WO2024023950A1 - Linear object detection device, linear object detection method, and linear object detection program - Google Patents

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

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Publication number
WO2024023950A1
WO2024023950A1 PCT/JP2022/028848 JP2022028848W WO2024023950A1 WO 2024023950 A1 WO2024023950 A1 WO 2024023950A1 JP 2022028848 W JP2022028848 W JP 2022028848W WO 2024023950 A1 WO2024023950 A1 WO 2024023950A1
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WIPO (PCT)
Prior art keywords
straight lines
straight line
linear object
dimensional
straight
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PCT/JP2022/028848
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French (fr)
Japanese (ja)
Inventor
幸弘 五藤
雄介 櫻原
正樹 和氣
崇 海老根
Original Assignee
日本電信電話株式会社
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Application filed by 日本電信電話株式会社 filed Critical 日本電信電話株式会社
Priority to PCT/JP2022/028848 priority Critical patent/WO2024023950A1/en
Publication of WO2024023950A1 publication Critical patent/WO2024023950A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures

Definitions

  • the disclosed technology relates to a linear object detection device, a linear object detection method, and a linear object detection program.
  • MMS Mobile Mapping System
  • the disclosed technology has been made in view of the above points, and provides a linear object detection device that can detect linear objects with high accuracy even if the three-dimensional point group representing three-dimensional coordinates is highly dense. , a linear object detection method, and a linear object detection program.
  • a first aspect of the present disclosure is a linear object detection device that detects a straight line based on the three-dimensional coordinates of a three-dimensional point group representing the three-dimensional coordinates of a point on the surface of a structure.
  • a straight line detection unit that divides the three-dimensional point group related to the straight line according to the distance between the three-dimensional points related to the straight line, and detects the straight line again based on the three-dimensional coordinates for each divided three-dimensional point group; , the straight lines are grouped based on the angles formed between the plurality of detected straight lines and the ground, and the orientation of the straight lines, and for each straight line included in the same group, a line segment connecting the straight lines, and a line segment connecting the straight lines, The method further includes a classification unit that classifies the straight lines whose angles with the connected straight lines are within a threshold value as straight lines corresponding to the same structure.
  • a second aspect of the present disclosure is a linear object detection method, in which the straight line detection unit detects a straight line based on the three-dimensional coordinates of a three-dimensional point group representing the three-dimensional coordinates of points on the surface of the structure. Then, the 3D point group related to the straight line is divided according to the distance between the detected 3D points related to the one straight line, and the straight line is detected again based on the 3D coordinates for each divided 3D point group. Then, the classification unit groups the straight lines based on the angles formed between the plurality of detected straight lines and the ground and the orientation of the straight lines, and classifies each straight line included in the same group into a line segment connecting the straight lines. , is a linear object detection method in which straight lines whose angles with straight lines connected by the line segments are within a threshold value are classified as straight lines corresponding to the same structure.
  • a third aspect of the present disclosure is a linear object detection program, which is a program for causing a computer to function as each part of the linear object detection device of the first aspect.
  • FIG. 1 is a configuration diagram showing an example of the configuration of a linear object model generation system according to an embodiment. It is a schematic diagram showing an example of a detection target by a linear object detection device of an embodiment.
  • FIG. 1 is a schematic diagram showing an example of a hardware configuration of a linear object detection device according to an embodiment. It is a block diagram showing an example of the functional composition of the linear object detection device of an embodiment. It is a schematic diagram which shows an example of the three-dimensional point group of the street tree which concerns on embodiment and is partially missing.
  • FIG. 3 is a diagram for explaining processing performed by an exclusion unit.
  • FIG. 3 is a diagram for explaining processing performed by an exclusion unit.
  • FIG. 3 is a diagram for explaining processing performed by a straight line detection section.
  • FIG. 1 is a configuration diagram showing an example of the configuration of a linear object model generation system according to an embodiment. It is a schematic diagram showing an example of a detection target by a linear object detection device of an embodiment.
  • FIG. 1 is
  • FIG. 3 is a diagram for explaining processing performed by a straight line detection section.
  • FIG. 3 is a diagram for explaining processing performed by a classification section.
  • FIG. 3 is a diagram for explaining processing performed by a classification section.
  • FIG. 3 is a diagram for explaining processing performed by a linear object model generation unit. It is a flow chart which shows an example of linear object detection processing in a linear object detection device of an embodiment. It is a figure showing an example of the linear object model generated by the linear object detection device of an embodiment.
  • 14A is a diagram showing an example of a three-dimensional point group used to generate the linear object model shown in FIG. 14A.
  • FIG. 14A is a diagram showing an example of a three-dimensional point group used to generate the linear object model shown in FIG. 14A.
  • the linear object model generation system 1 of this embodiment includes a point cloud measuring device 20 and a linear object detection device 30.
  • the point cloud measuring device 20 and the linear object detection device 30 are connected via a network 9 by wired or wireless communication.
  • 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, and acquires three-dimensional (X, Y, Z) coordinates of points on the surface of the structure as point group data by scanning the surface of the structure with a laser.
  • the point cloud data acquired by the scanner 22 is stored in a storage medium 24 that is a non-temporary storage medium.
  • Examples of the storage medium 24 include a USB (Universal Serial Bus) memory, an HDD (Hard Disk Drive), and an SSD (Solid State Drive).
  • the communication I/F 26 communicates various data such as point cloud data stored in the storage medium 24 to the linear object detection device 30 via the network 9 by wired or wireless communication.
  • the scanner 22 of the point cloud measuring device 20 scans the surfaces of the utility poles 10 1 to 10 3 , cables 12 1 to 12 4 , and branch lines 13 shown in FIG. , the cables 12 1 to 12 4 , and point cloud data representing the three-dimensional coordinates of points on the surface of the branch line 13 are obtained.
  • the utility poles 10 1 to 10 3 are collectively referred to without distinguishing them individually, the reference numerals 1 to 3 used to distinguish them from each other will be omitted, and they will simply be referred to as the utility pole 10.
  • 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 a scan line.
  • the linear object detection device 30 extracts a three-dimensional point group constituting a linear object of the structure from point cloud data acquired by the scanner 22 of the point cloud measuring device 20 and stored in the storage medium 24. This is a device that generates a linear object model representing the linear object.
  • the linear detection device 30 is a CPU (Central Processing Unit) 31, ROM (READ ONLY MEMORY) 32, RAM (RANDOM Access Memory) 33, Storage 37, Display 37, and Display 37.
  • Each component is communicably connected to each other via a bus 39 such as a system bus or a control bus.
  • the CPU 31 is a central processing unit, and executes various programs such as the linear object detection program 35 stored in the storage 34 and controls each section.
  • the ROM 32 stores various programs and data executed by the CPU 31. Further, the RAM 33 temporarily stores programs or data as a work area when the CPU 31 executes various programs. That is, the CPU 31 reads the program from the storage 34 and executes the program using the RAM 33 as a work area.
  • a linear object detection program 35 is stored in the storage 34 of this embodiment.
  • the linear object detection program 35 may be one program, or may be a program group composed of a plurality of programs or modules.
  • the storage 34 is configured by an HDD or an SSD.
  • the storage 34 also stores various programs including an operating system and various data (all not shown).
  • a linear object model 36 generated by executing a linear object detection program 35 is stored in the storage 34 .
  • the display unit 37 displays a linear object model and various information.
  • the display section 37 is not particularly limited, and various types of displays may be used.
  • the communication I/F 38 is an interface for communicating with the point cloud measuring device 20 via the network 9, and uses standards such as Ethernet (registered trademark), FDDI, and Wi-Fi (registered trademark), for example.
  • the linear object detection device 30 includes a reading section 40, a parameter setting section 42, a linear object model calculation section 44, a storage control section 46, and a display control section 48.
  • the CPU 31 executes the linear object detection program 35 stored in the storage 34, the CPU 31 executes the reading section 40, the parameter setting section 42, the linear object model calculation section 44, the storage control section 46, and the display control section. Functions as 48.
  • the reading unit 40 reads 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 14 three-dimensional points read by the reading unit 40. Below, as an example, a case where the reading unit 40 reads the group of 14 three-dimensional points shown in FIG. 5 will be described.
  • the parameter setting unit 42 stores various parameters used when calculating a linear object model in the linear object model calculation unit 44, and outputs them to the linear object model calculation unit 44.
  • the linear object model calculation section 44 includes an exclusion section 50, a straight line detection section 52, a classification section 54, and a linear object model generation section 56.
  • the exclusion unit 50 extracts surfaces of structures other than linear objects to be detected from the three-dimensional point cloud represented by the point cloud data read by the reading unit 40 (hereinafter referred to as the three-dimensional point group read by the reading unit 40). Exclude three-dimensional points that can be considered as points above.
  • the linear object detection device 30 of this embodiment detects the cable 12 as the linear object. Therefore, the exclusion unit 50 excludes three-dimensional points that can be regarded as points on the surfaces of the utility poles 10 1 to 10 3 from the three-dimensional point group read by the reading unit 40 .
  • the exclusion unit 50 calculates the distance between the three-dimensional points 14 among the plurality of three-dimensional points 14 arranged in the vertical direction (Z-axis direction in the figure) intersecting the horizontal plane (corresponding to the XY plane in the figure). If the vertical length of a partial point group consisting of three-dimensional points 14 with a predetermined interval or less is greater than or equal to a predetermined length, the three-dimensional points included in the partial point group are excluded from being projected onto a horizontal plane. do.
  • the exclusion unit 50 selects three-dimensional points 14 from among a plurality of three-dimensional points 14 lined up in the vertical direction (Z-axis direction in FIG. 6) from the acquired group of three-dimensional points 14.
  • the three-dimensional points 14 whose intervals are equal to or less than a predetermined interval are grouped to form a partial point group.
  • the scan line of the scanner 22 extends in the vertical direction. Therefore, this processing corresponds to grouping the three-dimensional points 14 from the group of three-dimensional points 14 according to the scan line.
  • FIG. 6 the example shown in FIG.
  • two partial point groups 60 are formed from the 3-dimensional point 14 group corresponding to the utility pole 10 1
  • two partial point groups 60 are formed from the 3-dimensional point 14 group corresponding to the utility pole 10 2
  • a group 60 is formed.
  • seven partial point groups 60 are formed from the three -dimensional point group 14 corresponding to the cable 121
  • seven partial point groups 60 are formed from the three-dimensional point group 14 corresponding to the cable 122.
  • Seven partial point groups 60 are formed from the three-dimensional point group 14 corresponding to the cable 123 .
  • four partial point groups 60 are formed from the three-dimensional point group 14 corresponding to the cable 124 .
  • five partial point groups 60 are formed from the three-dimensional point group 14 corresponding to the branch line 13.
  • 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.
  • the vertical length 62 of the utility pole 10 is longer than that of the cable 12 or the branch line 13. Therefore, a length that is a threshold value for distinguishing between the cable 12 and the branch line 13 and the utility pole 10 is determined in advance as a predetermined length. If the vertical length 62 of the partial point group 60 is greater than or equal to a predetermined length, the exclusion unit 50 determines that the three-dimensional point 14 included in the partial point group 60 is the three-dimensional point 14 corresponding to the utility pole 10.
  • these three-dimensional points 14 are excluded from the group of three-dimensional points 14 that are candidates for the cable 12 (hereinafter referred to as candidate point group), and are not used in subsequent processing.
  • the exclusion unit 50 excludes the three-dimensional point 14 included in the partial point group 60 from the three-dimensional point corresponding to the cable 12. It is assumed that the three-dimensional point 14 corresponds to the point 14 or the branch line 13, and is used as a candidate point group for subsequent processing.
  • the predetermined length used as the threshold is set in the parameter setting section 42.
  • the above-mentioned predetermined length may be set as appropriate.
  • the exclusion unit 50 further excludes three-dimensional points 14 that exist below an arbitrary height from the candidate point group.
  • the cable 12 is the detection target, and the cable 12 is laid at a relatively high position (about 5 m or more). Therefore, the exclusion unit 50 excludes three-dimensional points 14 located at low positions, such as near the ground, from the candidate point group. Specifically, the exclusion unit 50 excludes three-dimensional points 14 whose Z coordinate value is less than a threshold value from the candidate point group. Thereby, the processing load on subsequent processing can be reduced, and the processing time can be shortened. Note that even if the Z coordinate value of the three-dimensional point 14 is the same, the height in real space differs depending on the position where the scanner 22 is provided.
  • the scanner 22 is located near the ground surface or if it is installed on a tripod, even if the Z coordinate value of the three-dimensional point 14 is 0, the height of the three-dimensional point 14 in real space will be The result will be different. Therefore, the arbitrary height used here changes depending on the vertical position where the scanner 22 is provided, that is, the height.
  • the exclusion unit 50 narrows down the candidate point group from the three-dimensional point group 14 shown in FIG. 5 to the three-dimensional point 14 group shown in FIG. It will be done.
  • the exclusion unit 50 outputs information about the candidate point group to the straight line detection unit 52.
  • the straight line detection unit 52 detects straight lines based on the three-dimensional coordinates of a group of 14 three-dimensional points representing the three-dimensional coordinates of points on the surface of the structure, and calculates the relationship between the three-dimensional points 14 related to one detected straight line.
  • the 14 groups of 3-dimensional points related to the straight line are divided according to distance, and the straight line is detected again based on the 3-dimensional coordinates for each group of 14 divided 3-dimensional points.
  • the straight line detection unit 52 detects a straight line from a group of candidate points, which is a group of 14 three-dimensional points, based on three-dimensional coordinates, that is, in a three-dimensional space.
  • the method by which the straight line detection unit 52 detects a straight line from the group of 14 three-dimensional points is not particularly limited, and for example, a known Hough transformation or the like may be used.
  • a straight line 64 1 is detected from the group of 14 three-dimensional points corresponding to the cable 12 1
  • a straight line 64 2 is detected from the group of 14 three-dimensional points corresponding to the cable 12 2 . is detected.
  • a straight line 64 3 is detected from the group of 14 three-dimensional points corresponding to the cable 12 3
  • a straight line 64 4 is detected from the group of 14 three-dimensional points corresponding to the cable 12 4
  • a straight line 645 is detected from the group of three-dimensional points 14 corresponding to the branch line 13.
  • the straight line detection unit 52 detects that the three-dimensional points 14 are Divide the group.
  • the cable 12 is a structure to be detected and there is another structure adjacent to it, such as a tree, as shown in FIG.
  • One straight line 64 may be detected from the group of 14 dimensional points.
  • the group of 14 three-dimensional points corresponding to the cable 12 and the group of 14 three-dimensional points corresponding to the tree are relatively spaced apart.
  • the straight line detection unit 52 detects that a group of three-dimensional points 14 originating from different structures correspond to one straight line 64.
  • the 14 groups of three-dimensional points are divided into separate sets based on the distance between them. Then, the straight line detection unit 52 performs straight line detection again for each divided three-dimensional point 14, that is, for each different set.
  • a straight line 64A is detected by a group of 14 three-dimensional points corresponding to the cable 12, and a straight line 64B is detected by a group of 14 three-dimensional points corresponding to a tree.
  • the straight line detection unit 52 outputs information representing the plurality of detected straight lines 64 to the classification unit 54.
  • the classification unit 54 groups the straight lines 64 based on the angles formed between the plurality of detected straight lines 64 and the ground, and the direction of the straight lines 64, and classifies lines 64 that connect the straight lines 64 for each straight line 64 included in the same group.
  • Straight lines 64 in which the angle between the line segment and the straight line 64 connected by the line segment are within a threshold value are classified as straight lines 64 corresponding to the same structure.
  • the straight line detection unit 52 first calculates the angle between the straight line 64 and the ground (ground surface) based on the three-dimensional coordinates of the group of 14 three-dimensional points corresponding to the straight line 64.
  • the straight line detection unit 52 then classifies the straight line 64 based on the calculated angle and the range of the angle depending on the type of structure. For example, the cable 12 is laid relatively parallel to the ground surface. Therefore, the angle between the straight line 64 detected by the group of 14 three-dimensional points corresponding to the cable 12 and the ground becomes relatively small. Further, it is recommended that the branch line 13 be installed at an angle of 25 degrees to 45 degrees.
  • the angle between the straight line 64 detected by the group of 14 three-dimensional points corresponding to the branch line 13 and the ground tends to be around 45 degrees to 65 degrees. Furthermore, the angle between the straight line 64 detected by the group of 14 three-dimensional points 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 straight line 64 and the ground is less than 45 degrees, the straight line 64 is classified as the straight line 64 detected by the group of three-dimensional points 14 corresponding to the cable 12. Further, when the angle between the straight line 64 and the ground is 45 degrees or more and less than 65 degrees, the straight line 64 is classified as a straight line 64 detected by the group of three-dimensional points 14 corresponding to the branch line 13.
  • the straight line 64 is classified as a straight line 64 detected by a group of 14 three-dimensional points corresponding to other structures. For example, in the example shown in FIG. 8, straight lines 64 1 to 64 4 are classified as corresponding to the cable 12. Further, the straight line 645 is classified as corresponding to the branch line 13.
  • the classification unit 54 groups the straight lines based on the angles formed between the plurality of detected straight lines and the ground, and the encirclement of the straight lines, and for each straight line included in the same group, segments connecting the straight lines, Straight lines whose angles with straight lines connected by line segments are within a threshold are classified as straight lines corresponding to the same structure.
  • the classification unit 54 derives the direction (angle) in which each straight line 64 extends for each of the above classifications. Then, grouping is performed such that straight lines 64 for which the difference between the derived angles is equal to or less than a threshold value are in the same group.
  • a threshold value a threshold value
  • FIG. 10 six straight lines 64 (64 11 , 64 12 , 64 13 , 64 14 , 64 21 , and 64 22 ) classified into straight lines 64 detected by a group of 14 three-dimensional points corresponding to the cable 12 are shown .
  • An example of grouping by the classification unit 54 is shown.
  • the straight lines 64 11 to 64 14 are grouped into the same group 70 1 because the difference in angle is less than the threshold value.
  • the straight lines 64 21 and 64 22 are grouped into the same group 70 2 .
  • the classification unit 54 calculates the center of gravity of each straight line 64 included in the group 70 from the corresponding group of three-dimensional points 14, and sets the calculated center of gravity of each straight line 64. For example, as shown in FIG. 11, in the group 70 1 , the classification unit 54 determines the center of gravity 80 1 of the straight line 64 11 , the center of gravity 80 2 of the straight line 64 12 , the center of gravity 80 3 of the straight line 64 13 , and the center of gravity of the straight line 64 14 . Calculate 80 4 .
  • the classification unit 54 calculates a line segment (direction vector) connecting the calculated centers of gravity 80. For example, as shown in FIG. 11, regarding the center of gravity 80 1 of the straight line 64 11 , a line segment 82 2 connecting the center of gravity 80 2 of the straight line 64 12 and a line segment 82 3 connecting the center of gravity 80 3 of the straight line 64 13 , and the center of gravity 804 of the straight line 6414 , a line segment 824 is calculated. Note that, as the center of gravity 80, it is preferable to use the average value of the coordinates of the three-dimensional points 14.
  • the classification unit 54 calculates the angle between the straight line 64 and the line segment 82.
  • the angle between the line 64 11 and the line segment 82 3 , the angle between the straight line 64 11 and the line segment 82 4 , and the angle between the straight line 64 14 and the line segment 82 4 are calculated.
  • the classification unit 54 regards the straight lines 64 whose angles with the calculated line segment 82 are within the threshold value as straight lines corresponding to the same structure, and classifies them into the same group.
  • the angle between the line segment 82 2 and the straight line 64 11 and the angle between the line segment 82 2 and the straight line 64 12 are less than the threshold value, so the straight line 64 11 and the straight line 64 12 are , classified into group 861 .
  • the straight line 64 11 , the straight line 64 13 , and the straight line 64 14 are , classified into different groups. Further, in the same manner as described above, the angle between the straight line 64 and the line segment 82 is calculated, and the straight lines 64 13 and 64 14 are classified into the group 86 2 .
  • the classification unit 54 outputs information on the group 86 to the linear object model generation unit 56 as a classification result.
  • the linear object model generation unit 56 generates an approximate curve from a group of three-dimensional points corresponding to each straight line classified as a straight line corresponding to the same structure, and creates a plurality of planes perpendicular to the approximate curve at regular intervals. , a linear object model representing a linear object is generated based on the radius and center coordinates of a circle obtained by circle fitting a three-dimensional point group existing on a plane.
  • 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 groups of three-dimensional points included in the group 86, and generates the example shown in FIG.
  • a catenary curve 90 is generated as shown in FIG.
  • the linear object model generation unit 56 provides planes 92 perpendicular to the catenary curve 90 at regular intervals. Furthermore, as shown in FIG. 12, the linear object model generation unit 56 performs circle fitting on a group of 14 three-dimensional points existing on the generated plane 92, and derives the radius and center coordinates of the circle. As a circle fitting method, a known RANSAC or the like can be applied. Thereby, the radius of the linear model can be derived.
  • the linear object model generation unit 56 outputs the catenary curve 90, the radius and center coordinates obtained by circle fitting to the storage control unit 46 as information representing the generated linear object model 36.
  • the storage control unit 46 stores the generated linear object model 36 in the storage 34. Further, the display control unit 48 causes the linear object model 36 to be displayed on the display unit 37.
  • FIG. 13 shows a flowchart of an example of linear object detection processing performed by the linear object detection device 30 of this embodiment.
  • the linear object detection device 30 executes the linear object detection process shown in FIG. 16 by executing the linear object detection program 35 stored in the storage 34. Note that the linear object detection process shown in FIG. 16 is executed at a predetermined timing, such as the timing at which an execution instruction from the user is received.
  • 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 device 20 via the network 9, as described above.
  • the exclusion unit 50 selects, as described above, a partial point consisting of three-dimensional points 14 in which the interval between the three-dimensional points 14 is equal to or less than a predetermined interval, among the plurality of three-dimensional points 14 arranged in the vertical direction. group into groups (see Figure 6).
  • the exclusion unit 50 excludes the three-dimensional points 14 included in the partial point group whose vertical length 62 is greater than or equal to a predetermined length from the candidate point group, as described above (see FIG. (See Figure 7).
  • the exclusion unit 50 excludes the three-dimensional points 14 existing below an arbitrary height from the candidate point group, as described above.
  • the straight line detection unit 52 detects the straight line 64 based on the three-dimensional coordinates of the group of 14 three-dimensional points representing the three-dimensional coordinates of points on the surface of the structure, as described above (see FIG. reference).
  • the straight line detection unit 52 divides the group of 14 three-dimensional points related to one straight line 64 according to the distance between the three-dimensional points 14 related to one straight line 64, as described above (see FIG. 9).
  • the straight line detection unit 52 detects the straight line 64 again based on the three-dimensional coordinates for each group of the divided three-dimensional points 14, as described above (see FIG. 9).
  • the classification unit 54 derives the angles formed between the plurality of detected straight lines 64 and the ground, as described above.
  • the classification unit 54 classifies the straight line 64 based on the angle derived in step S114, as described above (see FIG. 10).
  • the classification unit 54 derives the direction (angle) in which each straight line 64 extends for each classification, as described above.
  • the classification unit 54 performs grouping, as described above, so that the straight lines 64 for which the difference in angle derived in step S118 is equal to or less than the threshold value are grouped into the same group (see FIG. 10).
  • the classification unit 54 calculates the center of gravity 80 for each straight line 64 included in the group 70 from the corresponding group of three-dimensional points 14 for each group 70, as described above (see FIG. 11).
  • the classification unit 54 calculates the line segment 82 connecting the centers of gravity 80, as described above (see FIG. 11).
  • the classification unit 54 calculates the angle between the line segment 82 and the straight line 64, as described above (see FIG. 11).
  • the classification unit 54 groups the straight lines 64 whose angles derived in step S126 are within the threshold into the same group, as described above (see FIG. 11).
  • the linear object model generation unit 56 generates the catenary curve 90 as described above (see FIG. 12).
  • the linear object model generation unit 56 provides planes 92 perpendicular to the catenary curve 90 at regular intervals, as described above (see FIG. 12).
  • the linear object model generation unit 56 performs circle fitting on the group of 14 three-dimensional points existing on the plane 92, as described above, and derives the radius and center coordinates of the circle (see FIG. 12). .
  • the storage control unit 46 stores the linear object model 36 in the storage 34, as described above.
  • FIG. 14A shows an example of the linear object model 36 generated by the linear object detection device 30 of this embodiment.
  • FIG. 14B shows an example of a group of 14 three-dimensional points used to generate the linear object model 36 shown in FIG. 14A. According to FIG. 14A, it can be seen that the linear object detection device 30 of this embodiment can detect linear objects with high accuracy.
  • the linear object detection device 30 of this embodiment detects the straight line 64 based on the three-dimensional coordinates of the group of 14 three-dimensional points representing the three-dimensional coordinates of points on the surface of the structure,
  • the 14 groups of 3-dimensional points related to the straight line 64 are divided according to the distance between the 3-dimensional points 14 related to one detected straight line 64, and the straight line 64 is divided again based on the 3-dimensional coordinates for each group of 14 divided 3-dimensional points.
  • the linear object detection device 30 groups the straight lines 64 based on the angles formed between the plurality of detected straight lines 64 and the ground, and the direction of the straight lines 64, and distinguishes between the straight lines 64 among the straight lines 64 included in the same group.
  • Straight lines 64 in which the angle between the connected line segment 82 and the straight line 64 connected by the line segment 82 is within a threshold value are classified as straight lines 64 corresponding to the same structure.
  • the linear object detection device 30 of this embodiment in order to detect linear objects, even if the three-dimensional point group representing the three-dimensional coordinates is highly dense, the linear object detection device 30 can accurately detect the linear objects. can be detected.
  • various processes that the CPU reads and executes software (programs) in the above embodiments may be executed by various processors other than the CPU.
  • the processor in this case is a PLD (Programmable Logic Device) whose circuit configuration can be changed after manufacturing, such as an FPGA (Field-Programmable Gate Array), and an ASIC (Application Specific Intel).
  • An example is a dedicated electric circuit that is a processor having a specially designed circuit configuration.
  • 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 type or different types (for example, a combination of multiple FPGAs, and a combination of a CPU and an FPGA). etc.).
  • the hardware structure of these various processors is, more specifically, an electric circuit that is a combination of circuit elements such as semiconductor elements.
  • the linear object detection program 35 is stored (installed) in the storage 34 in advance, but the present invention is not limited to this.
  • the linear object detection program 35 is a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital Versatile Disk Read Only Memory), and a USB (Universal Disk Read Only Memory).
  • Non-transitory storage such as Serial Bus) memory It may also be provided in a form stored on a medium.
  • the linear object detection program 35 may be downloaded from an external device via a network.
  • the processor includes: For a 3D point group representing 3D coordinates of points on the surface of a structure, a straight line is detected based on the 3D coordinates, and the straight line is adjusted according to the distance between the 3D points related to one of the detected straight lines. Divide the related 3D point group, detect the straight line again based on the 3D coordinates for each divided 3D point group, The straight lines are grouped based on the angles formed between the plurality of detected straight lines and the ground, and the orientation of the straight lines, and each straight line included in the same group is connected by a line segment connecting the straight lines to each other by the line segment. classifying the straight lines whose angles with the straight line formed within a threshold value as straight lines corresponding to the same structure; A linear object detection device configured as follows.
  • a non-temporary storage medium storing a program executable by a computer to execute a linear object detection process
  • the linear object detection process includes: For a 3D point group representing 3D coordinates of points on the surface of a structure, a straight line is detected based on the 3D coordinates, and the straight line is adjusted according to the distance between the 3D points related to one of the detected straight lines. Divide the related 3D point group, detect the straight line again based on the 3D coordinates for each divided 3D point group, The straight lines are grouped based on the angles formed between the plurality of detected straight lines and the ground, and the orientation of the straight lines, and each straight line included in the same group is connected by a line segment connecting the straight lines to each other by the line segment. classifying the straight lines whose angles with the straight line formed within a threshold value as straight lines corresponding to the same structure; Non-transitory storage medium.
  • 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 section 46
  • Storage control section 48
  • Classification section 52
  • Straight line detection section 54
  • Classification section 56 Linear object model generation section

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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

線状物検出装置、線状物検出方法、及び線状物検出プログラムLinear object detection device, linear object detection method, and linear object detection program
　開示の技術は、線状物検出装置、線状物検出方法、及び線状物検出プログラムに関する。 The disclosed technology relates to a linear object detection device, a linear object detection method, and a linear object detection program.
　従来、車載した３次元レーザスキャナにより、屋外構造物を３次元モデル化する技術（Ｍｏｂｉｌｅ　Ｍａｐｐｉｎｇ　Ｓｙｓｔｅｍ：ＭＭＳ）が開発されている。例えば、特許文献１には、レーザスキャナが１回転する間に取得される点群をスキャンラインと呼ぶクラスタとし、隣接するクラスタが懸垂線状にあることを検出することにより、ケーブル等の構造物を表す３次元モデルデータを作成する技術が記載されている。 Conventionally, a technology (Mobile Mapping System: MMS) has been developed that creates a three-dimensional model of an outdoor structure using a three-dimensional laser scanner mounted on a vehicle. For example, in Patent Document 1, a group of points acquired during one rotation of a laser scanner is defined as a cluster called a scan line, and by detecting that adjacent clusters are in a catenary line, structures such as cables can be A technique for creating three-dimensional model data representing .
特許第６５３１０５１号公報Patent No. 6531051
　しかしながら、特許文献１に記載の技術では、例えば、レーザスキャナによるスキャンラインの間隔が短い等、高密度な点群においては、点群座標の誤差が生じる場合には、その誤差によって懸垂線状と判定されない場合がある。そのため、ケーブル等の線状物を精度よく検出できないという課題があった。 However, in the technology described in Patent Document 1, if an error occurs in the point group coordinates in a high-density point cloud, such as when the interval between scan lines by a laser scanner is short, the error causes a catenary line shape. It may not be determined. Therefore, there was a problem that linear objects such as cables could not be detected with high accuracy.
　開示の技術は、上記の点に鑑みてなされたものであり、３次元座標を表す３次元点群が高密度であっても、線状物を精度良く検出することができる線状物検出装置、線状物検出方法、及び線状物検出プログラムを提供することを目的とする。 The disclosed technology has been made in view of the above points, and provides a linear object detection device that can detect linear objects with high accuracy even if the three-dimensional point group representing three-dimensional coordinates is highly dense. , a linear object detection method, and a linear object detection program.
　本開示の第１態様は、線状物検出装置であって、構造物の表面上の点における３次元座標を表す３次元点群について、３次元座標に基づいて直線を検出し、検出した１つの前記直線に係わる３次元点同士の距離に応じて前記直線に係わる３次元点群を分割し、分割した３次元点群毎に３次元座標に基づいて再度、直線を検出する直線検出部と、検出した複数の直線と地面とのなす角度、及び前記直線の方位に基づいて、前記直線をグルーピングし、同一グループに含まれる各直線について、直線同士を結んだ線分と、前記線分によって結ばれた直線とのなす角度が閾値以内である前記直線同士を、同一の構造物に対応する直線として分類する分類部と、を備える。 A first aspect of the present disclosure is a linear object detection device that detects a straight line based on the three-dimensional coordinates of a three-dimensional point group representing the three-dimensional coordinates of a point on the surface of a structure. a straight line detection unit that divides the three-dimensional point group related to the straight line according to the distance between the three-dimensional points related to the straight line, and detects the straight line again based on the three-dimensional coordinates for each divided three-dimensional point group; , the straight lines are grouped based on the angles formed between the plurality of detected straight lines and the ground, and the orientation of the straight lines, and for each straight line included in the same group, a line segment connecting the straight lines, and a line segment connecting the straight lines, The method further includes a classification unit that classifies the straight lines whose angles with the connected straight lines are within a threshold value as straight lines corresponding to the same structure.
　本開示の第２態様は、線状物検出方法であって、直線検出部が、構造物の表面上の点における３次元座標を表す３次元点群について、３次元座標に基づいて直線を検出し、検出した１つの前記直線に係わる３次元点同士の距離に応じて前記直線に係わる３次元点群を分割し、分割した３次元点群毎に３次元座標に基づいて再度、直線を検出し、分類部が、検出した複数の直線と地面とのなす角度、及び前記直線の方位に基づいて、前記直線をグルーピングし、同一グループに含まれる各直線について、直線同士を結んだ線分と、前記線分によって結ばれた直線とのなす角度が閾値以内である前記直線同士を、同一の構造物に対応する直線として分類する、線状物検出方法である。 A second aspect of the present disclosure is a linear object detection method, in which the straight line detection unit detects a straight line based on the three-dimensional coordinates of a three-dimensional point group representing the three-dimensional coordinates of points on the surface of the structure. Then, the 3D point group related to the straight line is divided according to the distance between the detected 3D points related to the one straight line, and the straight line is detected again based on the 3D coordinates for each divided 3D point group. Then, the classification unit groups the straight lines based on the angles formed between the plurality of detected straight lines and the ground and the orientation of the straight lines, and classifies each straight line included in the same group into a line segment connecting the straight lines. , is a linear object detection method in which straight lines whose angles with straight lines connected by the line segments are within a threshold value are classified as straight lines corresponding to the same structure.
　本開示の第３態様は、線状物検出プログラムであって、コンピュータを、上記第１態様の線状物検出装置の各部として機能させるためのプログラムである。 A third aspect of the present disclosure is a linear object detection program, which is a program for causing a computer to function as each part of the linear object detection device of the first aspect.
　開示の技術によれば、３次元座標を表す３次元点群が高密度であっても、線状物を精度良く検出することができる。 According to the disclosed technology, even if the three-dimensional point group representing three-dimensional coordinates has a high density, linear objects can be detected with high accuracy.
実施形態の線状物モデル生成システムの構成の一例を示す構成図である。FIG. 1 is a configuration diagram showing an example of the configuration of a linear object model generation system according to an embodiment. 実施形態の線状物検出装置による検出対象の一例を示す模式図である。It is a schematic diagram showing an example of a detection target by a linear object detection device of an embodiment. 実施形態の線状物検出装置のハードウェア構成の一例を示す模式図である。FIG. 1 is a schematic diagram showing an example of a hardware configuration of a linear object detection device according to an embodiment. 実施形態の線状物検出装置の機能構成の一例を示すブロック図である。It is a block diagram showing an example of the functional composition of the linear object detection device of an embodiment. 実施形態に係る一部が欠損している街路樹の三次元点群の一例を示す模式図である。It is a schematic diagram which shows an example of the three-dimensional point group of the street tree which concerns on embodiment and is partially missing. 除外部で行われる処理を説明するための図である。FIG. 3 is a diagram for explaining processing performed by an exclusion unit. 除外部で行われる処理を説明するための図である。FIG. 3 is a diagram for explaining processing performed by an exclusion unit. 直線検出部で行われる処理を説明するための図である。FIG. 3 is a diagram for explaining processing performed by a straight line detection section. 直線検出部で行われる処理を説明するための図である。FIG. 3 is a diagram for explaining processing performed by a straight line detection section. 分類部で行われる処理を説明するための図である。FIG. 3 is a diagram for explaining processing performed by a classification section. 分類部で行われる処理を説明するための図である。FIG. 3 is a diagram for explaining processing performed by a classification section. 線状物モデル生成部で行われる処理を説明するための図である。FIG. 3 is a diagram for explaining processing performed by a linear object model generation unit. 実施形態の線状物検出装置における線状物検出処理の一例を示すフローチャートである。It is a flow chart which shows an example of linear object detection processing in a linear object detection device of an embodiment. 実施形態の線状物検出装置により生成された線状物モデルの一例を示す図である。It is a figure showing an example of the linear object model generated by the linear object detection device of an embodiment. 図１４Ａに示した線状物モデルの生成に用いた３次元点群の一例を示す図である。14A is a diagram showing an example of a three-dimensional point group used to generate the linear object model shown in FIG. 14A. FIG.
　以下、開示の技術の実施形態の一例を、図面を参照しつつ説明する。なお、各図面において同一又は等価な構成要素及び部分には同一の参照符号を付与している。また、図面の寸法比率は、説明の都合上誇張されており、実際の比率とは異なる場合がある。 Hereinafter, an example of an embodiment of the disclosed technology will be described with reference to the drawings. In addition, the same reference numerals are given to the same or equivalent components and parts in each drawing. Furthermore, the dimensional ratios in the drawings are exaggerated for convenience of explanation and may differ from the actual ratios.
　まず、本実施形態の技術の線状物モデル生成システム１の構成の一例について説明する。図１に示すように、本実施形態の線状物モデル生成システム１は、点群測定器２０及び線状物検出装置３０を備える。点群測定器２０及び線状物検出装置３０は、ネットワーク９を介して有線通信または無線通信により接続されている。 First, an example of the configuration of the 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 of this embodiment includes a point cloud measuring device 20 and a linear object detection device 30. The point cloud measuring device 20 and the linear object detection device 30 are connected via a network 9 by wired or wireless communication.
　点群測定器２０は、スキャナ２２、記憶媒体２４、及び通信Ｉ／Ｆ（Ｉｎｔｅｒｆａｃｅ)２６を備える。スキャナ２２は、３次元レーザスキャナであり、構造物の表面をレーザによりスキャンすることにより、当該構造物の表面上の点における３次元（Ｘ，Ｙ，Ｚ）座標を点群データとして取得する。 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, and acquires three-dimensional (X, Y, Z) coordinates of points on the surface of the structure as point group data by scanning the surface of the structure with a laser.
　スキャナ２２により取得した点群データは、非一時的記憶媒体である記憶媒体２４に記憶される。記憶媒体２４としては、例えば、ＵＳＢ（Ｕｎｉｖｅｒｓａｌ　Ｓｅｒｉａｌ　Ｂｕｓ）メモリや、ＨＤＤ（Ｈａｒｄ　Ｄｉｓｋ　Ｄｒｉｖｅ）、及びはＳＳＤ（Ｓｏｌｉｄ　Ｓｔａｔｅ　Ｄｒｉｖｅ）等が挙げられる。 The point cloud data acquired by the scanner 22 is stored in a storage medium 24 that is a non-temporary storage medium. Examples of the storage medium 24 include a USB (Universal Serial Bus) memory, an HDD (Hard Disk Drive), and an SSD (Solid State Drive).
　通信Ｉ／Ｆ２６は、ネットワーク９を介して、有線通信または無線通信により、線状物検出装置３０に対して記憶媒体２４に記憶されている点群データ等の各種データの通信を行う。 The communication I/F 26 communicates various data such as point cloud data stored in the storage medium 24 to the linear object detection device 30 via the network 9 by wired or wireless communication.
　例えば、点群測定器２０のスキャナ２２は、図２に示した電柱１０１～１０３、ケーブル１２１～１２４、及び支線１３の表面をレーザによりスキャンニングすることにより電柱１０１～１０３、ケーブル１２１～１２４、及び支線１３の表面上の点における３次元座標を表す点群データを取得する。なお、以下では、電柱１０１～１０３について個々を区別せずに総称する場合、個々を区別するための符号１～３の記載を省略し、単に電柱１０という。また同様にケーブル１２１～１２４について個々を区別せずに総称する場合、個々を区別するための符号１～４の記載を省略し、単にケーブル１２という。本実施形態のスキャナ２２は、図２のＺ軸方向である鉛直方向をスキャンラインとして、例えば、１回転（３６０°）の計測が可能とされている。 For example, the scanner 22 of the point cloud measuring device 20 scans the surfaces of the utility poles 10 1 to 10 3 , cables 12 1 to 12 4 , and branch lines 13 shown in FIG. , the cables 12 1 to 12 4 , and point cloud data representing the three-dimensional coordinates of points on the surface of the branch line 13 are obtained. In the following, when the utility poles 10 1 to 10 3 are collectively referred to without distinguishing them individually, the reference numerals 1 to 3 used to distinguish them from each other will be omitted, and they will simply be referred to as the utility pole 10. Similarly, when the cables 12 1 to 12 4 are collectively referred to without distinguishing them individually, the reference numerals 1 to 4 used to distinguish them from each other are omitted and the cables 12 1 to 12 4 are simply referred to as the 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 a scan line.
　線状物検出装置３０は、点群測定器２０のスキャナ２２が取得し、記憶媒体２４に記憶されている点群データから、構造物のうちの線状物を構成する３次元点群を抽出し、当該線状物を表す線状物モデルを生成する装置である。 The linear object detection device 30 extracts a three-dimensional point group constituting a linear object of the structure from point cloud data acquired by the scanner 22 of the point cloud measuring device 20 and stored in the storage medium 24. This is a device that generates a linear object model representing the linear object.
　本実施形態の線状物検出装置３０のハードウェア構成を説明する。図３に示すように、線状物検出装置３０は、ＣＰＵ（Ｃｅｎｔｒａｌ　Ｐｒｏｃｅｓｓｉｎｇ　Ｕｎｉｔ）３１、ＲＯＭ（Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）３２、ＲＡＭ（Ｒａｎｄｏｍ　Ａｃｃｅｓｓ　Ｍｅｍｏｒｙ）３３、ストレージ３４、表示部３７、及び通信Ｉ／Ｆ３８を備える。各構成は、システムバスやコントロールバス等のバス３９を介して相互に通信可能に接続されている。 The hardware configuration of the linear object detection device 30 of this embodiment will be explained. As shown in Fig. 3, the linear detection device 30 is a CPU (Central Processing Unit) 31, ROM (READ ONLY MEMORY) 32, RAM (RANDOM Access Memory) 33, Storage 37, Display 37, and Display 37. Communication I / Equipped with F38. Each component is communicably connected to each other via a bus 39 such as a system bus or a control bus.
　ＣＰＵ３１は、中央演算処理ユニットであり、ストレージ３４に記憶されている線状物検出プログラム３５等の各種プログラムを実行したり、各部を制御したりする。 The CPU 31 is a central processing unit, and executes various programs such as the linear object detection program 35 stored in the storage 34 and controls each section.
　ＲＯＭ３２には、ＣＰＵ３１で実行される各種プログラム及び各種データが記憶されている。また、ＲＡＭ３３は、ＣＰＵ３１が各種プログラムを実行する際の作業領域として一時的にプログラムまたはデータを記憶する。すなわち、ＣＰＵ３１は、ストレージ３４からプログラムを読み出し、ＲＡＭ３３を作業領域としてプログラムを実行する。 The ROM 32 stores various programs and data executed by the CPU 31. Further, the RAM 33 temporarily stores programs or data as a work area when the CPU 31 executes various programs. That is, the CPU 31 reads the program from the storage 34 and executes the program using the RAM 33 as a work area.
　本実施形態のストレージ３４には、線状物検出プログラム３５が格納されている。なお、線状物検出プログラム３５は、１つのプログラムであってもよいし、複数のプログラム又はモジュールで構成されるプログラム群であってもよい。ストレージ３４としては、ＨＤＤまたはＳＳＤにより構成される。また、ストレージ３４には、オペレーティングシステムを含む各種プログラム、及び各種データ（いずれも図示省略）を格納する。さらに、ストレージ３４には、線状物検出プログラム３５を実行することにより生成された線状物モデル３６が格納される。 A linear object detection program 35 is stored in the storage 34 of this embodiment. Note that the linear object detection program 35 may be one program, or may be a program group composed of a plurality of programs or modules. The storage 34 is configured by an HDD or an SSD. The storage 34 also stores various programs including an operating system and various data (all not shown). Furthermore, a linear object model 36 generated by executing a linear object detection program 35 is stored in the storage 34 .
　表示部３７は、線状物モデルや、各種情報を表示する。表示部３７は特に限定されるものではなく各種のディスプレイ等が挙げられる。 The display unit 37 displays a linear object model and various information. The display section 37 is not particularly limited, and various types of displays may be used.
　通信Ｉ／Ｆ３８は、ネットワーク９を介して点群測定器２０と通信するためのインタフェースであり、例えば、イーサネット（登録商標）、ＦＤＤＩ、Ｗｉ－Ｆｉ（登録商標）等の規格が用いられる。 The communication I/F 38 is an interface for communicating with the point cloud measuring device 20 via the network 9, and uses standards such as Ethernet (registered trademark), FDDI, and Wi-Fi (registered trademark), for example.
　次に、線状物検出装置３０の機能構成について説明する。図４に示すように、線状物検出装置３０は、読込部４０、パラメータ設定部４２、線状物モデル算出部４４、保存制御部４６、及び表示制御部４８を備える。ＣＰＵ３１がストレージ３４に記憶されている線状物検出プログラム３５を実行することにより、ＣＰＵ３１が、読込部４０、パラメータ設定部４２、線状物モデル算出部４４、保存制御部４６、及び表示制御部４８として機能する。 Next, the functional configuration of the linear object detection device 30 will be explained. As shown in FIG. 4, the linear object detection device 30 includes a reading section 40, a parameter setting section 42, a linear object model calculation section 44, a storage control section 46, and a display control section 48. When the CPU 31 executes the linear object detection program 35 stored in the storage 34, the CPU 31 executes the reading section 40, the parameter setting section 42, the linear object model calculation section 44, the storage control section 46, and the display control section. Functions as 48.
　読込部４０は、点群測定器２０の記憶媒体２４に記憶されている点群データを、ネットワーク９を介して読み込む。読込部４０は、読み込んだ点群データを線状物モデル算出部４４に出力する。図５には、読込部４０が読み込んだ３次元点１４群の一例が示されている。以下では、一例として、読込部４０が図５に示した３次元点１４群を読み込んだ場合について説明する。 The reading unit 40 reads 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 14 three-dimensional points read by the reading unit 40. Below, as an example, a case where the reading unit 40 reads the group of 14 three-dimensional points shown in FIG. 5 will be described.
　パラメータ設定部４２は、線状物モデル算出部４４において線状物モデルを算出する際に用いられる各種パラメータを記憶し、線状物モデル算出部４４に出力する。 The parameter setting unit 42 stores various parameters used when calculating a linear object model in the linear object model calculation unit 44, and outputs them to the linear object model calculation unit 44.
　線状物モデル算出部４４は、除外部５０、直線検出部５２、分類部５４、及び線状物モデル生成部５６を含む。 The linear object model calculation section 44 includes an exclusion section 50, a straight line detection section 52, a classification section 54, and a linear object model generation section 56.
　除外部５０は、読込部４０が読み込んだ点群データが表す３次元点群（以下、読込部４０が読み込んだ３次元点群という）から、検出対象となる線状物以外の構造物の表面上の点とみなせる３次元点を除外する。一例として、本実施形態の線状物検出装置３０は線状物として、ケーブル１２を検出する。そのため、除外部５０は、読込部４０が読み込んだ３次元点群から、電柱１０１～１０３の表面上の点とみなせる３次元点を除外する。 The exclusion unit 50 extracts surfaces of structures other than linear objects to be detected from the three-dimensional point cloud represented by the point cloud data read by the reading unit 40 (hereinafter referred to as the three-dimensional point group read by the reading unit 40). Exclude three-dimensional points that can be considered as points above. As an example, the linear object detection device 30 of this embodiment detects the cable 12 as the linear object. Therefore, the exclusion unit 50 excludes three-dimensional points that can be regarded as points on the surfaces of the utility poles 10 1 to 10 3 from the three-dimensional point group read by the reading unit 40 .
　具体的には、除外部５０は、水平面（図のＸＹ平面に相当）と交差する鉛直方向（図のＺ軸方向）に並んだ複数の３次元点１４のうち、３次元点１４同士の間隔が所定の間隔以下の３次元点１４からなる部分点群の鉛直方向の長さが所定の長さ以上の場合、当該部分点群に含まれる３次元点を、水平面上に投影する対象から除外する。 Specifically, the exclusion unit 50 calculates the distance between the three-dimensional points 14 among the plurality of three-dimensional points 14 arranged in the vertical direction (Z-axis direction in the figure) intersecting the horizontal plane (corresponding to the XY plane in the figure). If the vertical length of a partial point group consisting of three-dimensional points 14 with a predetermined interval or less is greater than or equal to a predetermined length, the three-dimensional points included in the partial point group are excluded from being projected onto a horizontal plane. do.
　図６及び図７を参照して、除外部５０が行う処理について詳細に説明する。まず、図６に示すように、除外部５０は、取得した３次元点１４群から、鉛直方向（図６のＺ軸方向）に並んだ複数の３次元点１４のうち、３次元点１４同士の間隔が所定の間隔以下の３次元点１４をグループ化し、部分点群とする。本実施形態では、スキャナ２２のスキャンラインは鉛直方向に伸びている。そのため、本処理は、３次元点１４群からスキャンラインに応じて３次元点１４をグループ化することに相当する。図６に示した例では、電柱１０１に対応する３次元点１４群からは、２つの部分点群６０が形成され、電柱１０２に対応する３次元点１４群からは、２つの部分点群６０が形成される。また、ケーブル１２１に対応する３次元点１４群からは、７つの部分点群６０が形成され、ケーブル１２２に対応する３次元点１４群からは、７つの部分点群６０が形成され、ケーブル１２３に対応する３次元点１４群からは、７つの部分点群６０が形成される。さらに、ケーブル１２４に対応する３次元点１４群からは、４つの部分点群６０が形成される。また、支線１３に対応する３次元点１４群からは、５つの部分点群６０が形成される。 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 selects three-dimensional points 14 from among a plurality of three-dimensional points 14 lined up in the vertical direction (Z-axis direction in FIG. 6) from the acquired group of three-dimensional points 14. The three-dimensional points 14 whose intervals are equal to or less than a predetermined interval are grouped to form a partial point group. In this embodiment, the scan line of the scanner 22 extends in the vertical direction. Therefore, this processing corresponds to grouping the three-dimensional points 14 from the group of three-dimensional points 14 according to the scan line. In the example shown in FIG. 6, two partial point groups 60 are formed from the 3-dimensional point 14 group corresponding to the utility pole 10 1 , and two partial point groups 60 are formed from the 3-dimensional point 14 group corresponding to the utility pole 10 2 . A group 60 is formed. Furthermore, seven partial point groups 60 are formed from the three -dimensional point group 14 corresponding to the cable 121 , and seven partial point groups 60 are formed from the three-dimensional point group 14 corresponding to the cable 122. Seven partial point groups 60 are formed from the three-dimensional point group 14 corresponding to the cable 123 . Furthermore, four partial point groups 60 are formed from the three-dimensional point group 14 corresponding to the cable 124 . Furthermore, five partial point groups 60 are formed from the three-dimensional point group 14 corresponding to the branch line 13.
　次に、除外部５０は、部分点群６０毎に、部分点群の鉛直方向の長さ６２が、所定の長さ以上であるか否かを検出する。一般に、ケーブル１２や支線１３よりも、電柱１０の方が、鉛直方向の長さ６２が長くなる。そのため、ケーブル１２及び支線１３と、電柱１０とを区別するための閾値となる長さを所定の長さとして予め定めておく。除外部５０は、部分点群６０の鉛直方向の長さ６２が所定の長さ以上である場合、その部分点群６０に含まれる３次元点１４は、電柱１０に対応する３次元点１４であるとみなせるため、これらの３次元点１４を、ケーブル１２の候補となる３次元点１４群（以下、候補点群という）から除外し、以降の処理に用いない。換言すると、除外部５０は、部分点群６０の鉛直方向の長さ６２が所定の長さ未満である場合、その部分点群６０に含まれる３次元点１４は、ケーブル１２に対応する３次元点１４または支線１３に対応する３次元点１４であるとみなして、候補点群とし、以降の処理に用いる。なお、ここで閾値として用いる所定の長さは、パラメータ設定部４２に設定されている。 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 the utility pole 10 is longer than that of the cable 12 or the branch line 13. Therefore, a length that is a threshold value for distinguishing between the cable 12 and the branch line 13 and the utility pole 10 is determined in advance as a predetermined length. If the vertical length 62 of the partial point group 60 is greater than or equal to a predetermined length, the exclusion unit 50 determines that the three-dimensional point 14 included in the partial point group 60 is the three-dimensional point 14 corresponding to the utility pole 10. Therefore, these three-dimensional points 14 are excluded from the group of three-dimensional points 14 that are candidates for the cable 12 (hereinafter referred to as candidate point group), and are not used in subsequent processing. In other words, when the vertical length 62 of the partial point group 60 is less than a predetermined length, the exclusion unit 50 excludes the three-dimensional point 14 included in the partial point group 60 from the three-dimensional point corresponding to the cable 12. It is assumed that the three-dimensional point 14 corresponds to the point 14 or the branch line 13, and is used as a candidate point group for subsequent processing. Note that the predetermined length used as the threshold here is set in the parameter setting section 42.
　このように、鉛直方向の長さ６２が所定の長さ以上となる部分点群６０に含まれる３次元点１４を除外することにより、電柱１０に限らず、壁や地面等の表面上の３次元点１４を候補点群から除外することができる。これにより、以降の処理にかかる処理負荷を低減することができる。なお、壁や地面等の表面上の３次元点１４を候補点群から除外する場合、上述の所定の長さは、適宜、設定すればよい。 In this way, by excluding the three-dimensional points 14 included in the partial point group 60 whose vertical length 62 is greater than or equal to a predetermined length, three-dimensional points 14 on the surface of not only the telephone pole 10 but also a wall, the ground, etc. Dimensional point 14 can be excluded from the candidate point cloud. Thereby, the processing load on subsequent processing can be reduced. Note that when excluding the three-dimensional points 14 on a surface such as a wall or the ground from the candidate point group, the above-mentioned predetermined length may be set as appropriate.
　また、除外部５０は、任意の高さ以下に存在する３次元点１４を候補点群からさらに除外する。上述したように本実施形態ではケーブル１２を検出対象としており、ケーブル１２は、ある程度、高い位置（約５ｍ以上）に敷設されている。そのため、除外部５０は、地面付近等、低い位置に存在する３次元点１４を候補点群から除外する。具体的には、除外部５０は、Ｚ座標の値が閾値未満の３次元点１４を候補点群から除外する。これにより、以降の処理にかかる処理負荷を低減することができ、処理時間を短縮化することができる。なお、スキャナ２２が設けられている位置に応じて、３次元点１４のＺ座標の値が同じであっても、実空間における高さは異なる。例えば、スキャナ２２が地表付近に存在する場合と、三脚上に設置されている場合とでは、３次元点１４のＺ座標の値が０であっても、その３次元点１４の実空間における高さが異なることになる。そのため、ここで用いる任意の高さは、スキャナ２２が設けられている鉛直方向の位置、すなわち高さに応じて変化する。 Furthermore, the exclusion unit 50 further excludes three-dimensional points 14 that exist below an arbitrary height from the candidate point group. As described above, in this embodiment, the cable 12 is the detection target, and the cable 12 is laid at a relatively high position (about 5 m or more). Therefore, the exclusion unit 50 excludes three-dimensional points 14 located at low positions, such as near the ground, from the candidate point group. Specifically, the exclusion unit 50 excludes three-dimensional points 14 whose Z coordinate value is less than a threshold value from the candidate point group. Thereby, the processing load on subsequent processing can be reduced, and the processing time can be shortened. Note that even if the Z coordinate value of the three-dimensional point 14 is the same, the height in real space differs depending on the position where the scanner 22 is provided. For example, if the scanner 22 is located near the ground surface or if it is installed on a tripod, even if the Z coordinate value of the three-dimensional point 14 is 0, the height of the three-dimensional point 14 in real space will be The result will be different. Therefore, the arbitrary height used here changes depending on the vertical position where the scanner 22 is provided, that is, the height.
　このように除外部５０が、３次元点１４を候補点群から除外することで、図５に示した３次元点１４群から、図７に示した３次元点１４群に候補点群が絞られる。除外部５０は、候補点群の情報を、直線検出部５２に出力する。 By excluding the three-dimensional points 14 from the candidate point group in this way, the exclusion unit 50 narrows down the candidate point group from the three-dimensional point group 14 shown in FIG. 5 to the three-dimensional point 14 group shown in FIG. It will be done. The exclusion unit 50 outputs information about the candidate point group to the straight line detection unit 52.
　直線検出部５２は、構造物の表面上の点における３次元座標を表す３次元点１４群について、３次元座標に基づいて直線を検出し、検出した１つの直線に係わる３次元点１４同士の距離に応じて直線に係わる３次元点１４群を分割し、分割した３次元点１４群毎に３次元座標に基づいて再度、直線を検出する。 The straight line detection unit 52 detects straight lines based on the three-dimensional coordinates of a group of 14 three-dimensional points representing the three-dimensional coordinates of points on the surface of the structure, and calculates the relationship between the three-dimensional points 14 related to one detected straight line. The 14 groups of 3-dimensional points related to the straight line are divided according to distance, and the straight line is detected again based on the 3-dimensional coordinates for each group of 14 divided 3-dimensional points.
　詳細には、直線検出部５２は、３次元点１４群である候補点群から、３次元座標に基づいて、すなわち３次元空間において直線を検出する。なお、直線検出部５２が３次元点１４群から直線検出を行う方法は特に限定されず、例えば、公知のＨｏｕｇｈ変換等を用いればよい。本実施形態では、図８に示すように、ケーブル１２１に対応する３次元点１４群からは、直線６４１が検出され、ケーブル１２２に対応する３次元点１４群からは、直線６４２が検出される。また、ケーブル１２３に対応する３次元点１４群からは、直線６４３が検出され、ケーブル１２４に対応する３次元点１４群からは、直線６４４が検出される。さらに、支線１３に対応する３次元点１４群からは、直線６４５が検出される。本処理を行うことにより、ケーブル１２２に対するケーブル１２３のように、分岐ケーブル部を分離して検出することができる。 Specifically, the straight line detection unit 52 detects a straight line from a group of candidate points, which is a group of 14 three-dimensional points, based on three-dimensional coordinates, that is, in a three-dimensional space. Note that the method by which the straight line detection unit 52 detects a straight line from the group of 14 three-dimensional points is not particularly limited, and for example, a known Hough transformation or the like may be used. In this embodiment, as shown in FIG. 8, a straight line 64 1 is detected from the group of 14 three-dimensional points corresponding to the cable 12 1 , and a straight line 64 2 is detected from the group of 14 three-dimensional points corresponding to the cable 12 2 . is detected. Further, a straight line 64 3 is detected from the group of 14 three-dimensional points corresponding to the cable 12 3, and a straight line 64 4 is detected from the group of 14 three-dimensional points corresponding to the cable 12 4 . Furthermore, a straight line 645 is detected from the group of three-dimensional points 14 corresponding to the branch line 13. By performing this processing, branch cable parts can be separated and detected, such as cable 12 3 for cable 12 2 .
　また、直線検出部５２は、検出した直線６４に係わる３次元点１４同士の距離に応じて、３次元点１４間の距離が所定の距離以上離れている場合は、その部分で３次元点１４群を分割する。ケーブル１２を検出対象の構造物とした場合に、樹木等他の構造物が隣接している場合、図９に示すように、ケーブル１２に対応する３次元点１４群と、樹木に対応する３次元点１４群とから、１本の直線６４が検出される場合がある。ケーブル１２に対応する３次元点１４群と、樹木に対応する３次元点１４群とは、比較的、離間している。そのため、直線検出部５２は、３次元点１４間の距離が所定の距離以上、離間している場合、１本の直線６４に対して、異なる構造物に由来する３次元点１４群が対応してしいるとみなして、離間している部分で３次元点１４群を分割し、各々別の集合とする。そして、直線検出部５２は、分割した３次元点１４毎、すなわち別の集合毎に、再度、直線検出を行う。図９に示した例では、ケーブル１２に対応する３次元点１４群により直線６４Ａが検出され、樹木に対応する３次元点１４群により直線６４Ｂが検出される。 Further, according to the distance between the three-dimensional points 14 related to the detected straight line 64, if the distance between the three-dimensional points 14 is a predetermined distance or more, the straight line detection unit 52 detects that the three-dimensional points 14 are Divide the group. When the cable 12 is a structure to be detected and there is another structure adjacent to it, such as a tree, as shown in FIG. One straight line 64 may be detected from the group of 14 dimensional points. The group of 14 three-dimensional points corresponding to the cable 12 and the group of 14 three-dimensional points corresponding to the tree are relatively spaced apart. Therefore, when the distance between the three-dimensional points 14 is a predetermined distance or more, the straight line detection unit 52 detects that a group of three-dimensional points 14 originating from different structures correspond to one straight line 64. The 14 groups of three-dimensional points are divided into separate sets based on the distance between them. Then, the straight line detection unit 52 performs straight line detection again for each divided three-dimensional point 14, that is, for each different set. In the example shown in FIG. 9, a straight line 64A is detected by a group of 14 three-dimensional points corresponding to the cable 12, and a straight line 64B is detected by a group of 14 three-dimensional points corresponding to a tree.
　直線検出部５２は、検出した複数の直線６４を表す情報を分類部５４に出力する。 The straight line detection unit 52 outputs information representing the plurality of detected straight lines 64 to the classification unit 54.
　分類部５４は、検出した複数の直線６４と地面とのなす角度、及び直線６４の方位に基づいて、直線６４をグルーピングし、同一グループに含まれる各直線６４について、直線６４同士を結んだ線分と、線分によって結ばれた直線６４とのなす角度が閾値以内である直線６４同士を、同一の構造物に対応する直線６４として分類する。 The classification unit 54 groups the straight lines 64 based on the angles formed between the plurality of detected straight lines 64 and the ground, and the direction of the straight lines 64, and classifies lines 64 that connect the straight lines 64 for each straight line 64 included in the same group. Straight lines 64 in which the angle between the line segment and the straight line 64 connected by the line segment are within a threshold value are classified as straight lines 64 corresponding to the same structure.
　具体的には、直線検出部５２は、まず、直線６４に対応する３次元点１４群の３次元座標に基づいて、直線６４と、地面（地表面）とのなす角度を算出する。そして直線検出部５２は、算出したなす角度と、構造物の種類に応じたなす角度の範囲とによって、直線６４を分類する。例えば、ケーブル１２は、比較的地表面に平行に近い状態に配線されている。そのため、ケーブル１２に対応する３次元点１４群によって検出された直線６４と、地面とのなす角度は、比較的小さくなる。また、支線１３は、支線取付角度として、２５度～４５度が推奨されている。そのため、支線１３に対応する３次元点１４群によって検出された直線６４と、地面とのなす角度は、４５度～６５度近傍となる傾向がある。さらに、樹木や電柱１０等他の構造物に対応する３次元点１４群によって検出された直線６４と、地面とのなす角度は、比較的垂直に近くなる。そのため、例えば、直線６４と地面とのなす角度が４５度未満の場合、その直線６４は、ケーブル１２に対応する３次元点１４群により検出された直線６４に分類する。また、直線６４と地面とのなす角度が４５度以上、６５度未満の場合、その直線６４は、支線１３に対応する３次元点１４群により検出された直線６４に分類する。また、直線６４と地面とのなす角度が６５度以上の場合、その直線６４は、その他の構造物に対応する３次元点１４群により検出された直線６４に分類する。例えば、図８に示した例では、直線６４１～直線６４４は、ケーブル１２に対応するものとして分類する。また、直線６４５は、支線１３に対応するものとして分類する。 Specifically, the straight line detection unit 52 first calculates the angle between the straight line 64 and the ground (ground surface) based on the three-dimensional coordinates of the group of 14 three-dimensional points corresponding to the straight line 64. The straight line detection unit 52 then classifies the straight line 64 based on the calculated angle and the range of the angle depending on the type of structure. For example, the cable 12 is laid relatively parallel to the ground surface. Therefore, the angle between the straight line 64 detected by the group of 14 three-dimensional points corresponding to the cable 12 and the ground becomes relatively small. Further, it is recommended that the branch line 13 be installed at an angle of 25 degrees to 45 degrees. Therefore, the angle between the straight line 64 detected by the group of 14 three-dimensional points corresponding to the branch line 13 and the ground tends to be around 45 degrees to 65 degrees. Furthermore, the angle between the straight line 64 detected by the group of 14 three-dimensional points 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 straight line 64 and the ground is less than 45 degrees, the straight line 64 is classified as the straight line 64 detected by the group of three-dimensional points 14 corresponding to the cable 12. Further, when the angle between the straight line 64 and the ground is 45 degrees or more and less than 65 degrees, the straight line 64 is classified as a straight line 64 detected by the group of three-dimensional points 14 corresponding to the branch line 13. Further, if the angle between the straight line 64 and the ground is 65 degrees or more, the straight line 64 is classified as a straight line 64 detected by a group of 14 three-dimensional points corresponding to other structures. For example, in the example shown in FIG. 8, straight lines 64 1 to 64 4 are classified as corresponding to the cable 12. Further, the straight line 645 is classified as corresponding to the branch line 13.
　また、分類部５４は、検出した複数の直線と地面とのなす角度、及び直線の包囲に基づいて、直線をグルーピングし、同一グループに含まれる各直線について、直線同士を結んだ線分と、線分によって結ばれた直線とのなす角度が閾値以内である直線同士を、同一の構造物に対応する直線として分類する。 Furthermore, the classification unit 54 groups the straight lines based on the angles formed between the plurality of detected straight lines and the ground, and the encirclement of the straight lines, and for each straight line included in the same group, segments connecting the straight lines, Straight lines whose angles with straight lines connected by line segments are within a threshold are classified as straight lines corresponding to the same structure.
　具体的には、分類部５４は、上記分類毎に、各直線６４が伸びている方位（角度）を導出する。そして、導出した角度の差が閾値以下となる直線６４同士が同一のグループとなるようグルーピングを行う。図１０には、ケーブル１２に対応する３次元点１４群により検出された直線６４に分類された６本の直線６４（６４１１、６４１２、６４１３、６４１４、６４２１、及び６４２２）を分類部５４がグルーピングした例が示されている。直線６４１１～６４１４は角度の差が閾値以下となるため、同一のグループ７０１にグルーピングされる。また、直線６４２１、６４２２は角度の差が閾値以下となるため、同一のグループ７０２にグルーピングされる。 Specifically, the classification unit 54 derives the direction (angle) in which each straight line 64 extends for each of the above classifications. Then, grouping is performed such that straight lines 64 for which the difference between the derived angles is equal to or less than a threshold value are in the same group. In FIG. 10, six straight lines 64 (64 11 , 64 12 , 64 13 , 64 14 , 64 21 , and 64 22 ) classified into straight lines 64 detected by a group of 14 three-dimensional points corresponding to the cable 12 are shown . An example of grouping by the classification unit 54 is shown. The straight lines 64 11 to 64 14 are grouped into the same group 70 1 because the difference in angle is less than the threshold value. Further, since the difference in angle between the straight lines 64 21 and 64 22 is less than or equal to the threshold value, the straight lines 64 21 and 64 22 are grouped into the same group 70 2 .
　また、分類部５４は、グループ７０毎に、グループ７０に含まれる各直線６４について、対応する３次元点１４群から重心を算出し、各直線６４の重心とする。例えば、図１１に示すように、分類部５４は、グループ７０１においては、直線６４１１の重心８０１、直線６４１２の重心８０２、直線６４１３の重心８０３、及び直線６４１４の重心８０４を算出する。 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 three-dimensional points 14, and sets the calculated center of gravity of each straight line 64. For example, as shown in FIG. 11, in the group 70 1 , the classification unit 54 determines the center of gravity 80 1 of the straight line 64 11 , the center of gravity 80 2 of the straight line 64 12 , the center of gravity 80 3 of the straight line 64 13 , and the center of gravity of the straight line 64 14 . Calculate 80 4 .
　さらに、分類部５４は、算出した重心８０同士を結んだ線分（方向ベクトル）を算出する。例えば、図１１に示したように、直線６４１１の重心８０１については、直線６４１２の重心８０２と結んだ線分８２２、直線６４１３の重心８０３とを結んだ線分８２３、及び直線６４１４の重心８０４とを結んだ線分８２４が算出される。なお、重心８０は、３次元点１４の座標の平均値を用いることが好ましい。 Further, the classification unit 54 calculates a line segment (direction vector) connecting the calculated centers of gravity 80. For example, as shown in FIG. 11, regarding the center of gravity 80 1 of the straight line 64 11 , a line segment 82 2 connecting the center of gravity 80 2 of the straight line 64 12 and a line segment 82 3 connecting the center of gravity 80 3 of the straight line 64 13 , and the center of gravity 804 of the straight line 6414 , a line segment 824 is calculated. Note that, as the center of gravity 80, it is preferable to use the average value of the coordinates of the three-dimensional points 14.
　さらに、分類部５４は、直線６４と線分８２とのなす角度を算出する。図１１に示した例では、直線６４１１と線分８２２とのなす角度、直線６４１２と線分８２２とのなす角度、直線６４１１と線分８２３とのなす角度、直線６４１３と線分８２３とのなす角度、直線６４１１と線分８２４とのなす角度、及び直線６４１４と線分８２４とのなす角度が算出される。そして、分類部５４は、算出した線分８２とのなす角度が、閾値以内である直線６４同士を、同一の構造物に対応する直線とみなして、同一のグループに分類する。図１１に示した例では、線分８２２と直線６４１１とのなす角度、及び線分８２２と直線６４１２とのなす角度は閾値以下であるため、直線６４１１と直線６４１２とは、グループ８６１に分類される。また、線分８２３と直線６４１３とのなす角度、及び線分８２４と直線６４１４とのなす角度は、ともに閾値を超えるため、直線６４１１と、直線６４１３及び直線６４１４とは、別のグループに分類される。また、上記と同様に、直線６４と線分８２とのなす角が算出され、直線６４１３と直線６４１４とは、グループ８６２に分類される。 Further, the classification unit 54 calculates the angle between the straight line 64 and the line segment 82. In the example shown in FIG. 11, the angle between straight line 64 11 and line segment 82 2 , the angle between straight line 64 12 and line segment 82 2 , the angle between straight line 64 11 and line segment 82 3, and the angle between straight line 64 11 and line segment 82 3 , straight line 64 13 The angle between the line 64 11 and the line segment 82 3 , the angle between the straight line 64 11 and the line segment 82 4 , and the angle between the straight line 64 14 and the line segment 82 4 are calculated. Then, the classification unit 54 regards the straight lines 64 whose angles with the calculated line segment 82 are within the threshold value as straight lines corresponding to the same structure, and classifies them into the same group. In the example shown in FIG. 11, the angle between the line segment 82 2 and the straight line 64 11 and the angle between the line segment 82 2 and the straight line 64 12 are less than the threshold value, so the straight line 64 11 and the straight line 64 12 are , classified into group 861 . In addition, since the angle between the line segment 82 3 and the straight line 64 13 and the angle between the line segment 82 4 and the straight line 64 14 exceed the threshold, the straight line 64 11 , the straight line 64 13 , and the straight line 64 14 are , classified into different groups. Further, in the same manner as described above, the angle between the straight line 64 and the line segment 82 is calculated, and the straight lines 64 13 and 64 14 are classified into the group 86 2 .
　分類部５４は、分類結果としてグループ８６の情報を線状物モデル生成部５６に出力する。 The classification unit 54 outputs information on the group 86 to the linear object model generation unit 56 as a classification result.
　線状物モデル生成部５６は、同一の構造物に対応する直線として分類された各直線に対応する３次元点群から近似曲線を生成し、近似曲線に垂直な複数の平面を一定間隔で設け、平面上に存在する３次元点群を円フィッティングすることで得られた円の半径及び中心座標に基づいて、線状物を表す線状物モデルを生成する。 The linear object model generation unit 56 generates an approximate curve from a group of three-dimensional points corresponding to each straight line classified as a straight line corresponding to the same structure, and creates a plurality of planes perpendicular to the approximate curve at regular intervals. , a linear object model representing a linear object is generated based on the radius and center coordinates of a circle obtained by circle fitting a three-dimensional point group existing on a plane.
　具体的には、線状物モデル生成部５６は、グループ８６毎に、グループ８６に含まれる３次元点１４群各々の３次元座標を用いて、カテナリ曲線近似を行い、図１２に示した例のように、カテナリ曲線９０を生成する。 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 groups of three-dimensional points included in the group 86, and generates the example shown in FIG. A catenary curve 90 is generated as shown in FIG.
　また、線状物モデル生成部５６は、図１２に示すように、カテナリ曲線９０に垂直な平面９２を一定間隔で設ける。さらに、線状物モデル生成部５６は、図１２に示すように、生成された平面９２上に存在する３次元点１４点群を、円フィッティングし、その円の半径と中心座標を導出する。円フィッティングの方法としては、公知のＲＡＮＳＡＣ等を適用することができる。これにより、線状モデルの半径を導出することができる。 Furthermore, as shown in FIG. 12, the linear object model generation unit 56 provides planes 92 perpendicular to the catenary curve 90 at regular intervals. Furthermore, as shown in FIG. 12, the linear object model generation unit 56 performs circle fitting on a group of 14 three-dimensional points existing on the generated plane 92, and derives the radius and center coordinates of the circle. As a circle fitting method, a known RANSAC or the like can be applied. Thereby, the radius of the linear model can be derived.
　線状物モデル生成部５６は、生成した線状物モデル３６を表す情報として、カテナリ曲線９０、円フィッティングにより得られた半径及び中心座標を保存制御部４６に出力する。 The linear object model generation unit 56 outputs the catenary curve 90, the radius and center coordinates obtained by circle fitting to the storage control unit 46 as information representing the generated linear object model 36.
　保存制御部４６は、生成した線状物モデル３６をストレージ３４に格納する。また、表示制御部４８は、線状物モデル３６を表示部３７に表示させる。 The storage control unit 46 stores the generated linear object model 36 in the storage 34. Further, the display control unit 48 causes the linear object model 36 to be displayed on the display unit 37.
　次に線状物検出装置３０の作用について説明する。 Next, the operation of the linear object detection device 30 will be explained.
　図１３には、本実施形態の線状物検出装置３０により実行される線状物検出処理の一例のフローチャートが示されている。線状物検出装置３０は、ストレージ３４に記憶されている線状物検出プログラム３５を実行することにより、図１６に示した線状物検出処理を実行する。なお、図１６に示した線状物検出処理は、ユーザからの実行指示を受け付けたタイミング等、所定のタイミングで実行される。 FIG. 13 shows a flowchart of an example of linear object detection processing performed by the linear object detection device 30 of this embodiment. The linear object detection device 30 executes the linear object detection process shown in FIG. 16 by executing the linear object detection program 35 stored in the storage 34. Note that the linear object detection process shown in FIG. 16 is executed at a predetermined timing, such as the timing at which an execution instruction from the user is received.
　図１６のステップＳ１００で読込部４０は、上述したように、点群測定器２０の記憶媒体２４に記憶されている点群データを、ネットワーク９を介して読み込む。 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 device 20 via the network 9, as described above.
　次のステップＳ１０２で除外部５０は、上述したように、鉛直方向に並んだ複数の３次元点１４のうち、３次元点１４同士の間隔が所定の間隔以下の３次元点１４からなる部分点群にグループ化する（図６参照）。 In the next step S102, the exclusion unit 50 selects, as described above, a partial point consisting of three-dimensional points 14 in which the interval between the three-dimensional points 14 is equal to or less than a predetermined interval, among the plurality of three-dimensional points 14 arranged in the vertical direction. group into groups (see Figure 6).
　次のステップＳ１０４で除外部５０は、上述したように、鉛直方向の長さ６２が所定の長さ以上となる部分点群に含まれる３次元点１４を候補点群から除外する（図６、図７参照）。 In the next step S104, the exclusion unit 50 excludes the three-dimensional points 14 included in the partial point group whose vertical length 62 is greater than or equal to a predetermined length from the candidate point group, as described above (see FIG. (See Figure 7).
　次のステップＳ１０６で除外部５０は、上述したように、任意の高さ以下に存在する３次元点１４を候補点群から除外する。 In the next step S106, the exclusion unit 50 excludes the three-dimensional points 14 existing below an arbitrary height from the candidate point group, as described above.
　次のステップＳ１０８で直線検出部５２は、上述したように、構造物の表面上の点における３次元座標を表す３次元点１４群について、３次元座標に基づいて直線６４を検出する（図８参照）。 In the next step S108, the straight line detection unit 52 detects the straight line 64 based on the three-dimensional coordinates of the group of 14 three-dimensional points representing the three-dimensional coordinates of points on the surface of the structure, as described above (see FIG. reference).
　次のステップＳ１１０で直線検出部５２は、上述したように、１つの直線６４に係わる３次元点１４同士の距離に応じて直線６４に係わる３次元点１４群を分割する（図９参照）。 In the next step S110, the straight line detection unit 52 divides the group of 14 three-dimensional points related to one straight line 64 according to the distance between the three-dimensional points 14 related to one straight line 64, as described above (see FIG. 9).
　次のステップＳ１１２で直線検出部５２は、上述したように、分割した３次元点１４群毎に３次元座標に基づいて再度、直線６４を検出する（図９参照）。 In the next step S112, the straight line detection unit 52 detects the straight line 64 again based on the three-dimensional coordinates for each group of the divided three-dimensional points 14, as described above (see FIG. 9).
　次のステップＳ１１４で分類部５４は、上述したように、検出した複数の直線６４と地面とのなす角度を導出する。 In the next step S114, the classification unit 54 derives the angles formed between the plurality of detected straight lines 64 and the ground, as described above.
　次のステップＳ１１６で分類部５４は、上述したように、上記ステップＳ１１４で導出した角度に基づいて、直線６４を分類する（図１０参照）。 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).
　次のステップＳ１１８で分類部５４は、上述したように、分類毎に、各直線６４が伸びている方位（角度）を導出する。 In the next step S118, the classification unit 54 derives the direction (angle) in which each straight line 64 extends for each classification, as described above.
　次のステップＳ１２０で分類部５４は、上述したように、上記ステップＳ１１８で導出した角度の差が閾値以下となる直線６４同士が同一のグループとなるようグルーピングを行う（図１０参照）。 In the next step S120, the classification unit 54 performs grouping, as described above, so that the straight lines 64 for which the difference in angle derived in step S118 is equal to or less than the threshold value are grouped into the same group (see FIG. 10).
　次のステップＳ１２２で分類部５４は、上述したように、グループ７０毎に、グループ７０に含まれる各直線６４について、対応する３次元点１４群から重心８０を算出する（図１１参照）。 In the next step S122, the classification unit 54 calculates the center of gravity 80 for each straight line 64 included in the group 70 from the corresponding group of three-dimensional points 14 for each group 70, as described above (see FIG. 11).
　次のステップＳ１２４で分類部５４は、上述したように、重心８０を結んだ線分８２を算出する（図１１参照）。 In the next step S124, the classification unit 54 calculates the line segment 82 connecting the centers of gravity 80, as described above (see FIG. 11).
　次のステップＳ１２６で分類部５４は、上述したように、線分８２と直線６４とのなす角度を算出する（図１１参照）。 In the next step S126, the classification unit 54 calculates the angle between the line segment 82 and the straight line 64, as described above (see FIG. 11).
　次のステップＳ１２８で分類部５４は、上述したように、上記ステップＳ１２６で導出した角度が閾値以内の直線６４を同一グループにグルーピングする（図１１参照）。 In the next step S128, the classification unit 54 groups the straight lines 64 whose angles derived in step S126 are within the threshold into the same group, as described above (see FIG. 11).
　次のステップＳ１３０で線状物モデル生成部５６は、上述したように、カテナリ曲線９０を生成する（図１２参照）。 In the next step S130, the linear object model generation unit 56 generates the catenary curve 90 as described above (see FIG. 12).
　次のステップＳ１３２で線状物モデル生成部５６は、上述したように、カテナリ曲線９０に垂直な平面９２を一定間隔で設ける（図１２参照）。 In the next step S132, the linear object model generation unit 56 provides planes 92 perpendicular to the catenary curve 90 at regular intervals, as described above (see FIG. 12).
　次のステップＳ１３４で線状物モデル生成部５６は、上述したように、平面９２上に存在する３次元点１４群を円フィッティングし、その円の半径と中心座標を導出する（図１２参照）。 In the next step S134, the linear object model generation unit 56 performs circle fitting on the group of 14 three-dimensional points existing on the plane 92, as described above, and derives the radius and center coordinates of the circle (see FIG. 12). .
　次のステップＳ１３６で保存制御部４６は、上述したように、線状物モデル３６をストレージ３４に格納する。 In the next step S136, the storage control unit 46 stores the linear object model 36 in the storage 34, as described above.
　次のステップＳ１３８で表示制御部４８は、上述したように、線状物モデル３６を表示部３７に表示させる。ステップＳ１３６の処理が終了すると、図１３に示した線状物検出処理が終了する。図１４Ａには、本実施形態の線状物検出装置３０によって生成された線状物モデル３６の一例を示す。また、図１４Ｂには、図１４Ａに示した線状物モデル３６の生成に用いた３次元点１４群の一例を示す。図１４Ａによれば、本実施形態の線状物検出装置３０によれば、線状物を精度良く検出することができることがわかる。 In the next step S138, the display control unit 48 causes the linear object model 36 to be displayed on the display unit 37, as described above. When the process of step S136 ends, the linear object detection process 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. Further, FIG. 14B shows an example of a group of 14 three-dimensional points used to generate the linear object model 36 shown in FIG. 14A. According to FIG. 14A, it can be seen that the linear object detection device 30 of this embodiment can detect linear objects with high accuracy.
　以上説明したように、本実施形態の線状物検出装置３０は、構造物の表面上の点における３次元座標を表す３次元点１４群について、３次元座標に基づいて直線６４を検出し、検出した１つの直線６４に係わる３次元点１４同士の距離に応じて直線６４に係わる３次元点１４群を分割し、分割した３次元点１４群毎に３次元座標に基づいて再度、直線６４を検出する。まあ、線状物検出装置３０は検出した複数の直線６４と地面とのなす角度、及び直線６４の方位に基づいて、直線６４をグルーピングし、同一グループに含まれる直線６４について、直線６４同士を結んだ線分８２と、線分８２によって結ばれた直線６４とのなす角度が閾値以内である直線６４同士を、同一の構造物に対応する直線６４として分類する。 As explained above, the linear object detection device 30 of this embodiment detects the straight line 64 based on the three-dimensional coordinates of the group of 14 three-dimensional points representing the three-dimensional coordinates of points on the surface of the structure, The 14 groups of 3-dimensional points related to the straight line 64 are divided according to the distance between the 3-dimensional points 14 related to one detected straight line 64, and the straight line 64 is divided again based on the 3-dimensional coordinates for each group of 14 divided 3-dimensional points. Detect. Well, the linear object detection device 30 groups the straight lines 64 based on the angles formed between the plurality of detected straight lines 64 and the ground, and the direction of the straight lines 64, and distinguishes between the straight lines 64 among the straight lines 64 included in the same group. Straight lines 64 in which the angle between the connected line segment 82 and the straight line 64 connected by the line segment 82 is within a threshold value are classified as straight lines 64 corresponding to the same structure.
　このように、本実施形態の線状物検出装置３０によれば、線状物の検出を行うため、３次元座標を表す３次元点群が高密度であっても、線状物を精度良く検出することができる。 In this way, according to the linear object detection device 30 of this embodiment, in order to detect linear objects, even if the three-dimensional point group representing the three-dimensional coordinates is highly dense, the linear object detection device 30 can accurately detect the linear objects. can be detected.
　また、上記各実施形態でＣＰＵがソフトウェア（プログラム）を読み込んで実行した各種処理を、ＣＰＵ以外の各種のプロセッサが実行してもよい。この場合のプロセッサとしては、ＦＰＧＡ（Ｆｉｅｌｄ－Ｐｒｏｇｒａｍｍａｂｌｅ　Ｇａｔｅ　Ａｒｒａｙ）等の製造後に回路構成を変更可能なＰＬＤ（Ｐｒｏｇｒａｍｍａｂｌｅ　Ｌｏｇｉｃ　Ｄｅｖｉｃｅ）、及びＡＳＩＣ（Ａｐｐｌｉｃａｔｉｏｎ　Ｓｐｅｃｉｆｉｃ　Ｉｎｔｅｇｒａｔｅｄ　Ｃｉｒｃｕｉｔ）等の特定の処理を実行させるために専用に設計された回路構成を有するプロセッサである専用電気回路等が例示される。また、位置推定処理を、これらの各種のプロセッサのうちの１つで実行してもよいし、同種又は異種の２つ以上のプロセッサの組み合わせ（例えば、複数のＦＰＧＡ、及びＣＰＵとＦＰＧＡとの組み合わせ等）で実行してもよい。また、これらの各種のプロセッサのハードウェア的な構造は、より具体的には、半導体素子等の回路素子を組み合わせた電気回路である。 Further, various processes that the CPU reads and executes software (programs) in the above embodiments may be executed by various processors other than the CPU. The processor in this case is a PLD (Programmable Logic Device) whose circuit configuration can be changed after manufacturing, such as an FPGA (Field-Programmable Gate Array), and an ASIC (Application Specific Intel). In order to execute specific processing such as egrated circuit) An example is a dedicated electric circuit that is a processor having a specially designed circuit configuration. Furthermore, 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 type or different types (for example, a combination of multiple FPGAs, and a combination of a CPU and an FPGA). etc.). Further, the hardware structure of these various processors is, more specifically, an electric circuit that is a combination of circuit elements such as semiconductor elements.
　また、上記各実施形態では、線状物検出プログラム３５がストレージ３４に予め記憶（インストール）されている態様を説明したが、これに限定されない。線状物検出プログラム３５は、ＣＤ－ＲＯＭ（Ｃｏｍｐａｃｔ　Ｄｉｓｋ　Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）、ＤＶＤ－ＲＯＭ（Ｄｉｇｉｔａｌ　Ｖｅｒｓａｔｉｌｅ　Ｄｉｓｋ　Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）、及びＵＳＢ（Ｕｎｉｖｅｒｓａｌ　Ｓｅｒｉａｌ　Ｂｕｓ）メモリ等の非一時的（ｎｏｎ－ｔｒａｎｓｉｔｏｒｙ）記憶媒体に記憶された形態で提供されてもよい。また、線状物検出プログラム３５は、ネットワークを介して外部装置からダウンロードされる形態としてもよい。 Furthermore, in each of the above embodiments, a mode has been described in which the linear object detection program 35 is stored (installed) in the storage 34 in advance, but the present invention is not limited to this. The linear object detection program 35 is a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital Versatile Disk Read Only Memory), and a USB (Universal Disk Read Only Memory). Non-transitory storage such as Serial Bus) memory It may also be provided in a form stored on a medium. Furthermore, the linear object detection program 35 may be downloaded from an external device via a network.
　以上の実施形態に関し、更に以下の付記を開示する。 Regarding the above embodiments, the following additional notes are further disclosed.
　（付記項１）
　メモリと、
　前記メモリに接続された少なくとも１つのプロセッサと、
　を含み、
　前記プロセッサは、
　構造物の表面上の点における３次元座標を表す３次元点群について、３次元座標に基づいて直線を検出し、検出した１つの前記直線に係わる３次元点同士の距離に応じて前記直線に係わる３次元点群を分割し、分割した３次元点群毎に３次元座標に基づいて再度、直線を検出し、
　検出した複数の直線と地面とのなす角度、及び前記直線の方位に基づいて、前記直線をグルーピングし、同一グループに含まれる各直線について、直線同士を結んだ線分と、前記線分によって結ばれた直線とのなす角度が閾値以内である前記直線同士を、同一の構造物に対応する直線として分類する、
　ように構成されている線状物検出装置。 (Additional note 1)
memory and
at least one processor connected to the memory;
including;
The processor includes:
For a 3D point group representing 3D coordinates of points on the surface of a structure, a straight line is detected based on the 3D coordinates, and the straight line is adjusted according to the distance between the 3D points related to one of the detected straight lines. Divide the related 3D point group, detect the straight line again based on the 3D coordinates for each divided 3D point group,
The straight lines are grouped based on the angles formed between the plurality of detected straight lines and the ground, and the orientation of the straight lines, and each straight line included in the same group is connected by a line segment connecting the straight lines to each other by the line segment. classifying the straight lines whose angles with the straight line formed within a threshold value as straight lines corresponding to the same structure;
A linear object detection device configured as follows.
　（付記項２）
　線状物検出処理を実行するようにコンピュータによって実行可能なプログラムを記憶した非一時的記憶媒体であって、
　前記線状物検出処理は、
　構造物の表面上の点における３次元座標を表す３次元点群について、３次元座標に基づいて直線を検出し、検出した１つの前記直線に係わる３次元点同士の距離に応じて前記直線に係わる３次元点群を分割し、分割した３次元点群毎に３次元座標に基づいて再度、直線を検出し、
　検出した複数の直線と地面とのなす角度、及び前記直線の方位に基づいて、前記直線をグルーピングし、同一グループに含まれる各直線について、直線同士を結んだ線分と、前記線分によって結ばれた直線とのなす角度が閾値以内である前記直線同士を、同一の構造物に対応する直線として分類する、
　非一時的記憶媒体。 (Additional note 2)
A non-temporary storage medium storing a program executable by a computer to execute a linear object detection process,
The linear object detection process includes:
For a 3D point group representing 3D coordinates of points on the surface of a structure, a straight line is detected based on the 3D coordinates, and the straight line is adjusted according to the distance between the 3D points related to one of the detected straight lines. Divide the related 3D point group, detect the straight line again based on the 3D coordinates for each divided 3D point group,
The straight lines are grouped based on the angles formed between the plurality of detected straight lines and the ground, and the orientation of the straight lines, and each straight line included in the same group is connected by a line segment connecting the straight lines to each other by the line segment. classifying the straight lines whose angles with the straight line formed within a threshold value as straight lines corresponding to the same structure;
Non-transitory storage medium.
２０　点群測定器
２２　スキャナ
３０　線状物検出装置
３１　ＣＰＵ
３２　ＲＯＭ
３３　ＲＡＭ
３４　ストレージ
３５　線状物検出プログラム
３６　線状物モデル
３７　表示部
３８　通信Ｉ／Ｆ
３９　バス
４０　読込部
４２ パラメータ設定部
４４　線状物モデル算出部
４６　保存制御部
４８　表示制御部
５０　分類部
５２　直線検出部
５４　分類部
５６　線状物モデル生成部 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 section 46 Storage control section 48 Display control section 50 Classification section 52 Straight line detection section 54 Classification section 56 Linear object model generation section

Claims (6)

  1. 　構造物の表面上の点における３次元座標を表す３次元点群について、３次元座標に基づいて直線を検出し、検出した１つの前記直線に係わる３次元点同士の距離に応じて前記直線に係わる３次元点群を分割し、分割した３次元点群毎に３次元座標に基づいて再度、直線を検出する直線検出部と、
    　検出した複数の直線と地面とのなす角度、及び前記直線の方位に基づいて、前記直線をグルーピングし、同一グループに含まれる各直線について、直線同士を結んだ線分と、前記線分によって結ばれた直線とのなす角度が閾値以内である前記直線同士を、同一の構造物に対応する直線として分類する分類部と、
    　を備えた線状物検出装置。     For a 3D point group representing 3D coordinates of points on the surface of a structure, a straight line is detected based on the 3D coordinates, and the straight line is adjusted according to the distance between the 3D points related to one of the detected straight lines. a straight line detection unit that divides the related three-dimensional point group and detects the straight line again based on the three-dimensional coordinates for each divided three-dimensional point group;
    The straight lines are grouped based on the angles formed between the plurality of detected straight lines and the ground, and the orientation of the straight lines, and each straight line included in the same group is connected by a line segment connecting the straight lines to each other by the line segment. a classification unit that classifies the straight lines that make an angle with the straight line within a threshold value as straight lines that correspond to the same structure;
    A linear object detection device equipped with
  2. 　前記直線同士を結んだ線分は、各直線の重心同士を結んだ線分である
    　請求項１に記載の線状物検出装置。     The linear object detection device according to claim 1, wherein the line segment connecting the straight lines is a line segment connecting the centers of gravity of each straight line.
  3. 　前記同一の構造物に対応する直線として分類された各直線に対応する３次点群から近似曲線を生成し、前記近似曲線に垂直な複数の平面を一定間隔で設け、前記平面上に存在する前記３次元点群を円フィッティングすることで得られた円の半径及び中心座標に基づいて、線状物を表す線状物モデルを生成する線状物モデル生成部をさらに備えた、
    　請求項１に記載の線状物検出装置。     An approximated curve is generated from a group of cubic points corresponding to each straight line classified as a straight line corresponding to the same structure, a plurality of planes perpendicular to the approximated curve are provided at regular intervals, and a plurality of planes exist on the plane. Further comprising a linear object model generation unit that generates a linear object model representing the linear object based on the radius and center coordinates of the circle obtained by circular fitting the three-dimensional point group.
    The linear object detection device according to claim 1.
  4. 　前記分類部は、前記複数の直線と地面とのなす角度、及び前記構造物の種類に応じたなす角度の範囲に基づいて前記複数の直線を分類し、同一の種類に属する各直線の方位が同一とみなせるか否かにより前記同一の分類に属する前記直線をグルーピングする、
    　請求項１に記載の線状物検出装置。     The classification unit classifies the plurality of straight lines based on the angles formed between the plurality of straight lines and the ground, and the range of angles formed according to the type of structure, and determines the orientation of each straight line belonging to the same type. Grouping the straight lines belonging to the same classification depending on whether they can be considered to be the same.
    The linear object detection device according to claim 1.
  5. 　直線検出部が、構造物の表面上の点における３次元座標を表す３次元点群について、３次元座標に基づいて直線を検出し、検出した１つの前記直線に係わる３次元点同士の距離に応じて前記直線に係わる３次元点群を分割し、分割した３次元点群毎に３次元座標に基づいて再度、直線を検出し、
    　分類部が、検出した複数の直線と地面とのなす角度、及び前記直線の方位に基づいて、前記直線をグルーピングし、同一グループに含まれる各直線について、直線同士を結んだ線分と、前記線分によって結ばれた直線とのなす角度が閾値以内である前記直線同士を、同一の構造物に対応する直線として分類する、
    　線状物検出方法。     The straight line detection unit detects straight lines based on the three-dimensional coordinates of the three-dimensional point group representing the three-dimensional coordinates of points on the surface of the structure, and calculates the distance between the three-dimensional points related to one of the detected straight lines. dividing the three-dimensional point group related to the straight line accordingly, and detecting the straight line again based on the three-dimensional coordinates for each divided three-dimensional point group,
    The classification unit groups the straight lines based on the angles formed between the plurality of detected straight lines and the ground and the orientation of the straight lines, and for each straight line included in the same group, segments connecting the straight lines, and Classifying the straight lines whose angles with straight lines connected by line segments are within a threshold value as straight lines corresponding to the same structure;
    Linear object detection method.
  6. 　コンピュータを請求項１に記載の線状物検出装置の各部として機能させるための線状物検出プログラム。 A linear object detection program for causing a computer to function as each part of the linear object detection device according to claim 1.
PCT/JP2022/028848 2022-07-26 2022-07-26 Linear object detection device, linear object detection method, and linear object detection program WO2024023950A1 (en)

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JP2015001901A (en) * 2013-06-17 2015-01-05 日本電信電話株式会社 Point group analysis processing apparatus, point group analysis processing method and program
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