JP6013962B2 - Feature surface detection apparatus, feature surface detection method, and program - Google Patents

Description

  The present invention relates to a feature surface detection device and feature surface detection for detecting a surface of a slope along a road or along a road along which vegetation has grown, based on three-dimensional coordinate data of a point cloud extracted from the feature surface by a laser scanner. The present invention relates to a method and a program.

  River embankments are mainly composed of soil. In general, the top edge is used as a river management passage, and gravel or asphalt pavement is applied as necessary. On the other hand, the slope is usually turfed to prevent the surface from collapsing. By the way, weeding management is carried out about once or twice a year in order to prevent weeds from growing and the slope weakening, or for regular inspection of the levee.

  The river embankment should be managed so that erosion due to running water, breakage due to infiltration of water, and overflow phenomenon due to land subsidence do not occur. In this management, it is effective to monitor the change of the dike shape.

  Here, as a technique for measuring the shape of a feature, Patent Document 1 discloses a technique for acquiring three-dimensional point cloud data representing the shape of a feature using a laser scanner. For example, in a mobile mapping system, three-dimensional point cloud data representing the shape of a feature along a road is acquired using a laser scanner mounted on a vehicle. In this system, a laser scanner mounted on an automobile irradiates a laser obliquely downward or obliquely upward from the top of the vehicle body. The optical axis of the laser is scanned in the horizontal direction, and laser pulses are emitted at every minute angle within the scanning angle range. The distance is measured based on the time from the laser emission to the reception of the reflected light, and at that time, the laser emission direction, time, and the position / posture of the vehicle body are measured. From these measurement data, point group data representing the three-dimensional coordinates of the point reflecting the laser pulse is obtained.

JP 2009-204615 A

  Conventionally, in order to read a feature on the basis of point cloud data, a manual operation is required, and a feature extraction operation is manually performed using an editing tool or the like in a three-dimensional CAD. For this reason, there is a problem that it takes time to acquire point cloud data from the dike and extract the dike shape based on the point cloud data. In particular, laser reflection due to vegetation such as slopes has a problem of generating point clouds that cause noise when extracting a dike shape.

  The present invention provides a feature surface detection device, a feature surface detection method, which automatically and accurately detects the shape of a feature surface on which vegetation has flourished based on three-dimensional coordinate data of a point cloud extracted from the feature surface, And to provide a program.

  A feature surface detection apparatus according to the present invention detects a feature surface on which vegetation existing in a target space is proliferated based on three-dimensional coordinate data of a point cloud extracted from the feature surface by a laser scanner. Point cloud extraction means for virtually setting a cutting plane that is a vertical plane in the target space, and extracting a point cloud of interest that is located within a preset proximity distance from the cutting plane among the point cloud; Boundary search means for searching for a boundary line formed by the feature surface in the cutting plane based on a projected point group which is a projection image of the point group onto the cutting plane, and the boundary search The means divides the cutting plane in a grid shape along the horizontal direction and the vertical direction to set a plurality of unit areas, and each vertical row of the unit areas in the cutting plane from bottom to top In order to examine the projection Lower limit unit area extraction means for extracting the unit area in which a group is first found as a lower limit unit area, and feature point extraction for extracting the lowest point among the projected point groups in the lower limit unit area as a feature point And a boundary determining means for obtaining, as the boundary line, a broken line having the feature points for each vertical column of the unit area as nodes.

  The feature surface detection apparatus according to another aspect of the present invention further includes an additional feature that extracts, as an additional feature point, a point having a height lower than a provisional boundary line formed by a line segment that connects the adjacent feature points among the projected point group. It has point extraction means, and the boundary determination means obtains a broken line having the feature points and the additional feature points as nodes as the boundary lines.

  In the feature surface detection apparatus according to still another aspect of the present invention, the additional feature point extraction unit divides the provisional boundary line into a plurality of sections between the adjacent feature points. The additional feature point is searched for in a search region in which the upper side is set to a height corresponding to the highest point of the provisional boundary line in the section.

  The feature surface detection method according to the present invention is a method for detecting a feature surface overgrown with vegetation existing in a target space, based on three-dimensional coordinate data of a point cloud extracted from the feature surface by a laser scanner. A point group extraction step of virtually setting a cutting plane which is a vertical plane in the target space, and extracting a point group of interest located within a preset proximity distance from the cutting plane among the point group; A boundary search step for searching a boundary formed by the feature surface in the cutting plane based on a projected point group that is a projection image of the point group onto the cutting plane, and the boundary searching step A unit region setting step for setting the plurality of unit regions by dividing the cutting plane in a grid shape along the horizontal direction and the vertical direction, and each vertical row of the unit regions in the cutting plane in order from the bottom to the top To A lower limit unit region extracting step for extracting the unit region in which the projected point group is first found as a lower limit unit region, and a point having the lowest height among the projected point groups in the lower limit unit region as a feature point A feature point extracting step for extracting, and a boundary determining step for obtaining, as the boundary, a broken line having the feature point at each node in the vertical column of the unit region.

  The program according to the present invention causes a computer to perform data analysis for detecting a feature surface on which a vegetation existing in a target space is proliferated based on three-dimensional coordinate data of a point cloud extracted from the feature surface by a laser scanner. A cutting plane that is a vertical plane in the target space, and a point of interest group that is located within a preset proximity distance from the cutting plane among the point group. Boundary search for searching for a boundary formed by the feature surface in the cutting plane based on a point group extracting means for extraction and a projected point group that is a projection image of the point of interest group on the cutting plane The boundary search means is configured to divide the cutting plane into a lattice shape along a horizontal direction and a vertical direction to set a plurality of unit areas; A lower limit unit area extracting means for examining each vertical row of the unit areas in the plane in order from the bottom to the top, and extracting the unit area in which the projection point group is first found as a lower limit unit area; and within the lower limit unit area Feature point extracting means for extracting the lowest point of the projected point group as a feature point, and boundary determining means for obtaining a broken line having the feature point for each vertical column of the unit area as the boundary. .

  According to the present invention, the shape of the feature surface can be automatically and accurately detected based on the three-dimensional coordinate data of the point group extracted from the feature surface.

1 is a block diagram showing a schematic configuration of a feature surface detection system according to an embodiment of the present invention. It is a typical perspective view which shows the cutting plane set to the crossing direction of a dike. It is a general | schematic flowchart of the embankment surface shape extraction process by the feature surface detection system which concerns on embodiment of this invention. It is a schematic diagram which shows an example of the shape of the embankment in a cutting plane, and arrangement | positioning of a unit area | region. It is a schematic diagram which shows an example of distribution of the projection point group in a part of embankment. It is a processing flow figure of boundary detection of a dyke surface by a feature surface detection system concerning an embodiment of the present invention. It is a schematic diagram of a part of a cutting plane showing an example of the lower limit unit region. It is a schematic diagram of a part of a cutting plane showing an example of feature points. It is a schematic diagram of a part of a cutting plane showing an example of setting a search area. It is a schematic diagram of a part of a cutting plane showing an example of a boundary line.

  Hereinafter, a feature surface detection system 2 according to an embodiment of the present invention (hereinafter referred to as an embodiment) will be described with reference to the drawings. This system detects, for example, the surface shape of a river bank based on point cloud data representing the three-dimensional shape of the feature surface. The point cloud data is acquired by, for example, a laser scanner mounted on a vehicle traveling on the ground like the above-described mobile mapping system. In addition, measurement may be performed by installing a laser scanner on the ground. In order for the point cloud data to represent the three-dimensional shape of the feature surface, it is necessary to perform laser scanning at a density corresponding to the unevenness of the feature surface and the scale of the step. In this regard, the scanning density, distance accuracy, and footprint size of laser scanning performed from the height of a vehicle, tripod, etc., for example, have specifications that can capture the shape of a feature with an accuracy of several centimeters. In addition, it is possible to detect the deformation of the slope of a river dike with higher accuracy than with an aerial laser or satellite laser.

  FIG. 1 is a block diagram showing a schematic configuration of the feature surface detection system 2. The system includes an arithmetic processing device 4, a storage device 6, an input device 8, and an output device 10. As the arithmetic processing device 4, it is possible to make dedicated hardware for performing various arithmetic processing of this system, but in this embodiment, the arithmetic processing device 4 uses a computer and a program executed on the computer. Built.

  A CPU (Central Processing Unit) of the computer constitutes the arithmetic processing unit 4 and functions as a point group extraction unit 20 and a boundary search unit 22 described later. The boundary search means 22 includes a unit area setting means 30, a lower limit unit area extraction means 32, a feature point extraction means 34, an additional feature point extraction means 36, and a boundary determination means 38.

  The storage device 6 is composed of a hard disk or the like built in the computer. The storage device 6 stores a program for causing the arithmetic processing unit 4 to function as the point cloud extraction unit 20 and the boundary search unit 22, and other programs, and various data necessary for processing of the present system. For example, the storage device 6 stores point cloud data of the analysis target space as the processing target data. The feature surface detection system 2 is basically used by setting a space where a dike exists as an analysis target space. In particular, the feature surface detection system 2 is effective for detecting the surface of a portion where vegetation exists, such as a slope of a river bank.

  The input device 8 is a keyboard, a mouse, or the like, and is used for a user to operate the system.

  The output device 10 is a display, a printer, or the like, and is used for showing the user the three-dimensional shape or cross-sectional shape of the bank obtained by the present system by screen display, printing, or the like.

  As will be described later, the basic process for extracting the surface shape of the levee in the present embodiment is a process for detecting the boundary line (cross-sectional shape) of the feature surface that appears on the cutting plane virtually set in the target space. The three-dimensional shape of the levee can be grasped by setting a plurality of cutting planes and analyzing each of them.

  In the feature surface detection system 2, the method of setting the cutting plane is arbitrary, but in the present embodiment, a case where the cutting plane is set in the crossing direction of the levee will be described as an example. FIG. 2 is a schematic perspective view showing this case. For example, when the user sets the reference line 50 along the transverse direction of the embankment 52, the cutting plane 54 is set to a vertical plane passing through the reference line 50. The reference line 50 can be automatically set by the feature surface detection system 2. Further, for example, when the user specifies parameters such as the initial position, interval, number of reference lines 50, and analysis range in the longitudinal direction of the dike, the feature surface detection system 2 is configured to use a plurality of reference lines based on the specified conditions. It is also possible to adopt a configuration in which the analysis is performed by automatically setting the items sequentially. For example, the mobile mapping system can acquire a laser point cloud while traveling on a top road. Therefore, based on the vehicle trajectory acquired by the mobile mapping system, a center line along the longitudinal direction is set at the top, and this is engraved at a certain distance (pitch) to generate a reference line 50 in the transverse direction. Thus, the processing for detecting the cross-sectional shape can be performed continuously or independently. Here, in addition to engraving at a predetermined distance, it may be engraved based on the value of a distance marker (kilo post). In addition, an existing longitudinal center line may be used instead of the center line based on the traveling locus of the vehicle. Here, if the horizontal direction along the reference line is the X axis and the vertical direction is the Y axis, the cutting plane is the XY plane, and the Z axis is set in a direction perpendicular to the cutting plane. The positive direction of the Y axis is set upward.

  FIG. 3 is a schematic flow diagram of the dike surface shape extraction process by the feature surface detection system 2. Each means of the arithmetic processing unit 4 will be described with reference to FIG.

  The point group extraction means 20 virtually sets a cutting plane 54 in the target space, and extracts a point group of interest located within a preset neighborhood distance from the cutting plane 54 out of the point group. Specifically, the point cloud extraction unit 20 sets a space having a dimension (depth) D in the Z-axis direction around the cutting plane 54 (hereinafter referred to as a focused partial space), and the focused point group from the focused partial space. Is extracted (S10). Incidentally, in FIG. 2, the horizontal section 56 of the target partial space is schematically represented by a rectangle.

  The boundary search means 22 searches for a boundary line 58 formed by the surface of the embankment 52 in the cutting plane 54 based on a projected point group that is a projection image of the point of interest group on the cutting plane 54.

  The unit area setting means 30 divides the cutting plane 54 in the horizontal direction and the vertical direction, and sets a rectangular area (unit area) having sides along the X and Y axes (S12). FIG. 4 is a schematic diagram for explaining the unit region, and shows an example of the shape of the embankment 52 (boundary line 58) and the arrangement of the unit regions 70 in the cutting plane 54 (XY plane). FIG. 5 is a schematic diagram showing an example of the distribution of the projected point group 60 in a part of the bank 52. The cutting plane 54 is partitioned in an orthogonal lattice (mesh) shape, and the unit regions 70 form a two-dimensional array with the X direction and the Y direction as the array directions. In this embodiment, the unit region is set to have a square size (width W) and a Y-axis direction size (height H), and the unit region is set to a square. In the program constituting the point group extraction unit 20 and the boundary search unit 22, the width W, the height H, and the depth D are parameterized, and can be changed by the user using the input device 8, for example.

  Incidentally, the mesh in FIG. 4 is drawn coarsely for the sake of illustration. On the other hand, FIG. 5 shows an example of actual projected point groups and meshes. In the example of FIG. 5, the unit region 70 is a square having a width W and a height H of 30 cm.

  The lower limit unit region extraction means 32 selects the unit region 70 in order from the bottom to the top for each vertical column of the unit region 70 on the cutting plane 54, and examines the presence or absence of a projection point group in the unit region 70. Then, the lower limit unit region extraction unit 32 extracts, as the lower limit unit region, the unit region in which the projection point group is first found for each vertical row of the unit regions 70 (S14).

  The feature point extracting means 34 extracts the point having the lowest height from the projected point group in each lower limit unit area as a feature point (S16).

  The additional feature point extraction unit 36 obtains a broken line composed of line segments connecting adjacent feature points as a temporary boundary line (S18), and adds a point whose height is lower than the temporary boundary line among the projected points. A point is extracted (S20).

  The boundary determining unit 38 obtains a broken line having the feature point extracted by the feature point extracting unit 34 and the additional feature point extracted by the additional feature point extracting unit 36 as a boundary line (S22).

  Hereinafter, the processing of the feature surface detection system 2 will be described in more detail. FIG. 6 is a schematic process flow diagram of the levee surface detection by the feature surface detection system 2, and shows the process of detecting the boundary line of the levee surface that appears on the cutting plane.

  The point group extraction means 20 virtually sets a cutting plane 54 in the target space, and uses a point group in the target subspace having a dimension D in the Z-axis direction around the cutting plane 54 as a target point group. The data is fetched from the storage device 6 into a work area such as a RAM (Random Access Memory) of the arithmetic processing device 4 (S30).

The unit area setting means 30 divides the cutting plane 54 into a mesh shape and divides it into unit areas 70. For example, when the size of the subspace of interest is set, the number of unit areas arranged in each of the X and Y directions is determined according to the unit area sizes W and H. For example, the position of the unit area in each direction An index range indicating is determined. Here, representing X, the index of each _I X in the Y direction, _{I Y,} also _{I X} range _{1 to N} X of the range of _{I Y} and 1 to N _Y. Here, I _Y is set to increase in the positive direction of the Y axis, that is, in order from the bottom to the top.

The lower limit unit region extraction means 32 initializes the value of the index _IX that designates the vertical column of the unit region to 0 (S32), increments _IX, and selects the vertical column of the unit region as the processing target one by one. (S36).

The lower limit unit region extraction unit 32 initializes the value of the index I _Y to 0 when starting the processing of each column (S38). Then, the lower limit unit region extraction means 32 increments I _Y and selects unit regions one by one from the bottom in order (S42), and the unit region including the projected point group obtained by projecting the point of interest onto the XY plane. The lower limit unit area located at the bottom is searched. Specifically, it is checked whether or not there is a projection point in the selected unit area (S44). If there is no projection point (in the case of “No” in S46), one unit is sequentially increased. A region is selected (S42). On the other hand, when there is a projection point in the selected unit area (“Yes” in S46), the unit area becomes the lower limit unit area.

  FIG. 7 is a schematic view of a part of the cutting plane 54 showing an example of the lower limit unit region. FIG. 7 shows an example of the unit area 70 and the projected image (projection point group 80) of the target point group in the k-th column to the (k + 3) -th column, and each unit region column with respect to the projection point group 80 is shown. The lower limit unit area 82 extracted in this manner is indicated by a bold rectangle.

Feature point extraction unit 34 searches the projection point located at the bottom at lower unit area, is extracted as the feature point P of the unit region sequence of the I _X column are the projection point as the processing target (I _X) (S48). When the feature point P (I _X ) is found, the lower limit unit region extraction means 32 starts processing the next unit region sequence (S36). Note that if the projection point is not found up to the unit area of I _Y = N _Y , that is, the upper end of the unit area sequence (“Yes” in S40), the process proceeds to the next unit area sequence (S36).

  FIG. 8 is a schematic diagram of a part of the cutting plane 54 showing the feature points P (k) to P (k + 3) extracted corresponding to the example of FIG. When the feature point search / extraction process is completed for all the unit region columns (in the case of “Yes” in S34), a broken line having the feature point as a node is set as a provisional boundary line 84 (S50).

  The additional feature point extraction means 36 extracts a projected point located below the provisional boundary line as an additional feature point (S52, S54). Specifically, the additional feature point extraction unit 36 can perform the processes S50 to S54 for each line segment constituting the provisional boundary line. The lower limit unit region extraction unit 32 sequentially selects feature point pairs that are both ends of the line segment constituting the provisional boundary line, that is, a pair of feature points adjacent to each other in the X direction (S50), and between the selected two feature points. Is divided into a plurality of sections in the X direction, and a search area for additional feature points is set for each section (S52). The search area is set to a rectangular area composed of a side along the X direction and a side along the Y direction, and the upper side is set to a height corresponding to the highest point of the provisional boundary line in the section.

FIG. 9 is a schematic diagram of a part of the cutting plane 54 showing an example of setting a search area, and corresponds to the examples of FIGS. 7 and 8. For example, the additional feature point extraction unit 36 sets a section by dividing the feature points at a predetermined interval from the side with the smaller X coordinate (left side in the figure). In this case, the rightmost section between feature points can be smaller than other sections. For example, the feature points P (k) and P (k + 1) are divided by points Q _{1 to} Q ₄ on the provisional boundary line, and five sections [P (k), Q ₁ ], [Q ₁ , Q ₂ ] , [Q ₂ , Q ₃ ], [Q ₃ , Q ₄ ], [Q ₄ , P (k + 1)] are set. A search area 90 is set corresponding to each of these sections. For example, since Q ₁ is the highest point in the section [Q ₁ , Q ₂ ], the height of the upper side of the search area 90 in the section can be basically set to the height of Q ₁ . The purpose of this setting is not to exclude the area below the provisional boundary from which additional feature points are extracted, but to set the search area so as not to include as many point clouds as possible due to the vegetation existing above the surface of the dike. This is to improve the processing efficiency.

  The height of the upper side is preferably matched with the highest point of the section for the purpose, but it may be set to a height that deviates from the highest point within a range in which the influence on the purpose is allowed. it can. Further, from the above purpose, the upper side of the search area does not necessarily have to be horizontal, and is preferably set along the provisional boundary line, and may be configured as such. On the other hand, as described above, the configuration with the upper side having a constant Y coordinate in the section simplifies the processing because there is no need to calculate the height of the upper side according to the position in the X direction.

  The height of the lower side of the search area 90 is, for example, the lower one of the feature points P (k) and P (k + 1) for each section between the feature points P (k) and P (k + 1). It can be arbitrarily set below the height of P (k + 1). This is because the feature point is the lowest point in the lower limit unit region, and there is no point lower than P (k + 1) between the feature points P (k) and P (k + 1), and the additional feature This is because the point exists only higher than the height of P (k + 1).

  In the present embodiment, the dimension in the Y direction of the search area 90 is uniformly set for each line segment of the provisional boundary line and for each section of the line segment, thereby simplifying the process. Therefore, the height of the lower side of the search area 90 shown in FIG. 9 is set sufficiently lower than the lower height of each of the provisional boundary lines. The required dimension in the Y direction of the search area 90 in this setting can be determined based on the assumed slope of the dike surface and the width W of the unit area. For example, since the slope of the riverbank slope is determined to be 50% or less in principle, the required dimension in the Y direction of the search area 90 is usually sufficient to be about half the width W of the unit area.

  In the search for additional feature points between feature points P (k) and P (k + 1), the X coordinate corresponding to the unit region sequence from which P (k), which is the higher of both feature points, is extracted. In this range, the search for additional feature points may be omitted. This is because in this range, P (k) is the lowest point and there is no point lower than the provisional boundary.

  The additional feature point extracting means 36 searches for a projection point having a height lower than the provisional boundary within the search region set as described above, and uses the obtained projection point as an additional feature point (S54). For example, in the example of FIG. 9, the point 92 is an additional feature point.

  The boundary determination unit 38 uses the feature points and additional feature points extracted by the feature point extraction unit 34 and the additional feature point extraction unit 36 as X-axis as boundary lines of the levee surface on the cutting plane 54 set by the point group extraction unit 20. A connected broken line is generated in accordance with the order of the directions (S56). FIG. 10 is a schematic view of a part of the cutting plane 54 showing an example of the boundary line 58, and corresponds to the examples of FIGS.

  For example, the boundary determination unit 38 registers the obtained boundary line data in the storage device 6 in association with the position / direction of the reference line 50 (or the cutting plane 54) (S58).

  The arithmetic processing unit 4 obtains boundary lines at the plurality of cutting planes 54 and accumulates them in the storage device 6. Then, image data representing a three-dimensional shape or a cross-sectional shape of the bank is generated based on the accumulated data of a plurality of boundary lines, and is output to a display, a printer, or the like that is the output device 10.

  In the above-described embodiment, the broken line including the feature point and the additional feature point at the node is obtained as the boundary line. However, the broken line including only the feature point at the node may be obtained as the boundary line.

  The feature surface detection system 2 extracts the feature cross-sectional shape with a resolution of about the width W of the unit region in the direction along the reference line 50. The unit region sequence is set so as to include a large number of point groups as in the example shown in FIG. 5, and the lowest point of the large number of point groups is extracted as a feature point for each unit region sequence. Here, the laser scanner cannot obtain a reflection point from the dike surface at an arbitrary position in the X-axis direction, and the lowest point of the point group at a certain X coordinate may be a reflection point from vegetation. . That is, even if the lowest point is extracted along the reference line to obtain the lower contour of the point group, the contour includes the reflection point of the vegetation as noise, and preferably represents the shape of the levee surface. It is not suitable for observing changes in the shape of the dike surface. On the other hand, it can be expected that the lowest point extracted for each unit region row of this system suitably indicates the height of the dike surface. In other words, the boundary line generated by this system is not easily affected by noise caused by vegetation. In addition, since the point cloud acquired by laser scanning from the ground has a higher density than aviation lasers and satellite lasers, the width W of the unit region can be made relatively small. Therefore, according to the present system, it is possible to obtain the shape of the levee surface that is less affected by noise and whose height is expressed with high accuracy, and to detect deformation of the levee with high accuracy. .

  Moreover, although the above-mentioned embodiment demonstrated the slope of a river embankment as an object, it is not limited to this, For example, it can apply also to the slope of the roadside along which the vegetation grew.

  2 feature surface detection system, 4 arithmetic processing device, 6 storage device, 8 input device, 10 output device, 20 point group extraction means, 22 boundary search means, 30 unit area setting means, 32 lower limit unit area extraction means, 34 Point extraction means, 36 additional feature point extraction means, 38 boundary determination means, 50 reference line, 52 levee, 54 cutting plane, 58 boundary line.

Claims (5)

A feature surface detection device for detecting a feature surface overgrown with vegetation existing in a target space based on three-dimensional coordinate data of a point cloud extracted from the feature surface by a laser scanner,
A point group extracting means for virtually setting a cutting plane which is a vertical plane in the target space, and extracting a point group of interest located within a preset proximity distance from the cutting plane among the point group;
Boundary search means for searching for a boundary line formed by the feature surface in the cutting plane based on a projection point group that is a projection image of the point of interest group on the cutting plane;
The boundary search means includes
Unit region setting means for setting the plurality of unit regions by dividing the cutting plane into a lattice shape along the horizontal direction and the vertical direction;
A lower limit unit region extracting unit that sequentially examines each vertical row of the unit regions in the cutting plane from bottom to top, and extracts the unit region in which the projection point group is first found as a lower limit unit region;
A feature point extracting means for extracting a point having the lowest height among the projected point groups in the lower limit unit region as a feature point;
Boundary determination means for obtaining a broken line having the feature point for each vertical row of the unit area as the boundary line;
A feature surface detection device comprising: The feature surface detection apparatus according to claim 1,
An additional feature point extracting means for extracting, as an additional feature point, a point whose height is lower than a provisional boundary line consisting of a line segment connecting adjacent feature points among the projected point group;
The boundary determining means determines a broken line having the feature point and the additional feature point as nodes as the boundary line;
Feature surface detection device characterized by In the feature surface detection apparatus according to claim 2,
The additional feature point extracting means divides the temporary boundary line into a plurality of sections between the adjacent feature points, and is a rectangular area for each section, the upper side of which is the highest point of the temporary boundary line in the section Searching for the additional feature point in a search area set to a height according to
Feature surface detection device characterized by A feature surface detection method for detecting a feature surface overgrown with vegetation existing in a target space based on three-dimensional coordinate data of a point cloud extracted from the feature surface by a laser scanner,
A point group extraction step of virtually setting a cutting plane that is a vertical plane in the target space, and extracting a point group of interest located within a preset proximity distance from the cutting plane among the point group;
A boundary search step for searching for a boundary formed by the feature surface in the cutting plane based on a projected point group that is a projection image of the point of interest group on the cutting plane;
The boundary search step includes:
A unit area setting step for setting a plurality of unit areas by dividing the cutting plane in a lattice shape along a horizontal direction and a vertical direction;
A lower limit unit region extracting step of examining each vertical row of the unit regions in the cutting plane in order from bottom to top, and extracting the unit region where the projection point group is first found as a lower limit unit region;
A feature point extracting step of extracting a point having the lowest height from among the projected point group in the lower limit unit region; and
A boundary determination step for determining a broken line having the feature point at each node in the vertical row of the unit area as the boundary;
A feature surface detection method characterized by comprising: A program for causing a computer to perform data analysis for detecting a feature surface on which vegetation existing in a target space is proliferated based on three-dimensional coordinate data of a point cloud extracted from the feature surface by a laser scanner, The computer
A point plane extracting means for virtually setting a cutting plane which is a vertical plane in the target space, and extracting a point group of interest located within a preset proximity distance from the cutting plane among the point group; and
Based on a projection point group that is a projection image of the target point group onto the cutting plane, function as boundary search means for searching for a boundary formed by the feature surface in the cutting plane,
The boundary search means includes
Unit region setting means for setting the plurality of unit regions by dividing the cutting plane into a lattice shape along the horizontal direction and the vertical direction;
A lower limit unit region extracting unit that sequentially examines each vertical row of the unit regions in the cutting plane from bottom to top, and extracts the unit region in which the projection point group is first found as a lower limit unit region;
A feature point extracting means for extracting a point having the lowest height among the projected point groups in the lower limit unit region as a feature point;
Boundary determination means for obtaining a broken line having the feature point at each node as a boundary for each vertical row of the unit region;
A program comprising:

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