Analysis device, analysis method, and program

Abstract

Provided is a novel technology with which the characteristics of a crossarm of a power line tower can be acquired. Analysis device (1) comprises: a three dimensional data acquisition unit (2) that acquires three dimensional data including a power line tower; a crossarm extraction unit (3) that extracts, from the three dimensional data of the power line tower, the three dimensional data of crossarms of the power line tower, for each crossarm; and a crossarm characteristic identification unit (4) that determines the shape of the crossarms from the extracted three dimensional data of the crossarm.

Description

Technical Field

[0001] The present disclosure relates to an analysis device, an analysis method, and a non-transitory computer-readable medium storing a program.

Background Art

[0002] Since transmission towers are used for a very long time after being installed on the ground, some of the existing transmission towers were installed quite a long time ago. For such transmission towers, the design drawings may not be available. Therefore, the features of the transmission towers, particularly the shape of the cross arms of the transmission towers, are unknown. Thus, there is a need for a new technology that can acquire the features of transmission towers.

[0003] By the way, in recent years, various processes using point cloud data obtained by a LiDAR (Light Detection and Ranging) device have been proposed. For example, Patent Document 1 discloses a technique for evaluating the separation distance between a power transmission line supported by a transmission tower and surrounding ground objects using point cloud data.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, the technique disclosed in Patent Document 1 can evaluate the separation distance between the power transmission line and the object, but cannot acquire the features of the cross arms of the transmission tower. Therefore, there is still a need for a technique for acquiring the features of the cross arms of the transmission tower.

[0006] Therefore, one of the purposes to be achieved by the embodiments disclosed in this specification is to provide a novel technology capable of acquiring the characteristics of the cross arms of a transmission tower.

Means for Solving the Problems

[0007] The analysis device according to the first aspect of the present disclosure includes: three-dimensional data acquisition means for acquiring three-dimensional data including a transmission tower; cross arm extraction means for extracting three-dimensional data of the cross arms of the transmission tower from the three-dimensional data of the transmission tower for each cross arm; cross arm feature identification means for determining the shape of the cross arm from the three-dimensional data of the extracted cross arm and.

[0008] In the analysis method according to the second aspect of the present disclosure, three-dimensional data including a transmission tower is acquired, three-dimensional data of the cross arms of the transmission tower is extracted from the three-dimensional data of the transmission tower for each cross arm, and the shape of the cross arm is determined from the three-dimensional data of the extracted cross arm.

[0009] The program according to the third aspect of the present disclosure causes a computer to execute: a three-dimensional data acquisition step of acquiring three-dimensional data including a transmission tower; a cross arm extraction step of extracting three-dimensional data of the cross arms of the transmission tower from the three-dimensional data of the transmission tower for each cross arm; a cross arm feature identification step of determining the shape of the cross arm from the three-dimensional data of the extracted cross arm and.

Advantages of the Invention

[0010] According to the present disclosure, it is possible to provide a novel technology capable of acquiring the characteristics of the cross arms of a transmission tower.

Brief Description of the Drawings

[0011]

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Mode for Carrying Out the Invention

[0012] <Outline of the Embodiment> Before explaining the details of the embodiments, an overview of the embodiments will be described. FIG. 1 is a block diagram showing an example of the configuration of the analysis apparatus 1 according to the overview of the embodiments. As shown in FIG. 1, the analysis apparatus 1 includes a three-dimensional data acquisition unit 2, a brace extraction unit 3, and a brace feature identification unit 4.

[0013] The three-dimensional data acquisition unit 2 acquires three-dimensional data including a transmission tower. The three-dimensional data acquisition unit 2 may acquire the data by reading the data from a storage that holds the three-dimensional data including the transmission tower, or may acquire the three-dimensional data in real time from any sensor that generates the three-dimensional data of the measurement object. For example, the three-dimensional data acquisition unit 2 may acquire data from a LiDAR device, may acquire data from a stereo camera, or may acquire data generated by combining a camera and SfM (Structure from Motion) technology. The three-dimensional data is, for example, point cloud data, but may be data in other data formats such as polygon data.

[0014] The brace extraction unit 3 extracts the three-dimensional data of the braces of the transmission tower from the three-dimensional data of the transmission tower for each brace. Then, the brace feature identification unit 4 determines the shape of the brace from the three-dimensional data of the brace extracted by the brace extraction unit 3. When the three-dimensional data acquired by the three-dimensional data acquisition unit 2 also includes the three-dimensional data of the objects around the transmission tower, the analysis apparatus 1 may further include a transmission tower extraction unit that extracts the three-dimensional data of the transmission tower from the three-dimensional data acquired by the three-dimensional data acquisition unit 2. In other words, when the three-dimensional data acquired by the three-dimensional data acquisition unit 2 includes only the three-dimensional data of the transmission tower, the analysis apparatus 1 does not necessarily have to include a transmission tower extraction unit. When the analysis apparatus 1 includes a transmission tower extraction unit, the brace extraction unit 3 extracts the three-dimensional data of the braces of the transmission tower from the three-dimensional data of the transmission tower extracted by the transmission tower extraction unit.

[0015] In the analysis device 1 having the above configuration, three-dimensional data corresponding to the braces of the transmission tower is extracted from the three-dimensional data including the transmission tower. That is, the three-dimensional data corresponding to the braces is specified from the input three-dimensional data. Then, the shape of the braces is determined from this three-dimensional data of the braces. Thus, according to the analysis device 1, even when there is no design drawing of the transmission tower, it is possible to acquire the characteristics of the braces of the transmission tower.

[0016] <Details of the Embodiment> Next, details of the embodiment will be described. FIG. 2 is a block diagram showing an example of the configuration of the analysis device 100 according to the embodiment. As shown in FIG. 2, the analysis device 100 includes a point cloud data acquisition unit 101, a transmission tower extraction unit 102, a brace extraction unit 103, a brace feature specification unit 104, and a result output unit 105.

[0017] The point cloud data acquisition unit 101 corresponds to the three-dimensional data acquisition unit 2 in FIG. 1. In the present embodiment, as an example of the three-dimensional data, point cloud data including a transmission tower is acquired. The point cloud data can be obtained, for example, by scanning a space in a predetermined range including the transmission tower with a LiDAR device. Therefore, the point cloud data acquired by the point cloud data acquisition unit 101 may include not only the point cloud of the transmission tower but also the point cloud of the objects existing on the ground in the space. That is, the point cloud data acquired by the point cloud data acquisition unit 101 may include the point cloud of the objects on the ground around the transmission tower in addition to the point cloud of the transmission tower.

[0018] The transmission tower extraction unit 102 extracts the point cloud data of the transmission tower from the point cloud data acquired by the point cloud data acquisition unit 101. Specifically, the transmission tower extraction unit 102 extracts the point cloud data of the transmission tower based on the vertical distribution of the point cloud data for each partial space divided by dividing the horizontal plane into a grid pattern. The details of the processing of the transmission tower extraction unit 102 will be described later with reference to the flowchart.

[0019] The tower arm extraction unit 103 corresponds to the tower arm extraction unit 3 in FIG. 1. The tower arm extraction unit 103 extracts the point cloud data of the tower arms of the transmission tower from the point cloud data of the transmission tower extracted by the transmission tower extraction unit 102 for each tower arm. Specifically, the tower arm extraction unit 103 extracts the point cloud data of the tower arms based on the horizontal spread of the point cloud data for each predetermined section. Here, the predetermined section is a section defined by dividing in the vertical direction at a predetermined interval. That is, the tower arm extraction unit 103 extracts the point cloud data of the tower arms based on the horizontal spread of the point cloud data of each section obtained by dividing the point cloud data of the transmission tower extracted by the transmission tower extraction unit 102 at a predetermined interval in the vertical direction. Note that the details of the processing of the tower arm extraction unit 103 will be described later with reference to the flowchart.

[0020] The tower arm feature identification unit 104 corresponds to the tower arm feature identification unit 4 in FIG. 1. The tower arm feature identification unit 104 determines the shape of the tower arm from the point cloud data of the tower arm extracted by the tower arm extraction unit 103. Specifically, the tower arm feature identification unit 104 determines the shape of the tower arm based on the spread of the point cloud data in a predetermined direction for each predetermined section. Here, the predetermined section is a section defined by dividing in the longitudinal direction of the tower arm at a predetermined interval. That is, the tower arm feature identification unit 104 determines the shape of the tower arm based on the spread of the point cloud data in a predetermined direction of each section obtained by dividing the extracted point cloud data of the tower arm at a predetermined interval in the longitudinal direction of the tower arm. Here, the longitudinal direction of the tower arm corresponds to the direction in which the tower arm protrudes. Also, the predetermined direction is specifically a direction orthogonal to the longitudinal direction of the tower arm. In the present embodiment, this predetermined direction is more specifically the horizontal direction and is a direction orthogonal to the longitudinal direction of the tower arm.

[0021] Further, the tower arm feature identification unit 104 identifies the number of tower arms of the transmission tower and the height at which the tower arms are installed based on the extracted point cloud data of the tower arms. Also, the tower arm feature identification unit 104 identifies the length in the longitudinal direction of the tower arm based on the extracted point cloud data of the tower arms. Note that the details of the processing of the tower arm feature identification unit 104 will be described later with reference to the flowchart.

[0022] The result output unit 105 outputs the processing result by the analysis device 100. As the output of the processing result, the result output unit 105 may display it on a display or transmit it to another device. The result output unit 105 may output, for example, the shape of the stay wire, the number of stay wires, the installed height of the stay wire, or the length of the stay wire. Further, the result output unit 105 may output the point cloud data of the extracted transmission tower or the point cloud data of the extracted stay wire.

[0023] Figure 3 is a block diagram showing an example of the hardware configuration of the analysis device 100. As shown in Figure 3, the analysis device 100 includes an input / output interface 151, a memory 152, and a processor 153.

[0024] The input / output interface 151 is an interface for communicably connecting to other devices as necessary. For example, the input / output interface 151 may be used for the point cloud data acquisition unit 101 to acquire point cloud data, or may be used for the result output unit 105 to output the processing result.

[0025] The memory 152 is composed of, for example, a combination of a volatile memory and a non-volatile memory. The memory 152 is used to store software (computer program) including one or more instructions executed by the processor 153 and data used for various processes.

[0026] The processor 153 reads and executes software (computer program) from the memory 152 to perform the processing of each component shown in Figure 2. The processor 153 may be, for example, a microprocessor, an MPU (Micro Processor Unit), or a CPU (Central Processing Unit). The processor 153 may include a plurality of processors. Thus, the analysis device 100 has the functions of a computer.

[0027] The program includes a set of instructions (or software code) for causing a computer to perform one or more functions described in the embodiments when loaded into the computer. The program may be stored in a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, the computer-readable medium or tangible storage medium includes random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD), or other memory technologies, CD-ROM, digital versatile disc (DVD), Blu-ray (registered trademark) disc, or other optical disc storage, magnetic cassette, magnetic tape, magnetic disc storage, or other magnetic storage devices. The program may be transmitted on a transitory computer-readable medium or a communication medium. By way of example and not limitation, the transitory computer-readable medium or communication medium includes electrical, optical, acoustic, or other forms of propagated signals.

[0028] Next, the specific processing of each component shown in FIG. 2 will be described with reference to the flowchart. FIG. 4 is a flowchart showing an example of the operation of the analysis apparatus 100 according to the present embodiment.

[0029] In step S10, the point cloud data acquisition unit 101 acquires point cloud data including a power transmission tower. For example, the point cloud data acquisition unit 101 reads the point cloud data from a storage device such as the memory 152. As described above, the point cloud data acquired in step S10 may include point clouds of objects other than the power transmission tower. Here, it is assumed that the point cloud data acquisition unit 101 acquires point cloud data having coordinates in a rectangular coordinate system with the X-axis, Y-axis, and Z-axis as coordinate axes. Also, here, it is assumed that the XY plane, that is, the plane parallel to the X-axis and Y-axis, is the horizontal plane, and the Z-axis is the vertical axis. Note that, for example, the horizontal plane where the value of the Z coordinate is 0 represents the ground.

[0030] Next, in step S11, the armature extraction unit 103 performs a process of specifying the height of the lowermost power transmission line for the extraction process of the point cloud data of the armature described later. FIG. 5 is a flowchart showing an example of the specific process flow of step S11 in FIG. 4. Further, FIG. 6 is a schematic diagram for explaining the process of step S11 in FIG. 4. Hereinafter, the details of the process of step S11 will be described.

[0031] First, in step S110, the armature extraction unit 103 thinly divides the point cloud data acquired in step S10 in the height direction (vertical direction). That is, the armature extraction unit 103 divides the acquired point cloud data by partitioning it at a predetermined interval in the Z-axis direction. Note that this predetermined interval is preferably as narrow as possible. This is to ensure that only the spread of points in the horizontal direction is analyzed in the principal component analysis described below. In other words, it is to prevent the spread of points in the vertical direction from being analyzed. As an example, this interval is 0.5 meters, but the specific value is not limited to this.

[0032] Next, in step S111, the armature extraction unit 103 performs principal component analysis (PCA) for each of the divided point cloud data. That is, the armature extraction unit 103 performs principal component analysis on the point cloud data for each interval. The direction of the eigenvector of the first principal component obtained by the principal component analysis means the direction in which the points spread the most. And the eigenvalue of the first principal component means the magnitude of the spread in that direction. Also, the direction of the eigenvector of the second principal component means the direction in which the points spread the most among the directions orthogonal to the direction of the eigenvector of the first principal component. And the eigenvalue of the second principal component means the magnitude of the spread in that direction.

[0033] As shown in the schematic diagram at the lower left of FIG. 6, in the point cloud data at the height where the power transmission line exists, points are distributed in a slender shape in the extending direction of the power transmission line. In this figure, two parallel dashed lines represent the point cloud of the power transmission line, and the dashed line existing between the two parallel dashed lines represents the point cloud of the power transmission tower. Therefore, in the principal component analysis of the point cloud data in the section where the power transmission line exists, the eigenvalue of the first principal component becomes a relatively large value, and the eigenvalue of the second principal component becomes a relatively small value.

[0034] Also, as shown in the schematic diagram at the lower center of FIG. 6, in the point cloud data at the height where only the power transmission tower (tower body) exists, points are distributed only within the range where the power transmission tower exists. In this figure, the rectangular dashed line represents the point cloud of the power transmission tower. Therefore, in the principal component analysis of the point cloud data in the section where only the power transmission tower exists, both the eigenvalue of the first principal component and the eigenvalue of the second principal component become relatively small values.

[0035] Also, as shown in the schematic diagram at the lower right of FIG. 6, in the point cloud data at the height where other objects on the ground exist, points are distributed in various places. In this figure, the rectangular dashed line represents the point cloud of the power transmission tower, and the circular dots around it represent the point cloud of other objects existing on the ground. Therefore, in the principal component analysis of the point cloud data in the section where other objects on the ground exist, both the eigenvalue of the first principal component and the eigenvalue of the second principal component become relatively large values.

[0036] For this reason, the ratio of the eigenvalue of the first principal component to the eigenvalue of the second principal component becomes a relatively large value for the point cloud data at the height where the power transmission line exists, and a relatively small value for the point cloud data at other heights. In the present embodiment, paying attention to this, the height of the lowermost power transmission line is specified. Specifically, in step S112, the bracket extraction unit 103 sets the height of the lowermost section among the sections where the ratio of the eigenvalue of the first principal component to the eigenvalue of the second principal component exceeds a predetermined threshold as the height of the lowermost power transmission line. Since the bracket exists above the lowermost power transmission line, specifying the height of the lowermost power transmission line makes it easier to identify the point cloud of the bracket.

[0037] Returning to FIG. 4, the description of the flowchart continues. After step S11, the process of step S12 is performed. In the example shown in FIG. 4, the process of step S12 is performed after step S11, but step S12 may be performed before step S11.

[0038] In step S12, the power transmission tower extraction unit 102 performs a process of extracting the point cloud data of the power transmission tower from the point cloud data acquired in step S10. FIG. 7 is a flowchart showing an example of the specific process flow of step S12 in FIG. 4. FIG. 8 is a schematic diagram for explaining the process of step S12 in FIG. 4. Hereinafter, the details of the process of step S12 will be described.

[0039] First, in step S120, the power transmission tower extraction unit 102 identifies the highest point P among the points included in the point cloud data acquired in step S10. That is, the power transmission tower extraction unit 102 identifies the point with the largest Z coordinate value.

[0040] Next, in step S121, the power transmission tower extraction unit 102 extracts the point cloud within the cylindrical region centered on the vertical line passing through the point identified in step S120. Specifically, when the power transmission tower extraction unit 102 projects the point cloud data acquired in step S10 onto the XY plane, it extracts the point cloud that will be included in a circle with a predetermined radius R centered on the XY coordinates of the point identified in step S120. Thereby, the point cloud of an object that is separated from the power transmission tower by a predetermined distance (i.e., the above-mentioned predetermined radius) or more is excluded. Note that this predetermined radius may be set according to the voltage of the electricity flowing through the power transmission line supported by the cross arm of the power transmission tower. Generally, from the viewpoint of safety, the higher the voltage of the electricity flowing through the power transmission line supported by the cross arm, the longer the cross arm used. For this reason, the higher the voltage of the electricity flowing through the power transmission line, the wider the range where the point cloud data of the power transmission tower exists. Therefore, when the voltage value is known, by using the radius set according to the voltage, the point cloud data of the power transmission tower can be extracted more appropriately.

[0041] Next, in step S122, the power transmission tower extraction unit 102 divides the point cloud extracted in step S121 by a grid set in the XY plane (horizontal plane). As a result, the point cloud data extracted in step S121 will belong to any of the partial spaces divided by partitioning the horizontal plane into a grid pattern.

[0042] Since the power transmission tower is a structure extending in the vertical direction, in the region where the power transmission tower exists, points exist at various heights. On the other hand, in the region where objects other than the power transmission tower exist, the points are biased to a certain height, such as near the ground. Therefore, it is possible to determine whether each partial space is a partial space to which the point cloud data including the power transmission tower belongs based on the vertical variation in the distribution of the point cloud data belonging to each partial space. In the present embodiment, paying attention to this, the point cloud data of the power transmission tower is extracted.

[0043] Therefore, in step S123, the power transmission tower extraction unit 102 extracts the point cloud data of the power transmission tower by specifying the partial space including the power transmission tower based on the vertical distribution of the point cloud data for each partial space divided by partitioning the horizontal plane into a grid pattern. Specifically, the power transmission tower extraction unit 102 calculates the standard deviation of the height values (Z coordinate values) for the point cloud included in each grid (each partial space). Then, the power transmission tower extraction unit 102 extracts the grid (partial space) whose calculated standard deviation value exceeds a predetermined threshold as the grid (partial space) including the power transmission tower. Thereby, the point cloud data of the power transmission tower is extracted. In the present embodiment, the power transmission tower extraction unit 102 calculates the standard deviation as an index representing the magnitude of the vertical variation of the points, but other indices representing the magnitude of the variation (for example, variance) may be calculated. Note that in this step, in addition to the point cloud of the power transmission tower, the point cloud of other ground objects belonging to the same grid as the grid to which the point cloud of the power transmission tower belongs may also be extracted. However, the point cloud of such an object can be easily excluded by excluding the point cloud existing at a low position (a position lower than the lowest power transmission line).

[0044] Incidentally, in the present embodiment, as shown in step S121, a point group within a cylindrical region having a predetermined radius with a vertical line passing through the point specified in step S120 as the central axis is set as the extraction target of the three-dimensional data of the transmission tower. However, the process of step S121 may be omitted, and the processes of step S122 and step S123 may be performed on the point group data obtained in step S10.

[0045] Returning to FIG. 4, the description of the flowchart will be continued. After step S12, the process of step S13 is performed. In step S13, the cross-arm extraction unit 103 performs a process of extracting point group data of the cross-arms included in the transmission tower from the point group data of the transmission tower extracted in step S12. FIG. 9 is a flowchart showing an example of the specific process flow of step S13 in FIG. 4. FIG. 10 is a schematic diagram for explaining the process of step S13 in FIG. 4. Hereinafter, the details of the process of step S13 will be described.

[0046] First, in step S130, the cross-arm extraction unit 103 thinly divides in the height direction (vertical direction) the point group data of the space above the height of the lowermost transmission line specified in step S11 (step S112) among the point group data of the transmission tower obtained in step S12 (step S123). That is, the cross-arm extraction unit 103 divides the point group data by partitioning the point group data of the space above the height of the lowermost transmission line at a predetermined interval in the Z-axis direction. Note that this predetermined interval is preferably as narrow as possible. As an example, this interval is 0.5 meters, but the specific value is not limited to this.

[0047] Next, in step S131, the arm bar extraction unit 103 performs principal component analysis for each of the divided point cloud data. That is, the arm bar extraction unit 103 performs principal component analysis on the point cloud data for each interval. As shown in FIG. 10, since the point cloud in the interval that does not include the arm bar does not have a structure that protrudes in the horizontal direction, the value of the eigenvalue of the first principal component is relatively small, while the point cloud in the interval that includes the arm bar has a relatively large value of the eigenvalue of the first principal component because the arm bar (and the transmission line) exists. In the present embodiment, paying attention to this fact, the point cloud data of the arm bar is extracted.

[0048] Specifically, in step S132, the arm bar extraction unit 103 extracts the point cloud whose eigenvalue of the first principal component is equal to or greater than a predetermined threshold as the point cloud including the arm bar. In this way, the arm bar extraction unit 103 determines, for each interval, whether the point cloud data of the interval includes the point cloud data of the arm bar based on the horizontal spread of the point cloud data of each interval obtained by dividing the extracted point cloud data of the transmission tower at a predetermined interval in the vertical direction. Thereby, the arm bar extraction unit 103 extracts the point cloud data of the arm bar.

[0049] Note that the threshold value compared with the eigenvalue of the first principal component may be set according to the voltage of the electricity flowing through the transmission line supported by the arm bar of the transmission tower. That is, the arm bar extraction unit 103 may extract the point cloud data of the arm bar by comparing the threshold value set according to this voltage with the magnitude of the horizontal spread of the point cloud data of the interval. As described above, generally, the higher the voltage of the electricity flowing through the transmission line, the longer the arm bar used. Therefore, the higher the voltage of the electricity flowing through the transmission line, the more preferably a larger threshold value is used. Therefore, when the value of the voltage is known, the point cloud data of the arm bar can be more appropriately extracted by using the threshold value set according to the voltage.

[0050] Incidentally, in this embodiment, the extraction range of the cross arms is limited using the height obtained in the process of step S11. Generally, since the lower part of a transmission tower is thick, if principal component analysis is performed without limiting the extraction range, the eigenvalue of the first principal component in the section corresponding to the lower part of the transmission tower may also be equal to or greater than a predetermined threshold. Therefore, it is preferable to limit the extraction range of the cross arms. However, the cross arm extraction unit 103 may extract, as the point group including the cross arms, the point group in which the ratio of the eigenvalue of the first principal component to the eigenvalue of the second principal component exceeds a predetermined threshold. In this case, it may not be necessary to limit the extraction range of the cross arms. The reason why the point group including the cross arms can be extracted by comparing the ratio of the eigenvalue of the first principal component to the eigenvalue of the second principal component with the predetermined threshold is that the ratio of the eigenvalues in the section not including the cross arms takes a value close to 1, while the ratio of the eigenvalues in the section including the cross arms takes a value greater than 1. Further, instead of limiting the extraction range of the cross arms using the height obtained in the process of step S11, the extraction range of the cross arms may be limited using a predetermined height value that is uniformly set regardless of the acquired point group.

[0051] Returning to FIG. 4, the description of the flowchart will be continued. After step S13, the process of step S14 is performed. In step S14, based on the point group data of the cross arms extracted in step S13, the cross arm feature identification unit 104 identifies the number of cross arms of the transmission tower and the height at which the cross arms are installed. The number of cross arms is identified from the number of extractions of the point group data of the cross arms. Further, the height of the cross arms is identified from the Z coordinate of the point group data of the cross arms. In this embodiment, the length in the longitudinal direction of the cross arms is identified in step S17 described later, but it may also be identified in this step using the X coordinate and the Y coordinate of the point group data of the cross arms.

[0052] After step S14, the process of step S15 is performed. In step S15, for the shape identification process of the bracelet described later, the bracelet feature identification unit 104 performs a process of rotating the point cloud data of the bracelet extracted in step S13 so as to be parallel to the coordinate axes. In the present embodiment, as an example, the point cloud data of the bracelet is rotated with the Z axis as the rotation axis so as to be parallel to the X axis. FIG. 11 is a flowchart showing an example of the specific process flow of step S15 in FIG. 4. FIG. 12 is a schematic diagram for explaining the process of step S15 in FIG. 4. Hereinafter, the details of the process of step S15 will be described.

[0053] First, in step S150, the bracelet feature identification unit 104 translates the point cloud so that the center of gravity of the point cloud of the bracelet extracted in step S13 is located at the origin of the coordinate system. Next, in step S151, the bracelet feature identification unit 104 projects the translated point cloud of the bracelet onto the XY plane.

[0054] Next, in step S152, the point cloud projected onto the XY plane is gradually rotated in the range from 0 to 180 degrees, and the rotation angle at which the area of the bounding box surrounding the point cloud becomes the minimum is searched. Here, the shape of the bounding box is a rectangle having sides parallel to the X axis and sides parallel to the Y axis. Here, in order to obtain the rotation angle at which the point cloud data of the bracelet becomes parallel to the X axis, the bracelet feature identification unit 104 searches for the rotation angle that minimizes the area for the bounding box in which the side parallel to the X axis is the long side and the side parallel to the Y axis is the short side.

[0055] Then, in step S153, the bracelet feature identification unit 104 rotates the translated point cloud of the bracelet by the angle obtained in step S152. Through such processing, the point cloud of the bracket is positioned at the origin, and the longitudinal direction of the bracket overlaps with the X-axis, so that the point cloud is arranged in the coordinate system. As a result, the size in the longitudinal direction of the bracket can be specified by the X coordinate, and the size in the horizontal direction orthogonal to the longitudinal direction of the bracket can be specified by the Y coordinate. Therefore, the shape identification and length identification described later can be easily performed. However, even without performing such coordinate transformation, it is possible to specify the size in the longitudinal direction and the size in the horizontal direction orthogonal to the longitudinal direction from both the X coordinate and the Y coordinate. Therefore, the process of step S15 may be omitted.

[0056] Returning to FIG. 4, the description of the flowchart will be continued. After step S15, the process of step S16 is performed. In step S16, the bracket feature identification unit 104 performs a process of identifying the shape of the bracket. FIG. 13 is a flowchart showing an example of the specific process flow of step S16 in FIG. 4. FIGS. 14 and 15 are schematic diagrams for explaining the process of step S16 in FIG. 4. Hereinafter, the details of the process of step S16 will be described.

[0057] First, in step S160, as shown in FIG. 14, the bracket feature identification unit 104 clusters the point cloud of the bracket on which the rotation process was performed in step S15 by the value of the Z coordinate (height). Then, the bracket feature identification unit 104 performs the following processing on each cluster to determine the shape of the bracket. In the present embodiment, as an example, the bracket feature identification unit 104 determines whether the shape of the tip side of the bracket is a triangular pyramid or a truncated pyramid. That is, the bracket feature identification unit 104 determines whether the shape of the bracket as viewed from above is a triangle or a trapezoid.

[0058] In step S161, as shown in the left figure of FIG. 15, the bracket feature identification unit 104 thinly divides the point cloud data of the bracket obtained as a cluster in the longitudinal direction (X-axis direction) of the bracket. That is, the bracket feature identification unit 104 divides the point cloud data of the bracket by separating the point cloud data at a predetermined interval in the longitudinal direction of the bracket.

[0059] Next, in step S162, the bracelet feature identification unit 104 calculates the width of the point cloud in the direction orthogonal to the longitudinal direction (Y-axis direction) for each section. Then, in step S163, as shown in the right diagram of FIG. 15, the bracelet feature identification unit 104 identifies the portions P1 and P2 where the width is minimized for each bracelet.

[0060] Next, in step S164, the bracelet feature identification unit 104 determines the shape based on the analysis result of the width of the point cloud in the direction orthogonal to the longitudinal direction. In the present embodiment, when the magnitude of the width of the portion identified in step S163 is equal to or greater than a predetermined threshold, the bracelet feature identification unit 104 determines that the shape of the bracelet is a frustum of a cone, and when it is less than the predetermined threshold, the bracelet feature identification unit 104 determines that the shape of the bracelet is a triangular pyramid. For example, in the right diagram of FIG. 15, it is determined that the shape of the bracelet having portion P1 is a triangular pyramid, and the shape of the bracelet having portion P2 is a frustum of a cone. Thus, the bracelet feature identification unit 104 determines the shape of the bracelet based on the spread of the point cloud data in each section in the direction orthogonal to the longitudinal direction. Note that in the present embodiment, as an example, the bracelet feature identification unit 104 determines whether the shape of the bracelet is a triangular pyramid or a frustum of a cone, but the bracelet feature identification unit 104 may similarly determine any other shape based on the spread of the point cloud data in each section in the direction orthogonal to the longitudinal direction.

[0061] Returning to FIG. 4, the description of the flowchart continues. After step S16, the process of step S17 is performed. In step S17, the bracelet feature identification unit 104 identifies the length of each bracelet based on the point cloud data of the bracelet. For example, the bracelet feature identification unit 104 uses the absolute value of the X coordinate of the portion where the width in the direction orthogonal to the longitudinal direction, which was identified in step S16, is minimized as the length of the bracelet.

[0062] Next, after step S17, the process of step S18 is performed. In step S18, the result output unit 105 outputs the result obtained by the above-described process. For example, the result output unit 105 outputs the number of braces, and the shape, installation height, and length of each brace. Further, the result output unit 105 may output the point cloud data of the extracted transmission tower or the point cloud data of the extracted brace.

[0063] The embodiments have been described above. According to the present embodiment, the point cloud data of the portion corresponding to the brace of the transmission tower is specified from the point cloud data including the transmission tower. Then, features such as the shape are specified from the point cloud data of this brace. Thus, according to the analysis device 100, even when there is no design drawing of the transmission tower, it is possible to acquire features such as the shape of the transmission tower. In the above-described embodiment, the shape, number, installation height, and length of the brace are specified. However, the analysis device 100 may specify only any one of them without specifying all of them.

[0064] The present invention has been described with reference to the embodiments. However, the present invention is not limited to the above. Various changes that can be understood by those skilled in the art within the scope of the invention can be made to the configuration and details of the present invention.

[0065] Some or all of the above embodiments may be described as follows in the appended claims, but are not limited thereto. (Appended Claim 1) Three-dimensional data acquisition means for acquiring three-dimensional data including a transmission tower, Brace extraction means for extracting, for each brace, three-dimensional data of the brace of the transmission tower from the three-dimensional data of the transmission tower, Brace feature specifying means for determining the shape of the extracted brace from the three-dimensional data of the brace An analysis device having the same. (Appended Claim 2) The analysis device further includes transmission tower extraction means for extracting three-dimensional data of the transmission tower from the three-dimensional data acquired by the three-dimensional data acquisition means, The arm brace extraction means extracts the three-dimensional data of the arm brace from the three-dimensional data of the power transmission tower extracted by the power transmission tower extraction means. The analysis device according to Supplementary Note 1. (Supplementary Note 3) The three-dimensional data is point cloud data, The power transmission tower extraction means extracts the three-dimensional data of the power transmission tower based on the vertical distribution of the point cloud data for each partial space divided by dividing the horizontal plane into a grid pattern. The analysis device according to Supplementary Note 2. (Supplementary Note 4) The power transmission tower extraction means sets, as an extraction target for the three-dimensional data of the power transmission tower, the point cloud within a cylindrical region having a predetermined radius with a straight line in the vertical direction passing through the highest point among the points included in the acquired point cloud data as the central axis. The analysis device according to Supplementary Note 3. (Supplementary Note 5) The predetermined radius is set according to the voltage of the electricity flowing through the power transmission line supported by the arm brace of the power transmission tower. The analysis device according to Supplementary Note 4. (Supplementary Note 6) The arm brace extraction means extracts the three-dimensional data of the arm brace based on the horizontal spread of the three-dimensional data for each section obtained by dividing the three-dimensional data of the power transmission tower at predetermined intervals in the vertical direction. The analysis device according to any one of Supplementary Notes 1 to 5. (Supplementary Note 7) The arm brace extraction means extracts the three-dimensional data of the arm brace by comparing a threshold value set according to the voltage of the electricity flowing through the power transmission line supported by the arm brace of the power transmission tower with the magnitude of the horizontal spread of the three-dimensional data for the section. The analysis device according to Supplementary Note 6. (Supplementary Note 8) The arm brace feature identification means determines the shape of the arm brace based on the spread in a predetermined direction of the three-dimensional data for each section obtained by dividing the extracted three-dimensional data of the arm brace at predetermined intervals in the longitudinal direction of the arm brace, The predetermined direction is a direction orthogonal to the longitudinal direction. The analysis device according to any one of Supplementary Notes 1 to 7. (Supplementary Note 9) Based on the three-dimensional data of the cross arm extracted, the number of cross arms of the transmission tower is specified by the cross arm feature specifying means. The analysis device according to any one of Supplementary Notes 1 to 8. (Supplementary Note 10) Based on the three-dimensional data of the cross arm extracted, the height at which the cross arm is installed is specified by the cross arm feature specifying means. The analysis device according to any one of Supplementary Notes 1 to 9. (Supplementary Note 11) Based on the three-dimensional data of the cross arm extracted, the length in the longitudinal direction of the cross arm is specified by the cross arm feature specifying means. The analysis device according to any one of Supplementary Notes 1 to 10. (Supplementary Note 12) Three-dimensional data including a transmission tower is acquired. Three-dimensional data of the cross arms of the transmission tower is extracted for each cross arm from the three-dimensional data of the transmission tower. The shape of the cross arm is determined from the three-dimensional data of the cross arm extracted. Analysis method. (Supplementary Note 13) A three-dimensional data acquisition step of acquiring three-dimensional data including a transmission tower, A cross arm extraction step of extracting three-dimensional data of the cross arms of the transmission tower for each cross arm from the three-dimensional data of the transmission tower, A cross arm feature specifying step of determining the shape of the cross arm from the three-dimensional data of the cross arm extracted, and A non-temporary computer-readable medium storing a program for causing a computer to execute the above steps.

Explanation of Signs

[0066] 1 Analysis device 2 Three-dimensional data acquisition unit 3 Cross arm extraction unit 4 Cross arm feature specifying unit 100 Analysis device 101 Point cloud data acquisition unit 102 Transmission tower extraction unit 103 Brace extraction unit 104 Brace feature identification unit 105 Result output unit 151 Input / output interface 152 Memory 153 Processor

Claims

1. Three-dimensional data acquisition means for acquiring three-dimensional data including a power transmission tower, Power transmission tower extraction means for extracting three-dimensional data of the power transmission tower from the three-dimensional data acquired by the three-dimensional data acquisition means, Arm bar extraction means for extracting three-dimensional data of the arm bars of the power transmission tower for each arm bar from the three-dimensional data of the power transmission tower extracted by the power transmission tower extraction means, Arm bar feature identification means for determining the shape of the arm bar from the extracted three-dimensional data of the arm bar An analysis device having the above.

2. The three-dimensional data is point cloud data, The power transmission tower extraction means extracts the three-dimensional data of the power transmission tower based on the vertical distribution of the point cloud data for each partial space divided by dividing a horizontal plane into a grid pattern. The analysis device according to Claim 1.

3. The power transmission tower extraction means sets, as an extraction target for the three-dimensional data of the power transmission tower, a point cloud within a cylindrical region having a predetermined radius with a vertical line passing through the highest point among the points included in the acquired point cloud data as the central axis. The analysis device according to Claim 2.

4. The predetermined radius is set according to the voltage of the electricity flowing through the power transmission line supported by the arm bar of the power transmission tower. The analysis device according to Claim 3.

5. Three-dimensional data acquisition means for acquiring three-dimensional data including a power transmission tower, Arm bar extraction means for extracting three-dimensional data of the arm bars of the power transmission tower for each arm bar from the three-dimensional data of the power transmission tower, Arm bar feature identification means for determining the shape of the arm bar from the extracted three-dimensional data of the arm bar having the above, The arm bar extraction means extracts the three-dimensional data of the arm bar based on the horizontal spread of the three-dimensional data of each section obtained by dividing the three-dimensional data of the power transmission tower at a predetermined interval in the vertical direction. An analysis device.

6. The arm bar extraction means extracts the three-dimensional data of the arm bar by comparing a threshold value set according to the voltage of the electricity flowing through the power transmission line supported by the arm bar of the power transmission tower with the magnitude of the horizontal spread of the three-dimensional data of the section. The analysis device according to Claim 5.

7. Three-dimensional data acquisition means for acquiring three-dimensional data including a power transmission tower, Arm bar extraction means for extracting three-dimensional data of the arm bars of the power transmission tower for each arm bar from the three-dimensional data of the power transmission tower, Arm bar feature identification means for determining the shape of the arm bar from the extracted three-dimensional data of the arm bar having the above, The tower arm feature identification means determines the shape of the tower arm based on the spread of the three-dimensional data of each section obtained by dividing the extracted three-dimensional data of the tower arm at predetermined intervals in the longitudinal direction of the tower arm. The predetermined direction is a direction orthogonal to the longitudinal direction. Analysis device.

8. Acquire three-dimensional data including a transmission tower. Extract the three-dimensional data of the transmission tower from the acquired three-dimensional data. Extract the three-dimensional data of the tower arms of the transmission tower from the extracted three-dimensional data of the transmission tower for each tower arm. Determine the shape of the tower arm from the extracted three-dimensional data of the tower arm. Analysis method.

9. Acquire three-dimensional data including a transmission tower. Extract the three-dimensional data of the tower arms of the transmission tower from the three-dimensional data of the transmission tower for each tower arm. Determine the shape of the tower arm from the extracted three-dimensional data of the tower arm. In the extraction of the three-dimensional data of the tower arm, the three-dimensional data of the tower arm is extracted based on the horizontal spread of the three-dimensional data of each section obtained by dividing the three-dimensional data of the transmission tower at predetermined intervals in the vertical direction. Analysis method.

10. Acquire three-dimensional data including a transmission tower. Extract the three-dimensional data of the tower arms of the transmission tower from the three-dimensional data of the transmission tower for each tower arm. Determine the shape of the tower arm from the extracted three-dimensional data of the tower arm. In the determination of the shape of the tower arm, the shape of the tower arm is determined based on the spread of the three-dimensional data of each section obtained by dividing the extracted three-dimensional data of the tower arm at predetermined intervals in the longitudinal direction of the tower arm. The predetermined direction is a direction orthogonal to the longitudinal direction. Analysis method.

11. A three-dimensional data acquisition step of acquiring three-dimensional data including a transmission tower, A transmission tower extraction step of extracting the three-dimensional data of the transmission tower from the three-dimensional data acquired in the three-dimensional data acquisition step, A tower arm extraction step of extracting the three-dimensional data of the tower arms of the transmission tower from the three-dimensional data of the transmission tower extracted in the transmission tower extraction step for each tower arm, A tower arm feature identification step of determining the shape of the tower arm from the extracted three-dimensional data of the tower arm A program for causing a computer to execute.

12. A three-dimensional data acquisition step of acquiring three-dimensional data including a transmission tower. A brace extraction step of extracting three-dimensional data of the braces of the power transmission tower from the three-dimensional data of the power transmission tower for each brace; A brace feature identification step of determining the shape of the brace from the three-dimensional data of the extracted brace; causing a computer to execute; In the brace extraction step, the three-dimensional data of the brace is extracted based on the horizontal spread of the three-dimensional data of each section obtained by dividing the three-dimensional data of the power transmission tower at a predetermined interval in the vertical direction. Program.

13. A three-dimensional data acquisition step of acquiring three-dimensional data including a power transmission tower; A brace extraction step of extracting three-dimensional data of the braces of the power transmission tower from the three-dimensional data of the power transmission tower for each brace; A brace feature identification step of determining the shape of the brace from the three-dimensional data of the extracted brace; causing a computer to execute; In the brace feature identification step, the shape of the brace is determined based on the spread of the three-dimensional data of each section obtained by dividing the three-dimensional data of the extracted brace at a predetermined interval in the longitudinal direction of the brace, wherein the predetermined direction is a direction orthogonal to the longitudinal direction Program.

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