Bridge data extraction device, bridge data extraction method, and program

Abstract

To provide a bridge data extraction device, a bridge data extraction method, and a program that precisely extract point group data representing a bridge from point group data representing an object region.SOLUTION: A bridge data extraction device has: a determination unit which determines that, when a direction of points determined using a point included in object region point group data representing an object region including the bridge and a plurality of points included in a range previously set around the point is considered to be horizontal, the points are lower-layer part point group data representing a bridge pier or bridge abutment of the bridge; and a selection unit which performs division processing based upon a distance between points included in the object region point group data, and selects bridge point group data representing the bridge based upon the division result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technical field relates to a bridge data extraction device, a bridge data extraction method, and a program for extracting point cloud data representing a bridge. [Background technology]

[0002] Non-Patent Document 1 discloses a technology for generating a 3D model of a bridge using point cloud data generated by a ground-based laser scanner and point cloud data generated from images captured by an autonomous unmanned aerial vehicle. The technology in Non-Patent Document 1 also discloses a function for removing noise from the point cloud data. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Yoshinori Tsukada et al., "Study on generating 3D bridge models using point cloud data," Intelligence and Information, vol. 27, No. 5, pp. 796-812, 2015. Summary of the Invention [Problem to be solved by the invention]

[0004] However, Non-Patent Document 1 only discloses a function for removing noise such as outliers caused by moving objects such as people and measurement errors.In other words, it does not disclose a function for accurately extracting point cloud data of a bridge from point cloud data obtained by measuring a target area (space) that contains a bridge and natural objects and structures other than the bridge that exist around the bridge.

[0005] One aspect of the present invention is to provide a bridge data extraction device, a bridge data extraction method, and a program that accurately extract point cloud data representing a bridge from point cloud data representing a target area. [Means for solving the problem]

[0006] In order to achieve the above object, a bridge data extraction device in one aspect includes: a determination unit that, when the direction of a point determined using a point included in target area point cloud data representing a target area including a bridge and a plurality of points included in a preset range around the point can be considered to be horizontal, determines that the point is lower layer point cloud data representing a pier or abutment of the bridge; a selection unit that executes a division process based on distances between points included in the target area point cloud data, and selects bridge point cloud data representing the bridge based on the division result; The present invention is characterized by having the following.

[0007] In order to achieve the above object, a bridge data extraction method in one aspect includes: The computer If the direction of a point determined using a point included in target area point cloud data representing a target area including a bridge and a plurality of points included in a predetermined range around the point can be considered to be horizontal, the point is determined to be lower layer point cloud data representing a pier or abutment of the bridge, A division process is performed based on the distances between points included in the target area point cloud data, and bridge point cloud data representing the bridge is selected based on the division result. It is characterized by:

[0008] Furthermore, in order to achieve the above object, the program in one aspect comprises: On the computer, If the direction of a point determined using a point included in target area point cloud data representing a target area including a bridge and a plurality of points included in a predetermined range around the point can be considered to be horizontal, the point is determined to be lower layer point cloud data representing a pier or abutment of the bridge, A division process is performed based on the distances between points included in the target area point cloud data, and bridge point cloud data representing the bridge is selected based on the division result. It is characterized by: [Effects of the Invention]

[0009] As one aspect, point cloud data representing a bridge can be extracted with high accuracy from point cloud data representing a target area. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram for explaining an example of a bridge data extraction device according to the first embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of a system including the bridge data extraction device according to the first embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of a target area from which point cloud data is acquired. [Figure 4] FIG. 4 is a diagram for explaining horizontal determination. [Figure 5] FIG. 5 is a diagram for explaining an example of the operation of the bridge data extraction device in the first embodiment. [Figure 6] FIG. 6 is a diagram illustrating an example of the operation of the determination process. [Figure 7] FIG. 7 is a diagram illustrating an example of the operation of the selection process. [Figure 8] FIG. 8 is a diagram illustrating an example of a bridge data extraction device according to the second embodiment. [Figure 9] FIG. 9 is a diagram for explaining an example of the operation of the bridge data extraction device in the second embodiment. [Figure 10] FIG. 10 is a diagram illustrating an example of a bridge data extraction device according to the third embodiment. [Figure 11] FIG. 11 is a diagram for explaining an example of the operation of the bridge data extraction device according to the third embodiment. [Figure 12] FIG. 12 is a diagram illustrating an example of a bridge data extraction device according to the fourth embodiment. [Figure 13] FIG. 13 is a diagram for explaining an example of the operation of the bridge data extraction device according to the fourth embodiment. [Figure 14]FIG. 14 is a diagram illustrating an example of a computer that realizes the bridge data extraction device according to the first to fourth embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments will be described with reference to the drawings. In the drawings described below, elements having the same or corresponding functions are denoted by the same reference numerals, and repeated description thereof may be omitted.

[0012] (Embodiment 1) The configuration of a bridge data extraction device 10 according to the first embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram for explaining an example of the bridge data extraction device according to the first embodiment.

[0013] [Device configuration] The bridge data extraction device 10 shown in Fig. 1 is a device that accurately extracts point cloud data representing a bridge from point cloud data representing a target area. Also, as shown in Fig. 1, the bridge data extraction device 10 has a determination unit 11 and a selection unit 12.

[0014] The determination unit 11 determines that a point is lower layer point cloud data representing a pier or abutment of a bridge when the direction of the point determined using a point included in the target area point cloud data representing the target area including the bridge and multiple points included in a predetermined range around the point can be considered to be horizontal (when the horizontal direction is determined to be dominant).

[0015] Both piers and abutments have sides that can be considered perpendicular to the horizontal plane. Therefore, the direction determined by a point included in the lower part point cloud data representing the lower part of the bridge (pier or abutment) and multiple points near that point, such as the direction of the normal, can be considered to be horizontal. Therefore, by determining whether the direction of the normal is a direction that can be considered horizontal, it can be determined that a point included in the target area point cloud data is included in the lower part point cloud data.

[0016] The selection unit 12 performs a division process (a process of dividing the target area point cloud data into groups with similar characteristics) based on the distances between points included in the target area point cloud data, and selects bridge point cloud data representing bridges based on the division results. This division process can also be said to be a process of grouping multiple points in the target area point cloud data that can be determined to be located close to each other into one group. In this case, "close" refers to the result of a relative comparison of the distances between points included in the target area point cloud data.

[0017] Specifically, the selection unit 12 performs a clustering process based on the distances between points included in the target area point cloud data to generate clusters, selects a cluster that has points included in the lower layer point cloud data, and determines that the points included in the selected cluster are included in the bridge point cloud data representing a bridge.

[0018] Since bridge point cloud data corresponding to the lower part of the bridge is obtained using clusters that have been clustered based on the distances between points included in the target area point cloud data and the lower part point cloud data, point cloud data of natural objects, structures, etc. other than the bridge can be removed. As a result, bridge point cloud data corresponding to the lower part of the bridge can be extracted with high accuracy.

[0019] [System Configuration] The configuration of the bridge data extraction device 10 according to the first embodiment will be described in detail with reference to Fig. 2. Fig. 2 is a diagram illustrating an example of a system 200 having the bridge data extraction device according to the first embodiment.

[0020] The system 200 includes a bridge data extraction device 10, a sensor 20, and a storage device 30. The bridge data extraction device 10 includes an acquisition unit 13, a direction calculation unit 14, a determination unit 11, and a selection unit 12.

[0021] The bridge data extraction device 10 is, for example, an information processing device such as a CPU (Central Processing Unit), a programmable device such as an FPGA (Field-Programmable Gate Array), a GPU (Graphics Processing Unit), or a circuit equipped with one or more of these, a server computer, a personal computer, or a mobile terminal.

[0022] The sensor 20 measures the target area and generates target area point cloud data using the measurement results. The sensor 20 measures an area having a preset angle of view.

[0023] Specifically, the sensor 20 measures a measurable range and generates point cloud data using the measurement results. The sensor 20 is, for example, a Light Detection and Ranging (LiDAR) sensor.

[0024] The LiDAR sensor may also be equipped with an inclinometer, which measures the vertical direction and the tilt of an object relative to the vertical direction.

[0025] The LiDAR sensor is assumed to be installed horizontally using an inclinometer in advance. For example, if the LiDAR sensor is supported by a tripod, the length of the tripod legs is adjusted to install the LiDAR sensor horizontally. However, the sensor 20 is not limited to a LiDAR sensor.

[0026] Alternatively, instead of a LiDAR sensor, point cloud data may be generated using an imaging device and an image processing device. In this case, first, multiple images of the target area are captured by the imaging device. Next, the multiple captured images are processed using Structure from Motion (SfM) software installed in the image processing device to generate point cloud data of the target area. However, the generation of point cloud data is not limited to the above-described process.

[0027] In the example of FIG. 2, the sensor 20 is provided outside the bridge data extraction device 10, but it may also be provided inside the bridge data extraction device 10.

[0028] The target area point cloud data will now be described. Fig. 3 is a diagram illustrating an example of a target area from which point cloud data is acquired. In the example of Fig. 3, a bridge 3, natural objects 4, structures 5, and a sensor 20 exist on the ground surface 2 in a target area 1 (target space). However, the target area is not limited to the target area 1 shown in Fig. 3.

[0029] Although the ground surface 2 is not actually flat, it is assumed to be flat for the sake of clarity. In addition, the ground surface 2 may actually be inclined.

[0030] The bridge 3 has an upper layer 6 and a lower layer 7 (7a, 7b). The upper layer 6 is composed of components such as girders and deck plates. The lower layer 7 (7a, 7b) is a pier or abutment. In Figure 3, the upper layer 6 and the lower layer 7 are used for convenience to make the structure of the bridge 3 easier to understand.

[0031] In the example of Figure 3, the natural object 4 is a plant. In the example of Figure 3, the structure 5 is a medium- to high-rise building. However, the type, size, number, position, etc. of the natural object 4 and the structure 5 are not limited to the example of Figure 3.

[0032] Point cloud data is data that represents a set of multiple points in a three-dimensional coordinate system. When the sensor 20 shown in Fig. 3 is a LiDAR sensor, the point cloud data is represented, for example, in a three-dimensional coordinate system with the position of the sensor 20 as the origin.

[0033] In the example of Fig. 3, the three-dimensional coordinate system represents the horizontal direction with the x-axis and y-axis, and the vertical direction with the z-axis. Specifically, the x-axis represents the horizontal direction in Fig. 3, and the y-axis represents the depth direction in Fig. 3. In addition, in the example of Fig. 3, the z-axis is perpendicular (vertical) to the horizontal plane. In addition, when the three-dimensional coordinate system is represented by the x-axis, y-axis, and z-axis, the point cloud data represents the position of a point using x-coordinate values, y-coordinate values, and z-coordinate values.

[0034] However, the point cloud data may be expressed using, for example, voxel data, depth images, etc. Furthermore, the point cloud data may be expressed using something other than the above-mentioned three-dimensional coordinate system, voxel data, and depth images.

[0035] In the following description, the three-dimensional coordinate system has its origin at the position where the sensor 20 is installed, the x-axis and y-axis represent the horizontal direction, and the z-axis represents the vertical direction.

[0036] Furthermore, the LiDAR sensor does not necessarily have to be installed horizontally. This is because even if the LiDAR sensor is not installed horizontally, it can be converted into a coordinate system in which the x-axis and y-axis represent the horizontal direction and the z-axis represents the vertical direction by performing coordinate conversion processing. A commonly known process is used for the coordinate conversion processing.

[0037] The storage device 30 stores bridge point cloud data, which is the result of extracting point cloud data representing bridges from the target area point cloud data. In addition to the bridge point cloud data, the storage device 30 may also store, for example, data generated during the process of acquiring the bridge point cloud data, target area point cloud data, etc.

[0038] The storage device 30 is, for example, a database, a server computer, a personal computer, or the memory of a mobile terminal.

[0039] In the example of FIG. 2, the storage device 30 is provided outside the bridge data extraction device 10, but it may also be provided inside the bridge data extraction device 10.

[0040] The bridge data extraction device will now be described in detail. The acquisition unit 13 acquires point cloud data (target area point cloud data P) obtained by measuring a part or all of the lower layer 7, and outputs the acquired target area point cloud data P to the direction calculation unit .

[0041] The acquisition unit 13 may acquire the target area point cloud data P in real time, or may acquire the target area point cloud data P stored in a storage device such as the storage device 30.

[0042] Furthermore, the target area point cloud data P may be an integration of a plurality of point cloud data measured by changing the measurement position of the sensor 20.

[0043] The target area point cloud data P can be expressed as in Equation 1. In Equation 1, each point included in the target area point cloud data P is represented as pi. The position (three-dimensional coordinates) of point pi is represented as (xi, yi, zi).

[0044] (Number 1) P={pi,i=1,2,3,…,N} pi=(xi,yi,zi)

[0045] The direction calculation unit 14 uses the target area point cloud data P to calculate a direction to be set for each point pi included in the target area point cloud data P. Specifically, first, the direction calculation unit 14 acquires the target area point cloud data P.

[0046] Next, the direction calculation unit 14 calculates the direction for each point pi included in the target area point cloud data P. The direction calculation unit 14 calculates a normal using, for example, point pi included in the target area point cloud data P and multiple points in the vicinity of point pi (multiple points around the target point). The vicinity is a range set in advance around the target point. The vicinity may be a range in which the distance from a certain point satisfies a criterion for determining that the point is in the vicinity. The criterion is, for example, that the distance is shorter than a predetermined threshold.

[0047] When the points in the target area point cloud data P are interpolated using a spline curve or the like, the normal line may be the normal direction of the spline curve.

[0048] The direction set for a point may be expressed not only as the normal direction but also as a direction tilted at a predetermined angle from the normal direction toward the y-axis. The method of expressing the direction at a point is not limited to the method using the normal line.

[0049] Next, the direction calculation unit 14 adds information (direction information) representing the direction calculated for each point pi to each point pi of the target area point cloud data P. Note that, hereinafter, information obtained by adding direction information to the target area point cloud data P shown in Equation 1 will be represented as target area point cloud data P' as shown in Equation 2.

[0050] (Number 2) P´={p´i,i=1,2,3,…,N} p´i=(xi,yi,zi,vector(Ni))

[0051] The information p'i in Equation 2 is included in the target area point cloud data P'. Also, vector(Ni) is information to which directional information calculated for each point pi is added. Vector(Ni) represents, for example, a normal unit vector.

[0052] In the following, the processing will be explained using a normal unit vector, but the normal direction does not necessarily have to be a unit vector. If the vector is not a unit vector, the processing described below can be realized by comparing the magnitude of the horizontal component of the vector with the magnitude of the vertical component.

[0053] The determination unit will now be described. The determination unit 11 includes a horizontal determination unit 15 and a size determination unit 16 .

[0054] The horizontal determination unit will now be described. The horizontal determination unit 15 selects point cloud data corresponding to the lower layer part (lower layer point cloud data C) based on the direction information of each piece of information p'i of the target area point cloud data P' to which direction information has been added.

[0055] Specifically, first, the horizontal determination unit 15 acquires the target area point cloud data P'. Next, the horizontal determination unit 15 selects lower layer point cloud data C representing the lower layer 7 based on the direction information of the target area point cloud data P'.

[0056] For example, if the direction information is a normal unit vector, the horizontal determination unit 15 calculates the magnitude of the horizontal component of the normal unit vector (the horizontal component represented by the x-axis and y-axis in Figure 3), and selects a point representing the lower layer 7 from the points included in the target area point cloud data P' based on the magnitude of the calculated horizontal component.

[0057] In detail, the horizontal determination unit 15 determines that a target point included in the target area point cloud data P' is a candidate for the lower layer 7 if the magnitude of the horizontal component of the normal unit vector of the target point is greater than a predetermined horizontal threshold.

[0058] The side of a pier or abutment is likely to have a vertical side, as in the lower layers 7a and 7b shown in Figure 3. In such cases, in the lower layers 7a and 7b, the horizontal component of the normal unit vector of the target point will be more dominant than the vertical component (the horizontal component will be larger), so it is possible to determine whether the target point is a candidate for a point on the side of the pier or abutment.

[0059] The horizontal threshold is a value used to determine whether a point corresponds to the side of a pier or abutment. The horizontal threshold can be determined through experiments and simulations. For example, the horizontal threshold can be set to 0.5.

[0060] Further, the lower layer point cloud data C can be expressed by Equation 3. The Horizontal function shown in Equation 3 is a function that calculates the horizontal component from the normal unit vector.

[0061] (Number 3) C={p´i|Horizontal(vector(Ni))>i that satisfies the Horizontal Threshold}

[0062] Since the lower layer point cloud data C is a subset of the target area point cloud data P, new flag information may be added as a field of the target area point cloud data P to distinguish points that correspond to the lower layer point cloud data C.

[0063] The size determination unit will now be described. The size determination unit 16 performs a clustering process based on the distances between points included in the lower layer point cloud data C to generate clusters, and reselects the lower layer point cloud data C based on the points belonging to the generated clusters to generate lower layer point cloud data C'.

[0064] Specifically, first, the size determination unit 16 performs, for example, Euclidean clustering processing on the points included in the lower layer point cloud data C to generate clusters.

[0065] Next, for each generated cluster, the size determination unit 16 compares the number of points belonging to the cluster with a preset size threshold, and then selects a cluster in which the number of points belonging to the cluster is greater than the size threshold.

[0066] The size threshold is determined based on the size of the side of the pier or abutment, for example, by experiment, simulation, or the like.

[0067] The above-mentioned process of selecting a cluster by comparing the number of points belonging to the cluster with a size threshold is a process of removing points located away from the lower layer 7 from the lower layer point cloud data C. In other words, it is a process of removing points (noise) such as natural objects 4 and structures 5 other than the lower layer 7 from the lower layer point cloud data C. In other words, it is a process of extracting points corresponding to the lower layer 7.

[0068] Therefore, by performing a process of selecting a cluster by comparing the number of points belonging to the cluster with a size threshold, points corresponding to the lower layer 7 can be extracted from the lower layer point cloud data C with high accuracy.

[0069] Fig. 4 is a diagram for explaining horizontal determination. In the example of Fig. 4, the lower layer portions 7a and 7b are each represented as a rectangular prism. In addition, in the example of Fig. 4, it is assumed that the side surfaces of the lower layer portions 7a and 7b are parallel to the z-axis.

[0070] Arrow nv1 in FIG. 4 represents the normal unit vector of a point on the side of lower layer 7a. Arrow nv2 in FIG. 4 represents the normal unit vector of a point on the side of lower layer 7b. Arrow nv3 in FIG. 4 represents the normal unit vector of a point on the floorboard of upper layer 6. Arrow nv4 in FIG. 4 represents the normal unit vector of a point on natural object 4. Arrow nv5 in FIG. 4 represents the normal unit vector of a point on structure 5. Arrow nv6 in FIG. 4 represents the normal unit vector of a point on ground surface 2.

[0071] Furthermore, when the horizontal components of the normal unit vectors of the arrows nv1 to nv6 are calculated, in the example of FIG. 4, the arrows nv1, 2, and 5 are oriented horizontally, so the magnitude of the horizontal component becomes the size threshold.

[0072] Furthermore, in addition to the above-described process of selecting a cluster by comparing the number of points belonging to the cluster with a size threshold, a cluster may be selected by the following process.

[0073] For example, the size determination unit 16 selects only clusters that are made up of points within a preset area range.

[0074] The process of selecting only clusters consisting of points within a preset area range, as described above, is a process of determining that points within a certain range of size are the lower layer among the lower layer point cloud data C. For example, if the size of the bridge pier is known in advance, this is a process of removing points (noise) such as natural objects 4 and structures 5 other than the lower layer 7 based on the number of points.

[0075] Instead of the number of points in a cluster, a mesh may be formed using points belonging to the cluster, and the area of ​​the formed mesh may be used.

[0076] Next, the size determination unit 16 uses the points included in the selected cluster to generate lower layer point cloud data C'. However, the processing of the size determination unit 16 described above does not necessarily have to be performed.

[0077] The selection unit will now be described. The selection unit 12 includes a height restriction unit 17 , a clustering unit 18 , and a cluster selection unit 19 .

[0078] The height limiting unit 17 performs preprocessing on the target area point cloud data P or the target area point cloud data P′ based on height information, which will be described later, and extracts point cloud data Q to be used in the clustering unit 18.

[0079] Specifically, first, the height limiting unit 17 acquires the target area point cloud data P and the lower layer point cloud data C (when the size determining unit 16 is not present), or the target area point cloud data P and the lower layer point cloud data C' (when the size determining unit 16 is present). Next, the height limiting unit 17 generates a height threshold (height information) based on the lower layer point cloud data C or the lower layer point cloud data C'.

[0080] The height threshold is the largest z coordinate value among the points included in the lower layer point cloud data C or the lower layer point cloud data C'. The height threshold may be set in advance based on the height of the pier or abutment.

[0081] Next, the height limiting unit 17 extracts points whose z coordinate values ​​are equal to or greater than a height threshold from among the points included in the target area point cloud data P or the target area point cloud data P'. Thereafter, the height limiting unit 17 sets the extracted points as point cloud data Q to be used by the clustering unit 18.

[0082] It is not necessary to execute the processing of the height limiting unit 17. In that case, the clustering unit 18 uses the lower layer point cloud data C or the lower layer point cloud data C′ as the point cloud data Q.

[0083] The clustering unit 18 executes clustering processing using the point cloud data Q. Specifically, the clustering unit 18 first acquires the point cloud data Q.

[0084] Next, the clustering unit 18 uses the points included in the point cloud data Q to perform Euclidean clustering processing based on the distances between the points to generate clusters.

[0085] The cluster Bj obtained by the clustering process executed on the point cloud data Q is expressed as in Equation 4.

[0086] (Number 4) Bj (j=1,2,3,…,M)

[0087] The cluster selection unit 19 selects a cluster in which at least one point belonging to the cluster Bj is included in the lower layer point cloud data C or the lower layer point cloud data C'. Then, the cluster selection unit 19 generates point cloud data B (bridge point cloud data) using the points belonging to the selected cluster.

[0088] [Device operation] Next, the operation of the bridge data extraction device in embodiment 1 will be described with reference to Fig. 5. Fig. 5 is a diagram for explaining the operation of the bridge data extraction device in embodiment 1. In the following description, the diagram will be referenced as appropriate. Furthermore, in embodiment 1, a bridge data extraction method is implemented by operating the bridge data extraction device. Therefore, the description of the bridge data extraction method in embodiment 1 will be replaced by the following description of the operation of the bridge data extraction device.

[0089] As shown in FIG. 5, first, the acquisition unit 13 acquires point cloud data (target area point cloud data P) measured from part or all of the lower layer 7, and outputs the acquired target area point cloud data P to the direction calculation unit 14 (step A1).

[0090] Next, the direction calculation unit 14 uses the target area point cloud data P to calculate, for each point pi included in the target area point cloud data P, a direction to be set for each point pi (step A2).

[0091] Specifically, in step A2, first, the direction calculation unit 14 acquires the target area point cloud data P. Next, in step A2, the direction calculation unit 14 calculates the direction for each point pi included in the target area point cloud data P.

[0092] Next, if the direction set for each point pi can be regarded as the horizontal direction (if the horizontal direction is determined to be dominant), the judgment unit 11 judges that the point pi is included in the lower layer point cloud data C (step A3: judgment process).

[0093] Next, the selection unit 12 performs a division process (a process of dividing into groups with similar characteristics) based on the distances between points included in the target area point cloud data P, and selects bridge point cloud data representing bridges based on the division results (step A4: selection process).

[0094] The determination process (step A3) will now be described in detail. 6 is a diagram for explaining an example of the operation of the determination process. As shown in Fig. 6, first, the horizontal determination unit 15 selects lower layer point cloud data C corresponding to the lower layer 7 based on the directional information of each piece of information p'i of the target area point cloud data P' to which directional information has been added (step B1).

[0095] Specifically, in step B1, first, the horizontal determination unit 15 acquires the target area point cloud data P'. Next, in step B1, the horizontal determination unit 15 selects lower layer point cloud data C representing the lower layer 7 based on the direction information of the target area point cloud data P'.

[0096] For example, if the direction information is a normal unit vector, the horizontal determination unit 15 calculates the magnitude of the horizontal component of the normal unit vector (the horizontal component represented by the x-axis and y-axis in Figure 3), and based on the magnitude of the calculated horizontal component, selects a point representing the lower layer 7 from the points included in the target area point cloud data P'.

[0097] In detail, the horizontal determination unit 15 determines that a target point included in the target area point cloud data P' is a candidate for the lower layer 7 if the magnitude of the horizontal component of the normal unit vector of the target point is greater than a predetermined horizontal threshold.

[0098] The side of a pier or abutment is likely to have a vertical side, as shown in the lower layers 7a and 7b in Figure 3. In such cases, in the lower layers 7a and 7b, the horizontal component of the normal unit vector of the target point is more dominant than the vertical component (the horizontal component becomes larger), so the target point can be selected as a candidate for the side of the pier or abutment.

[0099] Next, the size determination unit 16 performs a clustering process based on the distances between points included in the lower layer point cloud data C to generate clusters, and reselects the lower layer point cloud data C based on the points belonging to the clusters to generate lower layer point cloud data C' (step B2).

[0100] Specifically, in step B2, first, the size determination unit 16 performs, for example, Euclidean clustering processing on the points included in the lower layer point cloud data C to generate clusters.

[0101] Next, in step B2, the size determination unit 16 compares the number of points belonging to each generated cluster with a preset size threshold, and selects a cluster in which the number of points belonging to the cluster is greater than the size threshold.

[0102] Next, in step B2, the size determination unit 16 sets the points included in the selected cluster as the lower layer point cloud data C'. However, the processing of step B2 described above does not necessarily have to be executed.

[0103] The above-mentioned process of selecting a cluster by comparing the number of points belonging to the cluster with a size threshold is a process of removing points located away from the lower layer 7 from the lower layer point cloud data C. In other words, it is a process of removing points (noise) such as natural objects 4 and structures 5 other than the lower layer 7 from the lower layer point cloud data C. In other words, it is a process of extracting points corresponding to the lower layer 7.

[0104] Therefore, by performing a process of selecting a cluster by comparing the number of points belonging to the cluster with a size threshold, points corresponding to the lower layer 7 can be extracted from the lower layer point cloud data C with high accuracy.

[0105] Furthermore, in addition to the above-described process of selecting a cluster by comparing the number of points belonging to the cluster with a size threshold, a cluster may be selected by the following process.

[0106] For example, the size determination unit 16 selects only clusters that are made up of points within a preset area range.

[0107] The process of selecting only clusters consisting of points within a preset area range, as described above, is a process of determining that points within a certain range of size are the lower layer among the lower layer point cloud data C. For example, if the size of the bridge pier is known in advance, this is a process of removing points (noise) such as natural objects 4 and structures 5 other than the lower layer 7 based on the number of points.

[0108] Instead of the number of points in a cluster, a mesh may be formed using points belonging to the cluster, and the area of ​​the formed mesh may be used.

[0109] The selection process (step A4) will now be described in detail. Fig. 7 is a diagram for explaining an example of the operation of the selection process. As shown in Fig. 7, first, the height limiting unit 17 performs preprocessing on the target area point cloud data P or the target area point cloud data P' based on height information, which will be described later, to extract point cloud data Q to be used in the clustering unit 18 (step C1).

[0110] Specifically, in step C1, the height limiting unit 17 first acquires the target area point cloud data P and the lower layer point cloud data C (if the size determination unit 16 is not present), or the target area point cloud data P and the lower layer point cloud data C' (if the size determination unit 16 is present).

[0111] Next, in step C1, the height limiting unit 17 generates a height threshold (height information) based on the lower layer point cloud data C or the lower layer point cloud data C'. The height threshold is the largest z coordinate value among the points included in the lower layer point cloud data C or the lower layer point cloud data C'. The height threshold may be set in advance based on the height of the pier or abutment.

[0112] Next, in step C1, the height limiting unit 17 extracts points whose z coordinate values ​​are equal to or greater than a height threshold from among the points included in the target area point cloud data P or the target area point cloud data P'.

[0113] Thereafter, in step C1, the height limiting unit 17 extracts point cloud data Q to be used in the clustering unit 18 using the extracted points.

[0114] It is not necessary to execute the processing of the height limiting unit 17. In that case, the clustering unit 18 uses the lower layer point cloud data C or the lower layer point cloud data C′ as the point cloud data Q.

[0115] Next, the clustering unit 18 executes a clustering process using the point cloud data Q (step C2).

[0116] Specifically, in step C2, the clustering unit 18 first acquires the point cloud data Q.

[0117] Next, in step C2, the clustering unit 18 uses the points included in the point cloud data Q to perform Euclidean clustering processing based on the distances between the points to generate clusters Bj.

[0118] Next, the cluster selection unit 19 selects a cluster in which at least one point belonging to cluster Bj is included in the lower layer point cloud data C or the lower layer point cloud data C', and sets the points belonging to the selected cluster as point cloud data B (bridge point cloud data) (step C3).

[0119] [Effects of the First Embodiment] According to embodiment 1, if the direction set for a point included in the target area point cloud data representing a target area including a bridge can be regarded as the horizontal direction (if the horizontal direction is determined to be dominant), it can be determined that the point is included in the lower level point cloud data representing the bridge pier or abutment.

[0120] In other words, each pier or abutment has a side that can be considered perpendicular to the horizontal plane. Therefore, the direction determined by a point included in the lower level point cloud data representing the pier or abutment and multiple points in its vicinity, such as the direction of the normal, is a direction that can be considered horizontal. Therefore, by using the direction of the normal, it can be determined that a point is included in the lower level point cloud data.

[0121] In addition, a division process (a process of dividing into groups with similar characteristics) is performed based on the distance between points contained in the target area point cloud data, and bridge point cloud data representing bridges can be selected based on the division results.

[0122] Specifically, clusters are generated by performing a clustering process based on the distances between points included in the target area point cloud data, a cluster having points included in the lower layer point cloud data is selected, and the points included in the selected cluster are used as bridge point cloud data representing the bridge.

[0123] Therefore, bridge point cloud data corresponding to the lower part of the bridge is obtained using clusters that have been clustered based on the distances between points included in the target area point cloud data and the lower part point cloud data, so it is possible to remove point cloud data that becomes noise, such as natural objects and structures other than the bridge.As a result, bridge point cloud data corresponding to the lower part of the bridge can be extracted with high accuracy.

[0124] (Embodiment 2) The configuration of a bridge data extraction device 80 according to the second embodiment will be described with reference to Fig. 8. Fig. 8 is a diagram for explaining an example of the bridge data extraction device according to the second embodiment.

[0125] [System Configuration] The bridge data extraction device 80 shown in Fig. 8 is a device that accurately extracts point cloud data representing a bridge from point cloud data representing a target area. As shown in Fig. 8, the bridge data extraction device 80 includes an acquisition unit 13, a direction calculation unit 14, a determination unit 11, a removal unit 81, and a selection unit 82.

[0126] The acquisition unit 13, the direction calculation unit 14, and the determination unit 11 have already been described in the first embodiment, so descriptions of the acquisition unit 13, the direction calculation unit 14, and the determination unit 11 will be omitted.

[0127] The removal unit 81 removes the lower layer point cloud data C or the lower layer point cloud data C' from the target area point cloud data P or the target area point cloud data P'.

[0128] Specifically, the removal unit 81 first acquires the target area point cloud data P or the target area point cloud data P' and the lower layer point cloud data C or the lower layer point cloud data C'.

[0129] Next, the removal unit 81 removes the lower layer point cloud data C or the lower layer point cloud data C' from the target area point cloud data P or the target area point cloud data P' to obtain point cloud data D (point cloud data that does not have a point cloud corresponding to the lower layer (bridge or abutment)). Next, the removal unit 81 outputs the point cloud data D to the selection unit 82.

[0130] The selection unit 82 performs a clustering process based on the distances between points included in the point cloud data D to generate clusters, and generates point cloud data B (bridge point cloud data) using the points belonging to the generated clusters and the points included in the lower level point cloud data C or the lower level point cloud data C'.

[0131] Specifically, the selection unit 82 first acquires the point cloud data D and the lower layer point cloud data C or the lower layer point cloud data C'. Next, the selection unit 82 performs Euclidean clustering processing based on the distances between points included in the point cloud data D to generate clusters.

[0132] Next, the selection unit 82 calculates the distance between each of the points belonging to the generated cluster and the points included in the lower layer point cloud data C or the lower layer point cloud data C'.

[0133] Next, the selection unit 82 compares the calculated distance with a preset distance threshold, and if the calculated distance is equal to or less than the distance threshold (if the distance is short), selects a cluster including points at a distance equal to or less than the distance threshold.The selection unit 82 then sets the points belonging to the selected cluster as point cloud data B (bridge point cloud data).

[0134] [Device operation] Next, the operation of the bridge data extraction device in the second embodiment will be described with reference to FIG. 9. FIG. 9 is a diagram for explaining an example of the operation of the bridge data extraction device in the second embodiment. In the following description, the diagram will be referenced as appropriate. Furthermore, in the second embodiment, a bridge data extraction method is implemented by operating the bridge data extraction device. Therefore, the description of the bridge data extraction method in the second embodiment will be replaced by the following description of the operation of the bridge data extraction device.

[0135] In the second embodiment, the processes of steps A1 to A3 and steps D1 to D2 shown in Fig. 9 are executed. The processes of steps A1 to A3 have already been explained, so explanation of the processes of steps A1 to A3 will be omitted.

[0136] As shown in FIG. 9, first, the removal unit 81 removes the lower layer point cloud data C or the lower layer point cloud data C' from the target area point cloud data P or the target area point cloud data P' (step D1).

[0137] Specifically, in step D1, first, the removal unit 81 acquires the target area point cloud data P or the target area point cloud data P' and the lower layer point cloud data C or the lower layer point cloud data C'.

[0138] Next, in step D1, the removal unit 81 removes the lower layer point cloud data C or the lower layer point cloud data C' from the target area point cloud data P or the target area point cloud data P' to generate point cloud data D (point cloud data that does not have a point cloud corresponding to the lower layer (bridge or abutment)).

[0139] Next, the selection unit 82 performs a clustering process based on the distances between points included in the generated point cloud data D to generate clusters (step D2).

[0140] Specifically, in step D2, the selection unit 82 first acquires the point cloud data D and the lower layer point cloud data C or the lower layer point cloud data C'. Next, in step D2, the selection unit 82 performs a clustering process based on the distances between points included in the point cloud data D to generate clusters.

[0141] Next, in step D2, the selection unit 82 calculates the distance between each of the points belonging to the generated cluster and the points included in the lower layer point cloud data C or the lower layer point cloud data C'.

[0142] Next, in step D2, the selection unit 82 compares the calculated distance with a preset distance threshold, and if the calculated distance is equal to or less than the distance threshold (if the distance is short), selects a cluster including points at a distance equal to or less than the distance threshold. Then, the selection unit 82 sets the points belonging to the selected cluster as point cloud data B (bridge point cloud data).

[0143] [Effects of the second embodiment] According to the second embodiment, by performing a clustering process based on the distances between points included in the point cloud data D obtained by removing the lower layer point cloud data from the target area point cloud data, even if the points of the upper layer 6 (girders and deck panels), the points of the ground surface 2, and the points of the lower layer 7 (7a, 7b) belong to the same cluster, the points of the upper layer 6 and the points of the ground surface 2 can be separated into different clusters. Therefore, even if point cloud data corresponding to the ground surface 2 exists, bridge point cloud data can be extracted with high accuracy.

[0144] (Embodiment 3) The configuration of the bridge data extraction device 100 according to the third embodiment will be described with reference to Fig. 10. Fig. 10 is a diagram for explaining an example of the bridge data extraction device according to the third embodiment.

[0145] [System Configuration] The bridge data extraction device 100 shown in Fig. 10 is a device that accurately extracts point cloud data representing a bridge from point cloud data representing a target area. As shown in Fig. 10, the bridge data extraction device 100 includes an acquisition unit 13, a direction calculation unit 14, a determination unit 11, a selection unit 12, and a bridge determination unit 101.

[0146] The acquisition unit 13, direction calculation unit 14, determination unit 11, and selection unit 12 have already been explained in the first and second embodiments, so explanations of the acquisition unit 13, direction calculation unit 14, determination unit 11, and selection unit 12 will be omitted.

[0147] If the direction of a point belonging to a cluster generated by performing a clustering process based on the distance between points included in point cloud data B can be considered to be vertical, the bridge determination unit 101 determines that the points included in the cluster to which the point belongs are bridge point cloud data (point cloud data A) representing a bridge.

[0148] Specifically, the bridge determination unit 101 first acquires point cloud data B. Next, the bridge determination unit 101 performs Euclidean clustering processing based on the distances between points included in the point cloud data B to generate clusters.

[0149] Note that the clustering process in the bridge determination unit 101 is the same as the clustering process in the selection unit 12, and therefore the cluster Bj generated in the selection unit 12 may be used.

[0150] Next, the bridge determination unit 101 determines whether a cluster corresponds to a bridge based on the normals of the points belonging to the cluster. The bridge determination unit 101 calculates the vertical component of the normals of the points belonging to the cluster, and determines whether the vertical component of the normals is greater than a preset vertical threshold. The vertical threshold can be set to, for example, 0.5.

[0151] Next, if the direction of the points belonging to the cluster can be regarded as the vertical direction, the bridge determination unit 101 determines the points included in the cluster to which the point belongs as bridge point cloud data (point cloud data A) representing a bridge.

[0152] [Device operation] Next, the operation of the bridge data extraction device in the third embodiment will be described with reference to FIG. 11. FIG. 11 is a diagram for explaining an example of the operation of the bridge data extraction device in the third embodiment. In the following description, the diagram will be referenced as appropriate. Furthermore, in the third embodiment, a bridge data extraction method is implemented by operating the bridge data extraction device. Therefore, the description of the bridge data extraction method in the third embodiment will be replaced by the following description of the operation of the bridge data extraction device.

[0153] In the third embodiment, the processes of steps A1 to A4 and step E1 shown in Fig. 11 are executed. The processes of steps A1 to A4 have already been explained, so the explanation of the processes of steps A1 to A4 will be omitted.

[0154] 11, first, the bridge determination unit 101 generates clusters by performing Euclidean clustering processing based on the distances between points included in point cloud data B. Next, if the direction of a point belonging to the generated cluster can be considered to be the vertical direction, the bridge determination unit 101 sets the points included in the cluster to which the point belongs as bridge point cloud data (point cloud data A) representing a bridge (step E1).

[0155] Specifically, in step E1, the bridge determination unit 101 first acquires point cloud data B. Next, in step E1, the bridge determination unit 101 executes Euclidean clustering processing based on the distances between points included in the point cloud data B to generate clusters.

[0156] Note that the clustering process in the bridge determination unit 101 is the same as the clustering process in the selection unit 12, and therefore the cluster Bj generated in the selection unit 12 may be used.

[0157] Next, in step E1, the bridge determination unit 101 determines whether a cluster corresponds to a bridge based on the normals of the points belonging to the cluster. The bridge determination unit 101 calculates the vertical components of the normals of the points belonging to the cluster, and determines whether the vertical components of the normals are greater than a preset vertical threshold.

[0158] Next, in step E1, if the direction of the points belonging to the cluster can be regarded as the vertical direction, the bridge determination unit 101 determines the points included in the cluster to which the point belongs as bridge point cloud data (point cloud data A) representing the bridge.

[0159] [Effects of the Third Embodiment] As shown in Figure 3, the bridge 3 has an upper layer 6 (girders and deck plates) supported by a lower layer 7 (piers or abutments). Also, the arrow nv3 representing the normal unit vector of the upper layer 6 (girders and deck plates) in Figure 4 has a large vertical component.

[0160] In this way, clusters having points with large horizontal components, such as arrows nv1 and nv2 representing the normal unit vector of the side of the lower layer 7, and clusters having points with large vertical components, such as arrow nv3 representing the normal unit vector of the upper layer 6, can be determined to be clusters representing bridges.

[0161] Therefore, bridge point cloud data including points of the upper layer 6 and the lower layer 7 can be extracted from the target area point cloud data.

[0162] (Embodiment 4) The configuration of the bridge data extraction device 120 according to the fourth embodiment will be described with reference to Fig. 12. Fig. 12 is a diagram for explaining an example of the bridge data extraction device according to the fourth embodiment.

[0163] [System Configuration] 12 is a device that accurately extracts point cloud data representing a bridge from point cloud data representing a target area. As shown in FIG. 12, the bridge data extraction device 120 includes an acquisition unit 13, a direction calculation unit 14, a determination unit 11, a removal unit 81, a selection unit 82, and a bridge determination unit 101.

[0164] The acquisition unit 13, direction calculation unit 14, judgment unit 11, removal unit 81, selection unit 82, and bridge judgment unit 101 have already been explained in the first, second, and third embodiments, so explanations of the acquisition unit 13, direction calculation unit 14, judgment unit 11, removal unit 81, selection unit 82, and bridge judgment unit 101 will be omitted.

[0165] [Device operation] Next, the operation of the bridge data extraction device in the fourth embodiment will be described with reference to FIG. 13. FIG. 13 is a diagram for explaining an example of the operation of the bridge data extraction device in the fourth embodiment. In the following description, the diagram will be referenced as appropriate. Furthermore, in the fourth embodiment, a bridge data extraction method is implemented by operating the bridge data extraction device. Therefore, the description of the bridge data extraction method in the fourth embodiment will be replaced by the following description of the operation of the bridge data extraction device.

[0166] In the fourth embodiment, the processes of steps A1 to A3, steps D1 to D2, and step E1 shown in Fig. 13 are executed. Since the processes of steps A1 to A3, steps D1 to D2, and step E1 have already been explained, explanation of the processes of steps A1 to A3, steps D1 to D2, and step E1 will be omitted.

[0167] [Effects of the fourth embodiment] According to the fourth embodiment, even when point cloud data corresponding to the ground surface 2 exists, bridge point cloud data can be extracted with high accuracy.

[0168] According to the fourth embodiment, bridge point cloud data including points of the upper layer 6 and the lower layer 7 can be extracted from the target area point cloud data.

[0169] [program] The program in the first embodiment may be any program that causes a computer to execute steps A1 to A4 shown in FIG. 5, steps B1 to B2 shown in FIG. 6, and steps C1 to C3 shown in FIG.

[0170] The program in the second embodiment may be any program that causes a computer to execute steps A1 to A3 and steps D1 to D2 shown in FIG.

[0171] The program in the third embodiment may be any program that causes a computer to execute steps A1 to A4 and step E1 shown in FIG.

[0172] The program in the fourth embodiment may be any program that causes a computer to execute steps A1 to A3, steps D1 to D2, and step E1 shown in FIG.

[0173] By installing the above-described program in a computer and executing it, the bridge data extraction device and the bridge data extraction method according to the first to fourth embodiments can be realized.

[0174] In the case of embodiment 1, the computer processor functions as an acquisition unit 13, a direction calculation unit 14, a judgment unit 11 (horizontal judgment unit 15, size judgment unit 16), and a selection unit 12 (height limitation unit 17, clustering unit 18, cluster selection unit 19), and performs processing.

[0175] In the case of the second embodiment, the processor of the computer functions as the acquisition unit 13, the direction calculation unit 14, the determination unit 11, the removal unit 81, and the selection unit 82, and performs the processes.

[0176] In the case of the third embodiment, the processor of the computer functions as the acquisition unit 13, the direction calculation unit 14, the determination unit 11, the selection unit 12, and the bridge determination unit 101, and performs processing.

[0177] In the case of the fourth embodiment, the processor of the computer functions as the acquisition unit 13, the direction calculation unit 14, the determination unit 11, the removal unit 81, the selection unit 82, and the bridge determination unit 101, and performs processing.

[0178] The programs in the first to fourth embodiments may be executed by a computer system constructed by a plurality of computers. In this case, for example, each computer may function as one of the acquisition unit 13, direction calculation unit 14, determination unit 11, selection unit 12, removal unit 81, selection unit 82, and bridge determination unit 101.

[0179] [Physical configuration] Here, a computer that realizes the bridge data extraction device by executing the programs according to the first to fourth embodiments will be described with reference to Fig. 14. Fig. 14 is a diagram for explaining an example of a computer that realizes the bridge data extraction device according to the first to fourth embodiments.

[0180] 14, the computer 110 includes a CPU 111, a main memory 112, a storage device 113, an input interface 114, a display controller 115, a data reader / writer 116, and a communication interface 117. These components are connected to each other via a bus 121 so as to be able to communicate data with each other. Note that the computer 110 may include a GPU or an FPGA in addition to or instead of the CPU 111.

[0181] The CPU 111 loads the programs (codes) in the embodiment stored in the storage device 113 into the main memory 112 and executes them in a predetermined order to perform various calculations. The main memory 112 is typically a volatile storage device such as a DRAM (Dynamic Random Access Memory). The programs in the embodiment are provided in a state stored in a computer-readable recording medium 122. The programs in the embodiment may be distributed over the Internet connected via the communication interface 117. The recording medium 122 is a non-volatile recording medium.

[0182] Specific examples of the storage device 113 include a hard disk drive and a semiconductor storage device such as a flash memory. The input interface 114 mediates data transmission between the CPU 111 and input devices 118 such as a keyboard and a mouse. The display controller 115 is connected to a display device 119 and controls the display on the display device 119.

[0183] The data reader / writer 116 mediates data transmission between the CPU 111 and the recording medium 122, reads programs from the recording medium 122, and writes processing results from the computer 110 to the recording medium 122. The communication interface 117 mediates data transmission between the CPU 111 and other computers.

[0184] Specific examples of the recording medium 122 include general-purpose semiconductor storage devices such as CF (Compact Flash (registered trademark)) and SD (Secure Digital), magnetic recording media such as flexible disks, or optical recording media such as CD-ROMs (Compact Disk Read Only Memory).

[0185] The bridge data extraction device in the embodiment can be realized by using hardware corresponding to each part, rather than a computer on which a program is installed. Furthermore, the bridge data extraction device may be realized in part by a program and in the remaining part by hardware.

[0186] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention. [Industrial Applicability]

[0187] According to the above description, point cloud data representing a bridge can be extracted with high accuracy from point cloud data representing a target area, and is useful in fields where bridge analysis is required. [Explanation of symbols]

[0188] 1. Target Area 2 Earth's surface 3 Bridges 4 natural objects 5 Structures 6. Upper Management 7, 7a, 7b Lower part 10, 80, 100, 120 Bridge data extractor 11 Judgment section 12, 82 Selection Department 13 Acquisition Department 14 Direction calculation unit 15 Horizontal determination section 16 Size determination section 17 Height limited section 18 Clustering Department 19 Cluster Selection Unit 20 sensors 30 Storage device 81 Removal part 101 Bridge Judgment Department 110 Computer 111 CPU 112 main memory 113 Storage device 114 Input Interface 115 Display Controller 116 Data Reader / Writer 117 Communication Interface 118 Input Devices 119 Display Device 121 Bus 122 Recording Media 200 systems

Claims

1. a determination means for determining that a point is lower layer point cloud data representing a pier or abutment of the bridge when the direction of the point determined using a point included in target area point cloud data representing a target area including a bridge and a plurality of points included in a preset range around the point can be regarded as horizontal; a selection means for executing a division process based on distances between points included in the target area point cloud data, and selecting bridge point cloud data representing the bridge based on the division result; A bridge data extraction device having the above.

2. The bridge data extraction device according to claim 1, When the direction is a normal unit vector, the determining means calculates the magnitude of a horizontal component of the normal unit vector, and determines whether the point is included in the lower layer point cloud data based on the calculated magnitude of the horizontal component. Bridge data extraction device.

3. 3. The bridge data extraction device according to claim 1 or 2, The determining means performs a clustering process based on distances between points included in the lower layer point cloud data to generate clusters, and generates the lower layer point cloud data based on points belonging to the generated clusters. Bridge data extraction device.

4. 4. The bridge data extraction device according to claim 1, The selection means performs a clustering process based on distances between points included in the target area point cloud data to generate clusters, selects a cluster including points included in the lower layer point cloud data, and defines the points included in the selected cluster as the bridge point cloud data. Bridge data extraction device.

5. 5. The bridge data extraction device according to claim 4, The determining means generates height information for limiting the points included in the target area point cloud data by height in the vertical direction based on the points included in the lower layer point cloud data, and extracts point cloud data to be used in the clustering process based on the height information. Bridge data extraction device.

6. 6. The bridge data extraction device according to claim 1, The selection means performs a clustering process based on distances between points included in the point cloud data obtained by removing the lower layer point cloud data from the target area point cloud data, generates clusters, selects the clusters based on points included in the lower layer point cloud data, and defines points belonging to the selected clusters as the bridge point cloud data. Bridge data extraction device.

7. 7. The bridge data extraction device according to claim 6, If the direction of a point belonging to a cluster generated by performing clustering processing based on the distance between points belonging to the selected cluster can be considered to be vertical, the points included in the cluster to which the point belongs are considered to be the bridge point cloud data. Bridge data extraction device.

8. The computer If the direction of a point determined using a point included in target area point cloud data representing a target area including a bridge and a plurality of points included in a predetermined range around the point can be considered to be horizontal, the point is determined to be lower layer point cloud data representing a pier or abutment of the bridge, A division process is performed based on the distances between points included in the target area point cloud data, and bridge point cloud data representing the bridge is selected based on the division result. Bridge data extraction method.

9. On the computer, If the direction of a point determined using a point included in target area point cloud data representing a target area including a bridge and a plurality of points included in a predetermined range around the point can be considered to be horizontal, the point is determined to be lower layer point cloud data representing a pier or abutment of the bridge, A division process is performed based on the distances between points included in the target area point cloud data, and bridge point cloud data representing the bridge is selected based on the division result. program.

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