from the center line.   2. The tower inclination detection method according to claim 1, wherein the point cloud data is acquired by scanning from the center of a plurality of legs of the tower on the ground. Dividing the point cloud data into a predetermined grid in the horizontal direction;
Of each of the divided areas, an area including a measurement point where the difference in height is equal to or greater than a predetermined value with respect to coordinates regarded as the ground surface is extracted as an installation area of the tower.
From the point cloud data of the installation area of the tower, the measurement points near the ground surface are removed and used as point cloud data indicating the tower,
The method for detecting a tilt of a steel tower according to claim 1, wherein the characteristic line is extracted from point cloud data indicating the steel tower.   4. The tower inclination detection method according to claim 1, wherein when the main pillar material is an angle material, a trough line of the angle material is extracted as the feature line. 5. When the main pillar material is an angle material,
Detecting a plurality of planes from the point cloud data of the predetermined range;
Extracting adjacent planes among the plurality of planes,
Calculate the intersection line between the planes,
Extract a line segment that is close to the vertical among the intersection lines,
Group the line segments close to the vertical for each region where the main pillar material exists,
Divide line segments on the same straight line in each group as a line segment set,
In each line segment set, the line segments whose ends are close to each other are connected, and the line segment set having the longest total of the lengths of the element line segments is the valley line of the angle material and the characteristic line of the main pillar material The tilt detection method for a steel tower according to any one of claims 1 to 4, wherein: When the main pillar material is a steel pipe,
A center line of the steel pipe is detected from the point cloud data in the predetermined range;
Extract a vertical line segment from the center line,
Group the line segments close to the vertical for each region where the main pillar material exists,
Divide line segments on the same straight line in each group as a line segment set,
The line segments whose ends are close to each other in each line segment set are connected to each other, and the line segment set having the longest sum of the lengths of the element line segments is the characteristic line of the main pillar material. Item 4. The tower inclination detection method according to any one of Items 1 to 3. A feature line extraction unit that extracts a feature line that is a line segment representing the main column material for each of a plurality of main column materials provided in the tower from the point cloud data of the tower acquired by the ground-type laser scanner;
A center line calculation unit for calculating the center line of the steel tower from the plurality of extracted characteristic lines;
An inclination calculating unit for calculating an inclination of the tower from the calculated center line;
A tilt detection apparatus for a steel tower characterized by comprising: