Linear object detection device, linear object detection method, and linear object detection program

Abstract

This linear object detection device comprises: a straight line detection unit that detects, with respect to a three-dimensional point cloud representing three-dimensional coordinates at points on the surface of a structure, straight lines on the basis of the three-dimensional coordinates, divides the three-dimensional point cloud related to one of the detected straight lines according to the distance between three-dimensional points related to the one detected straight line, and detects straight lines again on the basis of the three-dimensional coordinates for each divided three-dimensional point cloud; and a classification unit that groups the straight lines on the basis of the orientations of the plurality of straight lines detected and angles between the straight lines and the ground, and classifies straight lines among the straight lines included in the same group as straight lines corresponding to the same structure if angles between the straight lines and line segments connecting the straight lines are within a threshold value.

Description

[Technical Field]

[0001] The disclosed technology relates to a linear object detection device, a linear object detection method, and a linear object detection program. [Background technology]

[0002] Conventionally, a technology (Mobile Mapping System: MMS) has been developed to create a 3D model of an outdoor structure using an on-board 3D laser scanner. For example, Patent Document 1 describes a technology in which a point cloud acquired during one rotation of the laser scanner is treated as a cluster called a scan line, and by detecting whether adjacent clusters are in a catenary shape, 3D model data representing a structure such as a cable is created. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6531051 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the technology described in Patent Document 1, if an error occurs in the point cloud coordinates in a high-density point cloud, for example, when the intervals between scan lines of a laser scanner are short, the error may cause the line to not be determined as a catenary line. As a result, there is a problem that linear objects such as cables cannot be detected with high accuracy.

[0005] The disclosed technology has been made in consideration of the above points, and aims to provide a linear object detection device, a linear object detection method, and a linear object detection program that can accurately detect linear objects even when the three-dimensional point cloud representing the three-dimensional coordinates is high density. [Means for solving the problem]

[0006] A first aspect of the present disclosure is a linear object detection device comprising: a line detection unit that detects lines based on the three-dimensional coordinates of a three-dimensional point cloud representing the three-dimensional coordinates of points on the surface of a structure, divides the three-dimensional point cloud related to the line according to the distance between the three-dimensional points related to one of the detected lines, and again detects lines based on the three-dimensional coordinates for each of the divided three-dimensional point clouds; and a classification unit that groups the detected lines based on the angles between the lines and the ground and the orientations of the lines, and classifies, for each line included in the same group, lines where the angle between the line segment connecting the lines and the line connected by the line segment is within a threshold value as lines corresponding to the same structure.

[0007] A second aspect of the present disclosure is a linear object detection method, in which a line detection unit detects lines based on three-dimensional coordinates for a three-dimensional point cloud representing the three-dimensional coordinates of points on the surface of a structure, divides the three-dimensional point cloud related to the line according to the distance between the three-dimensional points related to one of the detected lines, and again detects lines based on the three-dimensional coordinates for each of the divided three-dimensional point clouds, and a classification unit groups the lines based on the angles between the detected lines and the ground and the orientations of the lines, and classifies, for each line included in the same group, lines for which the angle between a line segment connecting the lines and a line connected by the line segment is within a threshold as lines corresponding to the same structure.

[0008] A third aspect of the present disclosure is a linear object detection program for causing a computer to function as each part of the linear object detection device of the first aspect. [Effects of the Invention]

[0009] According to the disclosed technology, linear objects can be detected with high accuracy even if the three-dimensional point cloud representing three-dimensional coordinates has a high density. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a configuration diagram illustrating an example of a configuration of a linear object model generation system according to an embodiment. [Figure 2] 1 is a schematic diagram illustrating an example of a detection target by a linear object detection device according to an embodiment. [Figure 3] FIG. 1 is a schematic diagram illustrating an example of a hardware configuration of a linear object detection device according to an embodiment. [Figure 4] 1 is a block diagram illustrating an example of a functional configuration of a linear object detection device according to an embodiment. [Figure 5] FIG. 1 is a schematic diagram illustrating an example of a three-dimensional point cloud of a roadside tree having a missing portion according to an embodiment. [Figure 6] FIG. 10 is a diagram for explaining a process performed by an exclusion unit. [Figure 7] FIG. 10 is a diagram for explaining a process performed by an exclusion unit. [Figure 8] 10A and 10B are diagrams for explaining processing performed by a line detection unit. [Figure 9] 10A and 10B are diagrams for explaining processing performed by a line detection unit. [Figure 10] FIG. 10 is a diagram for explaining processing performed by a classification unit. [Figure 11] FIG. 10 is a diagram for explaining processing performed by a classification unit. [Figure 12] 10A and 10B are diagrams for explaining processing performed by a linear object model generating unit. [Figure 13] 10 is a flowchart illustrating an example of a linear object detection process in the linear object detection device according to the embodiment. [Figure 14A] FIG. 2 is a diagram illustrating an example of a linear object model generated by the linear object detection device according to the embodiment. [Figure 14B] 14B is a diagram showing an example of a three-dimensional point cloud used to generate the linear object model shown in FIG. 14A. FIG. DETAILED DESCRIPTION OF THE INVENTION

[0011] An example of an embodiment of the disclosed technology will be described below with reference to the drawings. Note that the same reference numerals are used to designate identical or equivalent components and parts in each drawing. Also, the dimensional proportions in the drawings are exaggerated for the sake of explanation and may differ from the actual proportions.

[0012] First, an example of the configuration of a linear object model generation system 1 according to the technology of this embodiment will be described. As shown in Fig. 1, the linear object model generation system 1 according to this embodiment includes a point cloud measurement instrument 20 and a linear object detection device 30. The point cloud measurement instrument 20 and the linear object detection device 30 are connected via a network 9 by wired communication or wireless communication.

[0013] The point cloud measuring device 20 includes a scanner 22, a storage medium 24, and a communication I / F (Interface) 26. The scanner 22 is a three-dimensional laser scanner that scans the surface of a structure with a laser to obtain three-dimensional (X, Y, Z) coordinates of points on the surface of the structure as point cloud data.

[0014] The point cloud data acquired by the scanner 22 is stored in a non-transitory storage medium, that is, a storage medium 24. Examples of the storage medium 24 include a USB (Universal Serial Bus) memory, an HDD (Hard Disk Drive), and an SSD (Solid State Drive).

[0015] The communication I / F 26 communicates various data such as point cloud data stored in the storage medium 24 with the linear object detection device 30 via the network 9 by wired communication or wireless communication.

[0016] For example, the scanner 22 of the point cloud measuring device 20 scans the surfaces of the utility poles 101-103, cables 121-124, and branch wire 13 shown in Fig. 2 with a laser to obtain point cloud data representing the three-dimensional coordinates of points on the surfaces of the utility poles 101-103, cables 121-124, and branch wire 13. Note that, hereinafter, when the utility poles 101-103 are referred to collectively without distinguishing between them, the reference numerals 1-3 used to distinguish between them will be omitted, and they will simply be referred to as utility pole 10. Similarly, when the cables 121-124 are referred to collectively without distinguishing between them, the reference numerals 1-4 used to distinguish between them will be omitted, and they will simply be referred to as cable 12. The scanner 22 of this embodiment is capable of measuring, for example, one rotation (360°) with the vertical direction, which is the Z-axis direction in Fig. 2, as the scan line.

[0017] The linear object detection device 30 is a device that extracts a three-dimensional point cloud that constitutes a linear object among structures from the point cloud data acquired by the scanner 22 of the point cloud measuring device 20 and stored in the storage medium 24, and generates a linear object model that represents the linear object.

[0018] The hardware configuration of the linear object detection device 30 of this embodiment will be described. As shown in Fig. 3, the linear object detection device 30 includes a CPU (Central Processing Unit) 31, a ROM (Read Only Memory) 32, a RAM (Random Access Memory) 33, a storage 34, a display unit 37, and a communication I / F 38. Each component is connected to each other so as to be able to communicate with each other via a bus 39 such as a system bus or a control bus.

[0019] The CPU 31 is a central processing unit, and executes various programs such as a linear object detection program 35 stored in a storage 34, and controls each part.

[0020] The ROM 32 stores various programs and various data to be executed by the CPU 31. The RAM 33 temporarily stores programs or data as a work area when the CPU 31 executes the various programs. That is, the CPU 31 reads a program from the storage 34 and executes the program using the RAM 33 as a work area.

[0021] The storage 34 of this embodiment stores a linear object detection program 35. The linear object detection program 35 may be a single program or a group of programs made up of multiple programs or modules. The storage 34 is configured with an HDD or SSD. The storage 34 also stores various programs including an operating system and various data (neither of which is shown). The storage 34 also stores a linear object model 36 generated by executing the linear object detection program 35.

[0022] The display unit 37 displays a linear object model and various information. The display unit 37 is not particularly limited, and various displays may be used.

[0023] The communication I / F 38 is an interface for communicating with the point cloud measuring instrument 20 via the network 9, and uses standards such as Ethernet (registered trademark), FDDI, and Wi-Fi (registered trademark).

[0024] Next, a description will be given of the functional configuration of the linear object detection device 30. As shown in Fig. 4, the linear object detection device 30 includes a reading unit 40, a parameter setting unit 42, a linear object model calculation unit 44, a storage control unit 46, and a display control unit 48. The CPU 31 executes the linear object detection program 35 stored in the storage 34, causing the CPU 31 to function as the reading unit 40, the parameter setting unit 42, the linear object model calculation unit 44, the storage control unit 46, and the display control unit 48.

[0025] The reading unit 40 reads the point cloud data stored in the storage medium 24 of the point cloud measuring device 20 via the network 9. The reading unit 40 outputs the read point cloud data to the linear object model calculation unit 44. Fig. 5 shows an example of a group of 3D points 14 read by the reading unit 40. The following describes, as an example, a case where the reading unit 40 reads the group of 3D points 14 shown in Fig. 5.

[0026] The parameter setting unit 42 stores various parameters used when the linear object model calculation unit 44 calculates a linear object model, and outputs the parameters to the linear object model calculation unit 44 .

[0027] The linear object model calculation unit 44 includes an exclusion unit 50 , a line detection unit 52 , a classification unit 54 , and a linear object model generation unit 56 .

[0028] The exclusion unit 50 excludes 3D points that can be considered to be points on the surface of a structure other than the linear object to be detected from the 3D point cloud represented by the point cloud data read by the reading unit 40 (hereinafter referred to as the 3D point cloud read by the reading unit 40). As an example, the linear object detection device 30 of this embodiment detects a cable 12 as a linear object. Therefore, the exclusion unit 50 excludes 3D points that can be considered to be points on the surface of the utility poles 101 to 103 from the 3D point cloud read by the reading unit 40.

[0029] Specifically, if the vertical length of a partial point group consisting of multiple three-dimensional points 14 arranged in a vertical direction (Z-axis direction in the figure) that intersects with a horizontal plane (corresponding to the XY plane in the figure) and whose spacing between the three-dimensional points 14 is less than a predetermined spacing is greater than a predetermined length, the exclusion unit 50 excludes the three-dimensional points included in the partial point group from the targets to be projected onto the horizontal plane.

[0030] The processing performed by the exclusion unit 50 will be described in detail with reference to FIGS. 6 and 7. First, as shown in FIG. 6, the exclusion unit 50 groups, from the acquired group of 3D points 14, 3D points 14 arranged in the vertical direction (the Z-axis direction in FIG. 6) whose spacing between the 3D points 14 is equal to or less than a predetermined spacing, to form partial point groups. In this embodiment, the scan line of the scanner 22 extends in the vertical direction. Therefore, this processing corresponds to grouping the 3D points 14 from the group of 3D points 14 according to the scan line. In the example shown in FIG. 6, two partial point groups 60 are formed from the group of 3D points 14 corresponding to utility pole 101, and two partial point groups 60 are formed from the group of 3D points 14 corresponding to utility pole 102. Moreover, seven partial point groups 60 are formed from the group of 3D points 14 corresponding to cable 121, seven partial point groups 60 are formed from the group of 3D points 14 corresponding to cable 122, and seven partial point groups 60 are formed from the group of 3D points 14 corresponding to cable 123. Furthermore, four partial point groups 60 are formed from the group of 3D points 14 corresponding to cable 124. Furthermore, five partial point groups 60 are formed from the group of 3D points 14 corresponding to branch line 13.

[0031] Next, the exclusion unit 50 detects, for each partial point group 60, whether the vertical length 62 of the partial point group is equal to or greater than a predetermined length. Generally, the vertical length 62 of a utility pole 10 is longer than that of a cable 12 or a branch wire 13. For this reason, a predetermined length is set as a threshold length for distinguishing between the cable 12 and the branch wire 13 and the utility pole 10. If the vertical length 62 of a partial point group 60 is equal to or greater than the predetermined length, the exclusion unit 50 can determine that the 3D points 14 included in the partial point group 60 are 3D points 14 corresponding to the utility pole 10, and therefore excludes these 3D points 14 from a group of 3D points 14 that are candidates for the cable 12 (hereinafter referred to as a candidate point group), and does not use these 3D points 14 in subsequent processing. In other words, if the vertical length 62 of the partial point group 60 is less than a predetermined length, the exclusion unit 50 considers the 3D points 14 included in the partial point group 60 to be 3D points 14 corresponding to the cable 12 or the branch line 13, and sets them as candidate points to be used in subsequent processing. The predetermined length used as a threshold here is set in the parameter setting unit 42.

[0032] In this way, by excluding 3D points 14 included in the partial point cloud 60 whose vertical length 62 is equal to or greater than a predetermined length, it is possible to exclude from the candidate point cloud not only utility poles 10 but also 3D points 14 on the surface of walls, the ground, etc. This reduces the processing load required for subsequent processing. Note that when 3D points 14 on the surface of walls, the ground, etc. are excluded from the candidate point cloud, the above-mentioned predetermined length may be set appropriately.

[0033] The exclusion unit 50 also excludes 3D points 14 that exist at or below a given height from the candidate point cloud. As described above, in this embodiment, the detection target is the cable 12, which is laid at a relatively high position (approximately 5 m or higher). Therefore, the exclusion unit 50 excludes 3D points 14 that exist at a low position, such as near the ground, from the candidate point cloud. Specifically, the exclusion unit 50 excludes 3D points 14 whose Z coordinate value is less than a threshold value from the candidate point cloud. This reduces the processing load required for subsequent processing and shortens the processing time. Note that even if the Z coordinate value of a 3D point 14 is the same, the height in real space of the 3D point 14 differs depending on the location of the scanner 22. For example, even if the Z coordinate value of the 3D point 14 is 0, the height in real space of the 3D point 14 will differ depending on whether the scanner 22 is located near the ground surface or on a tripod. Therefore, the given height used here varies depending on the vertical position of the scanner 22, i.e., the height.

[0034] In this way, the exclusion unit 50 excludes the 3D points 14 from the candidate point group, thereby narrowing the candidate point group from the group of 3D points 14 shown in Fig. 5 to the group of 3D points 14 shown in Fig. 7. The exclusion unit 50 outputs information about the candidate point group to the line detection unit 52.

[0035] The line detection unit 52 detects lines based on the three-dimensional coordinates of a group of 14 three-dimensional points that represent the three-dimensional coordinates of points on the surface of a structure, divides the group of 14 three-dimensional points related to the line according to the distance between the 3-dimensional points 14 related to one detected line, and again detects lines based on the three-dimensional coordinates for each group of 14 divided three-dimensional points.

[0036] Specifically, the line detection unit 52 detects a line based on three-dimensional coordinates, i.e., in three-dimensional space, from a group of candidate points, which are a group of three-dimensional points 14. The method by which the line detection unit 52 detects a line from a group of three-dimensional points 14 is not particularly limited, and for example, a known Hough transform or the like may be used. In this embodiment, as shown in FIG. 8 , a line 641 is detected from a group of three-dimensional points 14 corresponding to cable 121, and a line 642 is detected from a group of three-dimensional points 14 corresponding to cable 122. A line 643 is detected from a group of three-dimensional points 14 corresponding to cable 123, and a line 644 is detected from a group of three-dimensional points 14 corresponding to cable 124. Furthermore, a line 645 is detected from a group of three-dimensional points 14 corresponding to branch line 13. By performing this process, it is possible to separately detect a branch cable portion, such as cable 123 from cable 122.

[0037] Furthermore, the line detection unit 52 divides the group of 3D points 14 at a portion where the distance between the 3D points 14 along the detected line 64 is greater than or equal to a predetermined distance. When a cable 12 is the detection target structure and other structures, such as trees, are adjacent to the cable 12, a single line 64 may be detected from the group of 3D points 14 corresponding to the cable 12 and the group of 3D points 14 corresponding to the tree, as shown in FIG. 9 . The group of 3D points 14 corresponding to the cable 12 and the group of 3D points 14 corresponding to the tree are relatively far apart. Therefore, when the distance between the 3D points 14 is greater than or equal to a predetermined distance, the line detection unit 52 determines that the group of 3D points 14 originating from different structures corresponds to the single line 64, and divides the group of 3D points 14 along the portion where the points are separated, creating separate sets for each of the 3D points 14. The line detection unit 52 then performs line detection again for each of the divided 3D points 14, i.e., for each separate set. In the example shown in FIG. 9, a straight line 64A is detected from the group of 3D points 14 corresponding to the cable 12, and a straight line 64B is detected from the group of 3D points 14 corresponding to the trees.

[0038] The line detection unit 52 outputs information representing the detected lines 64 to the classification unit 54 .

[0039] The classification unit 54 groups the detected multiple straight lines 64 based on the angles between the detected multiple straight lines 64 and the ground and the orientation of the straight lines 64, and for each straight line 64 included in the same group, classifies the straight lines 64 such that the angle between the line segment connecting the straight lines 64 and the straight lines 64 connected by the line segment is within a threshold value as straight lines 64 corresponding to the same structure.

[0040] Specifically, the line detection unit 52 first calculates the angle between the line 64 and the ground (ground surface) based on the three-dimensional coordinates of the group of three-dimensional points 14 corresponding to the line 64. The line detection unit 52 then classifies the line 64 based on the calculated angle and the range of angles corresponding to the type of structure. For example, the cable 12 is laid out relatively parallel to the ground surface. Therefore, the angle between the line 64 detected by the group of three-dimensional points 14 corresponding to the cable 12 and the ground is relatively small. Furthermore, a recommended installation angle for the branch wire 13 is 25 degrees to 45 degrees. Therefore, the angle between the line 64 detected by the group of three-dimensional points 14 corresponding to the branch wire 13 and the ground tends to be approximately 45 degrees to 65 degrees. Furthermore, the angle between the line 64 detected by the group of three-dimensional points 14 corresponding to other structures such as trees and utility poles 10 and the ground is relatively close to perpendicular. Therefore, for example, if the angle between the line 64 and the ground is less than 45 degrees, the line 64 is classified as a line 64 detected from a group of 3D points 14 corresponding to the cable 12. If the angle between the line 64 and the ground is equal to or greater than 45 degrees but less than 65 degrees, the line 64 is classified as a line 64 detected from a group of 3D points 14 corresponding to the branch wire 13. If the angle between the line 64 and the ground is equal to or greater than 65 degrees, the line 64 is classified as a line 64 detected from a group of 3D points 14 corresponding to other structures. For example, in the example shown in FIG. 8 , the lines 641 to 644 are classified as those corresponding to the cable 12. The line 645 is classified as those corresponding to the branch wire 13.

[0041] In addition, the classification unit 54 groups the detected lines based on the angles between the lines and the ground and the surrounding area of ​​the lines, and classifies lines included in the same group as lines corresponding to the same structure if the angle between the line segment connecting the lines and the line connected by the line segment is within a threshold value.

[0042] Specifically, the classification unit 54 derives the direction (angle) in which each straight line 64 extends for each of the above classifications. Then, the classification unit 54 groups the straight lines 64 such that the difference in the derived angles is equal to or less than a threshold value. FIG. 10 shows six straight lines 64 (64) classified into the straight lines 64 detected from the group of three-dimensional points 14 corresponding to the cable 12. 11 , 64 12 , 64 13 , 64 14 , 64 21 , and 64 22 ) is grouped by the classification unit 54. 11 ~64 14 Since the difference in angle between the straight lines 64 and 65 is equal to or less than the threshold, they are grouped into the same group 701. 21 , 64 22 Since the difference in angle between the two is equal to or less than the threshold, they are grouped into the same group 702.

[0043] Furthermore, for each group 70, the classification unit 54 calculates the center of gravity of each straight line 64 included in the group 70 from the corresponding group of 3D points 14, and sets the center of gravity as the center of gravity of each straight line 64. For example, as shown in FIG. 11, the classification unit 54 calculates the center of gravity of the straight line 64 in the group 701. 11 Center of gravity 801, line 64 12 Center of gravity 802, line 64 13 The center of gravity 803 and the line 64 14 The center of gravity 804 is calculated.

[0044] Furthermore, the classification unit 54 calculates a line segment (directional vector) connecting the calculated centers of gravity 80. For example, as shown in FIG. 11 For the center of gravity 801, the line 64 12 The line segment 822 and the line 6413 The line segment 823 connecting the center of gravity 803 and the line 64 14 A line segment 824 is calculated by connecting the center of gravity 80 and the center of gravity 804. It is preferable that the center of gravity 80 uses the average value of the coordinates of the three-dimensional point 14.

[0045] Furthermore, the classification unit 54 calculates the angle between the line 64 and the line segment 82. In the example shown in FIG. 11 and the angle between the line segment 822 and the line 64 12 and the angle between the line segment 822 and the line 64 11 and the angle between the line segment 823 and the line 64 13 and the angle between the line segment 823 and the line 64 11 and the angle between the line segment 824 and the line 64 14 The angle formed by the line segment 822 and the line segment 824 is calculated. The classification unit 54 then classifies the lines 64 whose calculated angle with the line segment 82 is within a threshold value as lines corresponding to the same structure, into the same group. In the example shown in FIG. 11, 11 The angle between the line segment 822 and the line 64 12 Since the angle between 11 and straight line 64 12 is classified into group 861. Also, line segment 823 and line 64 13 The angle between the line segment 824 and the line 64 14 The angle between and exceeds the threshold, so the line 64 11 And straight line 64 13 and Line 64 14 In addition, in the same manner as above, the angle between the line 64 and the line segment 82 is calculated, and the line 64 is classified into a different group. 13 and straight line 64 14 It is classified as Group 862.

[0046] The classification unit 54 outputs information on the group 86 as the classification result to the linear object model generation unit 56 .

[0047] The linear object model generation unit 56 generates an approximation curve from a group of three-dimensional points corresponding to each straight line classified as a straight line corresponding to the same structure, sets multiple planes perpendicular to the approximation curve at regular intervals, and generates a linear object model representing the linear object based on the radius and center coordinates of the circle obtained by circular fitting the group of three-dimensional points existing on the plane.

[0048] Specifically, the linear object model generation unit 56 performs catenary curve approximation for each group 86 using the three-dimensional coordinates of each of the 14 three-dimensional points included in the group 86, and generates a catenary curve 90, as in the example shown in Figure 12.

[0049] The linear object model generation unit 56 also sets planes 92 perpendicular to the catenary curve 90 at regular intervals, as shown in Fig. 12. Furthermore, the linear object model generation unit 56 performs circle fitting on the group of 14 3D points existing on the generated planes 92, as shown in Fig. 12, and derives the radius and center coordinates of the circle. A known method such as RANSAC can be used as the circle fitting method. This makes it possible to derive the radius of the linear model.

[0050] The linear object model generating unit 56 outputs the catenary curve 90, the radius obtained by circle fitting, and the center coordinates to the storage control unit 46 as information representing the generated linear object model 36.

[0051] The storage control unit 46 stores the generated linear object model 36 in the storage 34. The display control unit 48 causes the display unit 37 to display the linear object model 36.

[0052] Next, the operation of the linear object detection device 30 will be described.

[0053] Fig. 13 shows a flowchart of an example of linear object detection processing executed by the linear object detection device 30 of this embodiment. The linear object detection device 30 executes a linear object detection program 35 stored in the storage 34 to execute the linear object detection processing shown in Fig. 16. Note that the linear object detection processing shown in Fig. 16 is executed at a predetermined timing, such as when an execution instruction is received from a user.

[0054] In step S100 of FIG. 16, the reading unit 40 reads the point cloud data stored in the storage medium 24 of the point cloud measuring instrument 20 via the network 9, as described above.

[0055] In the next step S102, the exclusion unit 50 groups, as described above, the multiple 3D points 14 arranged in the vertical direction into a partial point group consisting of 3D points 14 whose spacing between each other is less than a predetermined spacing (see Figure 6).

[0056] In the next step S104, the exclusion unit 50 excludes from the candidate point group the 3D points 14 included in the partial point group whose vertical length 62 is equal to or greater than a predetermined length, as described above (see FIGS. 6 and 7).

[0057] In the next step S106, the exclusion unit 50 excludes the 3D points 14 that exist at or below an arbitrary height from the candidate point group, as described above.

[0058] In the next step S108, the line detection unit 52 detects a line 64 based on the three-dimensional coordinates of the group of three-dimensional points 14 representing the three-dimensional coordinates of points on the surface of the structure, as described above (see FIG. 8).

[0059] In the next step S110, the line detection unit 52 divides the group of 3D points 14 related to one line 64 according to the distance between the 3D points 14 related to one line 64, as described above (see FIG. 9).

[0060] In the next step S112, the line detection unit 52 detects the line 64 again based on the three-dimensional coordinates for each of the divided groups of three-dimensional points 14, as described above (see FIG. 9).

[0061] In the next step S114, the classification unit 54 derives the angles formed between the detected straight lines 64 and the ground, as described above.

[0062] In the next step S116, the classification unit 54 classifies the straight line 64 based on the angle derived in step S114, as described above (see FIG. 10).

[0063] In the next step S118, the classification unit 54 derives the orientation (angle) in which each straight line 64 extends for each classification, as described above.

[0064] In the next step S120, the classification unit 54 performs grouping so that the straight lines 64 whose angle difference derived in step S118 is equal to or smaller than the threshold value are grouped together (see FIG. 10), as described above.

[0065] In the next step S122, the classification unit 54 calculates, for each group 70, the center of gravity 80 from the corresponding group of 3D points 14 for each straight line 64 included in the group 70, as described above (see FIG. 11).

[0066] In the next step S124, the classification unit 54 calculates the line segments 82 connecting the centers of gravity 80 as described above (see FIG. 11).

[0067] In the next step S126, the classification unit 54 calculates the angle formed between the line segment 82 and the straight line 64 as described above (see FIG. 11).

[0068] In the next step S128, the classification unit 54 groups the straight lines 64 whose angles are within the threshold value calculated in step S126 into the same group (see FIG. 11), as described above.

[0069] In the next step S130, the linear object model generating unit 56 generates the catenary curve 90 as described above (see FIG. 12).

[0070] In the next step S132, the linear object model generating unit 56 provides planes 92 perpendicular to the catenary curve 90 at regular intervals, as described above (see FIG. 12).

[0071] In the next step S134, the linear object model generating unit 56 performs circle fitting on the group of 3D points 14 existing on the plane 92, as described above, and derives the radius and center coordinates of the circle (see FIG. 12).

[0072] In the next step S136, the storage control unit 46 stores the linear object model 36 in the storage 34 as described above.

[0073] In the next step S138, the display control unit 48 displays the linear object model 36 on the display unit 37, as described above. When the processing of step S136 ends, the linear object detection processing shown in FIG. 13 ends. FIG. 14A shows an example of the linear object model 36 generated by the linear object detection device 30 of this embodiment. Also, FIG. 14B shows an example of a group of 3D points 14 used to generate the linear object model 36 shown in FIG. 14A. It can be seen from FIG. 14A that the linear object detection device 30 of this embodiment can detect linear objects with high accuracy.

[0074] As described above, the linear object detection device 30 of this embodiment detects straight lines 64 based on the three-dimensional coordinates of a group of three-dimensional points 14 that represent the three-dimensional coordinates of points on the surface of a structure, divides the group of three-dimensional points 14 related to the detected straight line 64 according to the distance between the three-dimensional points 14 related to one detected straight line 64, and detects a straight line 64 again based on the three-dimensional coordinates for each divided group of three-dimensional points 14. The linear object detection device 30 groups the detected multiple straight lines 64 based on the angles between the detected multiple straight lines 64 and the ground and the orientations of the straight lines 64, and classifies the straight lines 64 included in the same group as those corresponding to the same structure if the angle between the line segment 82 connecting the straight lines 64 and the line 64 connected by the line segment 82 is within a threshold value.

[0075] As described above, according to the linear object detection device 30 of this embodiment, linear objects are detected, so that linear objects can be detected with high accuracy even if the three-dimensional point cloud representing the three-dimensional coordinates is high density.

[0076] Furthermore, the various processes executed by the CPU after reading software (programs) in the above embodiments may be executed by various processors other than the CPU. Examples of such processors include programmable logic devices (PLDs) such as field-programmable gate arrays (FPGAs), whose circuit configuration can be changed after manufacture, and dedicated electrical circuits such as application-specific integrated circuits (ASICs), which are processors having circuit configurations specifically designed to execute specific processes. The position estimation process may be executed by one of these various processors, or by a combination of two or more processors of the same or different types (e.g., multiple FPGAs, or a combination of a CPU and an FPGA). The hardware structure of these various processors is, more specifically, an electrical circuit that combines circuit elements such as semiconductor devices.

[0077] Furthermore, in each of the above embodiments, the linear object detection program 35 is described as being pre-stored (installed) in the storage 34, but this is not limiting. The linear object detection program 35 may be provided in a form stored in a non-transitory storage medium such as a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital Versatile Disk Read Only Memory), or a USB (Universal Serial Bus) memory. Furthermore, the linear object detection program 35 may be downloaded from an external device via a network.

[0078] The following additional notes are provided regarding the above-described embodiments.

[0079] (Additional note 1) Memory and at least one processor coupled to said memory; Including, The processor: For a three-dimensional point cloud representing the three-dimensional coordinates of points on the surface of a structure, a straight line is detected based on the three-dimensional coordinates, the three-dimensional point cloud related to the straight line is divided according to the distance between three-dimensional points related to one of the detected three-dimensional points, and a straight line is again detected for each of the divided three-dimensional point clouds based on the three-dimensional coordinates; The detected multiple straight lines are grouped based on the angles between the lines and the ground and the orientations of the lines, and for each of the straight lines included in the same group, the straight lines are classified as straight lines corresponding to the same structure if the angle between the line segment connecting the straight lines and the line connected by the line segment is within a threshold value. A linear object detection device configured as follows.

[0080] (Additional note 2) A non-transitory storage medium storing a program executable by a computer to perform linear object detection processing, The linear object detection process includes: For a three-dimensional point cloud representing the three-dimensional coordinates of points on the surface of a structure, a straight line is detected based on the three-dimensional coordinates, the three-dimensional point cloud related to the straight line is divided according to the distance between three-dimensional points related to one of the detected three-dimensional points, and a straight line is again detected for each of the divided three-dimensional point clouds based on the three-dimensional coordinates; The detected multiple straight lines are grouped based on the angles between the lines and the ground and the orientations of the lines, and for each of the straight lines included in the same group, the straight lines are classified as straight lines corresponding to the same structure if the angle between the line segment connecting the straight lines and the line connected by the line segment is within a threshold value. Non-transitory storage medium. [Explanation of symbols]

[0081] 20 point cloud measuring device 22 Scanner 30 Linear object detection device 31 CPU 32 ROM 33 RAM 34 Storage 35 Linear object detection program 36 Linear object model 37 Display section 38 Communication I / F 39 Bus 40 Reading section 42 Parameter setting section 44 Linear object model calculation unit 46 Storage control section 48 Display control unit 50 Classification Department 52 Line detector 54 Classification Department 56 Linear object model generation unit

Claims

1. a line detection unit that detects a line based on the three-dimensional coordinates of a three-dimensional point cloud representing the three-dimensional coordinates of points on the surface of a structure, divides the three-dimensional point cloud related to the line according to the distance between three-dimensional points related to one detected line, and again detects a line based on the three-dimensional coordinates for each divided three-dimensional point cloud; a classification unit that groups the detected multiple straight lines based on the angles between the detected multiple straight lines and the ground and the orientations of the detected multiple straight lines, and classifies, for each straight line included in the same group, straight lines whose angle between a line segment connecting the straight lines and a straight line connected by the line segment is within a threshold value as straight lines corresponding to the same structure; Equipped with The line segments connecting the straight lines are line segments connecting the centers of gravity of the three-dimensional point groups corresponding to the straight lines. Linear object detection device.

2. The system further includes a linear object model generation unit that generates an approximation curve from the three-dimensional point cloud corresponding to each straight line classified as a straight line corresponding to the same structure, sets a plurality of planes perpendicular to the approximation curve at regular intervals, and generates a linear object model representing a linear object based on the radius and center coordinates of a circle obtained by circular fitting the three-dimensional point cloud present on the planes. The linear object detection device according to claim 1 .

3. the classification unit classifies the plurality of straight lines based on angles formed between the plurality of straight lines and the ground and a range of angles formed according to the type of the structure, and groups the straight lines belonging to the same classification based on whether or not the orientations of the respective straight lines belonging to the same type can be considered to be the same; The linear object detection device according to claim 1 .

4. a line detection unit that detects a line based on the three-dimensional coordinates for a three-dimensional point cloud representing the three-dimensional coordinates of points on the surface of the structure, divides the three-dimensional point cloud related to the line in accordance with the distance between three-dimensional points related to one detected line, and again detects a line based on the three-dimensional coordinates for each divided three-dimensional point cloud; a classification unit that groups the detected multiple straight lines based on angles between the detected multiple straight lines and the ground and orientations of the detected multiple straight lines, and classifies, for each straight line included in the same group, straight lines whose angle between a line segment connecting the straight lines and a straight line connected by the line segment is within a threshold value as straight lines corresponding to the same structure; The line segments connecting the straight lines are line segments connecting the centers of gravity of the three-dimensional point groups corresponding to the straight lines. Linear object detection method.

5. A linear object detection program for causing a computer to function as each part of the linear object detection device according to claim 1.

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