JP5566353B2 - Data analysis apparatus, data analysis method, and program - Google Patents

Description

  The present invention relates to a data analysis apparatus, a data analysis method, and a program for detecting a wall surface of a feature based on point cloud data representing a three-dimensional shape of the feature surface.

  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, a laser scanner is mounted on the top of an automobile and irradiates the surrounding area with a laser. The optical axis of the laser is scanned in the vertical and horizontal directions by changing the elevation angle and azimuth angle, and laser pulses are emitted every minute angle within the scanning 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.

  Simultaneously with the acquisition of the point cloud data, a video is taken using the camera. The image can be used when the user designates a measurement target part in data analysis.

JP 2009-204615 A

  Conventionally, in order to interpret a feature based on point cloud data, it has been necessary to manually extract the feature using a three-dimensional CAD by using an editing tool or the like.

  An object of the present invention is to provide a data analysis apparatus, a data analysis method, and a program for automatically detecting a wall surface of a feature based on point cloud data representing a three-dimensional shape of the feature surface.

  The data analysis apparatus according to the present invention detects a wall surface of the feature based on point cloud data representing a three-dimensional shape of the feature surface in the target space, and divides the target space by a vertical plane. A partial space setting means for setting a plurality of columnar partial spaces, a partial space selecting means for selecting, as the partial space of interest, a point space including a point group having a height difference equal to or higher than a preset threshold among the partial spaces, A block space setting unit configured to divide a target partial space by a horizontal plane and set a plurality of block spaces stacked vertically; and a horizontal plane search unit configured to search for a position of the wall surface in the horizontal plane. For each block space, the means searches for a line segment in the horizontal plane where the point group projected onto the horizontal plane gathers in the vicinity of a predetermined reference or more, and determines the position of the inner wall surface in the horizontal plane. Defined as.

  Another data analysis apparatus according to the present invention further includes a vertical position determining unit that obtains a vertical position of the wall surface existing at the inner wall surface position in the target space in the target space, and the vertical position determining unit includes the horizontal plane. For each inner wall surface position, the point cloud whose projection position onto the horizontal plane is within a preset distance from the horizontal plane inner wall surface position is extracted as a point of interest group, and the point of interest group is preset in the vertical direction. Each range that exists above the threshold value is defined as a vertical wall surface position at the horizontal wall surface position.

  In another data analysis apparatus according to the present invention, the shape of the wall surface existing in each vertical wall surface position at the horizontal inner wall surface position is set to the distribution range of the attention point group at the vertical wall surface position. Wall surface shape determining means to be obtained based on, the wall surface shape determining means is a trapezoid on the vertical surface in the horizontal wall surface position, two parallel sides thereof are set in the vertical direction, and the distribution range is A shape approximating the shape projected onto the vertical plane is defined as the shape of the wall surface.

  In another data analysis apparatus according to the present invention, for each vertical wall surface position in the horizontal plane inner wall surface position that has already been detected, a new horizontal plane inner wall surface position connected to an end of the horizontal plane inner wall surface position is obtained. The tracking means is a rectangular parallelepiped space having a rectangular shape in which the midpoint of the short side is connected to the end of the detected inner surface of the horizontal plane, the height of which is the vertical direction. The adjacent space arranged corresponding to the wall surface position is set so that the center line along the long side of the rectangle matches the extension line of the horizontal wall surface position, and the point group is set in advance in the adjacent space In the case of collecting more than the standard, the line segment along the horizontal plane distribution of the point cloud in the adjacent space is set as the new horizontal plane inner wall surface position, while the center line is matched with the extension line. New in the set adjacent space When the position of the inner wall surface in the horizontal plane is not detected, the direction of the center line in the horizontal plane is changed to search for the adjacent space where the point cloud is most concentrated inside, and the obtained point in the adjacent space is obtained. A line segment along the distribution of the group in the horizontal plane is set as the new horizontal plane inner wall surface position.

  A data analysis method according to the present invention is a method for detecting a wall surface of a feature based on point cloud data representing a three-dimensional shape of the feature surface in the target space, and dividing the target space by a vertical plane. A partial space setting step for setting a plurality of columnar partial spaces, a partial space selecting step for selecting, as the partial space of interest, a point space that includes a point group having a height difference equal to or higher than a preset threshold among the partial spaces, A block space setting step of dividing a target partial space by a horizontal plane and setting a plurality of block spaces stacked vertically, and a horizontal plane search step of searching for the position of the wall surface in the horizontal plane, the horizontal plane search For each block space, the step searches for a line segment in the horizontal plane where the point group projected onto the horizontal plane gathers in the vicinity of a predetermined reference or more. To determine the horizontal plane in the wall position.

  A program according to the present invention is a program for causing a computer to perform data analysis for detecting a wall surface of a feature based on point cloud data representing a three-dimensional shape of the feature surface in a target space. A partial space setting means for setting a plurality of columnar partial spaces by dividing the target space by a vertical plane, and a portion including a point group having a height difference equal to or higher than a preset threshold among the partial spaces. Partial space selection means for selecting as a space, block space setting means for setting a plurality of block spaces that are vertically stacked by dividing the target partial space by a horizontal plane, and horizontal plane search means for searching for the position of the wall surface in the horizontal plane The horizontal plane search means is projected on the horizontal plane out of line segments in the horizontal plane for each block space. Group is defined as a horizontal plane wall position by searching what gather near the above criteria set in advance.

  According to the present invention, the wall surface of a feature can be automatically detected based on point cloud data representing the three-dimensional shape of the feature surface.

1 is a block diagram showing a schematic configuration of a wall surface extraction system according to an embodiment of the present invention. It is a general | schematic flowchart of the wall surface extraction process by the wall surface extraction system which concerns on embodiment of this invention. It is a general | schematic flowchart of the process by a partial space setting means and a partial space selection means. It is a general | schematic flowchart of the process by a block space setting means and a horizontal plane search means. It is a schematic diagram explaining the search process of the edge in a block space. It is a general | schematic flowchart of the process by a vertical position determination means and a wall surface shape determination means. It is explanatory drawing for demonstrating grouping of the point group in the vertical search space by a vertical position determination means. It is a schematic diagram explaining the process which calculates | requires the shape of a wall surface element. It is a general | schematic flowchart of the process by a tracking means. It is a general | schematic flowchart of a continuous wall surface tracking process. It is a general | schematic flowchart of a continuous wall surface tracking process. It is a schematic flowchart of edge fine adjustment processing. It is a schematic flowchart of edge fine adjustment processing. It is a general | schematic flowchart of the process by a wall surface element registration means. It is a processing flowchart after finishing the process about the main edge in a certain hierarchy. It is a schematic flowchart of multiple edge processing. It is a schematic diagram explaining a multiple edge process.

  Hereinafter, a wall surface extraction 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 is a data analysis device that detects a wall surface of a feature, that is, a surface of the feature surface that is substantially perpendicular to the ground surface 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 point density corresponding to the scale of the irregularities on the feature surface. In this regard, laser scanning performed from the ground such as a road using a vehicle or a tripod can achieve a scanning density that can capture the shape of walls such as buildings, fences, signboards, and signs built near the road, for example. .

  FIG. 1 is a block diagram showing a schematic configuration of the wall surface extraction 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 a partial space setting means 20, a partial space selection means 22, a block space setting means 24, a horizontal plane search means 26, a vertical position determination means 28, which will be described later, It functions as a wall surface shape determination unit 30, a tracking unit 32, and a wall surface element registration unit 34.

  The storage device 6 is composed of a hard disk or the like built in the computer. The storage device 6 converts the arithmetic processing unit 4 into a partial space setting means 20, a partial space selection means 22, a block space setting means 24, a horizontal plane search means 26, a vertical position determination means 28, a wall surface shape determination means 30, a tracking means 32, and a wall surface. A program for functioning as the element registration unit 34 and other programs, and various data necessary for processing of the present system are stored. For example, the storage device 6 stores point cloud data of the analysis target space as the processing target data. For example, the area along the road is the analysis target space.

  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 position of the wall surface in the target space obtained by the present system to the user by screen display, printing, or the like.

  FIG. 2 is a schematic flowchart of wall surface extraction processing by the wall surface extraction system 2. Each means of the arithmetic processing unit 4 will be described with reference to FIG.

  The partial space setting means 20 sets a plurality of columnar partial spaces obtained by dividing the target space along the vertical plane as initial unit spaces for wall surface extraction processing (S40). In the present embodiment, the partial space setting means 20 divides the target space into a two-dimensional orthogonal lattice (mesh) shape in a horizontal plane (the XY plane in the XYZ orthogonal coordinate system) and forms a quadrangular prism-shaped partial space. Is generated. For example, the horizontal cross section of the partial space can be a square having a width W and a depth D of 50 cm. The height H of the partial space can be matched with the height of the target space (dimension in the Z-axis direction), and the height is the height of the wall surface to be detected, specifically the height of the building, signboard / It is set larger than the height at which the sign is placed. In the program constituting the partial space setting means 20, the width W, the depth D, and the height H are parameterized and can be changed by the user using the input device 8, for example.

  The partial space selection means 22 selects a partial space including a point group having a height difference equal to or higher than a preset threshold as a partial space of interest. Specifically, the subspace selection unit 22 selects a plurality of set subspaces in a predetermined order, and pays attention to the subspace when a point difference in the subspace has a height difference equal to or greater than a threshold value. Let it be a subspace. As described above, the point cloud data is acquired by laser irradiation from a laser scanner located on a road or the like, and the wall surface visible from the laser scanner is captured on the three-dimensional shape by the point cloud data. Therefore, for example, the partial space selection means 22 determines a reference plane based on the portion of the plane corresponding to the road or the like in the three-dimensional shape, and horizontally in the width of the road along the road. A line is set (S42). Then, the starting point of selection of the partial space is set in order along the center line, the partial space is selected in the order from the starting point in both directions along the line orthogonal to the center line (S44), and the selected portion If the difference in level of the point group in the space is equal to or greater than the threshold, the subspace is determined as the target subspace (S46).

The block space setting means 24 divides the target partial space by a horizontal plane and sets a plurality of block spaces that are stacked vertically (S48). For example, it is possible to set the width W, comparable to the depth D of the height H _B subspace of the block space.

  For each block space, the horizontal plane search means 26 searches for a line segment where the point group projected on the horizontal plane in the horizontal plane gathers in the vicinity of a predetermined reference or more, and the line segment is searched for the wall surface in the horizontal plane. It is determined as a position (horizontal wall surface position) (S50). By searching for the position of the wall surface in the horizontal plane for each block space, it is possible to discriminate between a plurality of wall surfaces with different positions and orientations that may exist at different heights in the subspace that is elongated in the vertical direction.

  The vertical position determining means 28 obtains the vertical position of the wall surface existing at the wall surface position in the horizontal plane in the target space (S52). Specifically, the vertical position determining means 28 presets, for each horizontal inner wall surface position detected in the target partial space, a projection position onto the horizontal plane among the point group of the target partial space from the horizontal inner wall surface position. What is within the distance is extracted as a point of interest group. Then, for the attention point group extracted for each horizontal plane inner wall surface position, each range in which the attention point group is equal to or greater than a preset threshold with respect to the vertical position is defined as the vertical wall surface position at the horizontal plane inner wall position. Thereby, based on the horizontal plane inner wall surface position detected in a certain block space, the wall surface which exists in the said horizontal plane inner wall surface position in the height range other than the said block space is also detected. For example, one vertical wall surface position is determined for a wall surface extending over a plurality of continuous block spaces. In addition, when a plurality of wall surfaces separated in the height direction exist at the same horizontal plane wall surface position, that is, when the wall surfaces form a hierarchy in the vertical direction, the vertical wall surface position is obtained for each of the wall surfaces forming the hierarchy.

  A set of a horizontal plane inner wall surface position and a vertical wall surface position with respect to the horizontal plane inner wall surface position represents an arrangement of one wall surface in the target partial space. Since the wall surface may be a part of a larger wall surface that extends outside the target partial space, that is, in the lateral direction, it is referred to as a wall surface element here.

  The wall surface shape determining means 30 obtains the shape of the wall surface element existing at each vertical wall surface position at the horizontal wall surface position based on the distribution range of the point of interest group at the vertical wall surface position (S54). Specifically, the wall surface shape determining means 30 projects the point of interest on a vertical plane passing through the horizontal inner wall surface position, and obtains the shape of the wall surface element based on the distribution range of the projected point group. The shape is a trapezoid that approximates the distribution range of the point cloud projected on the vertical plane with the upper and lower bases, which are two parallel sides, set in the vertical direction.

  The tracking unit 32 obtains a new horizontal plane inner wall surface position connected to the end of the horizontal plane inner wall surface position for each vertical wall surface position in the detected horizontal plane inner wall surface position (S56). The tracking unit 32 sets a rectangular parallelepiped space in the lateral direction of the wall surface element, projects a point cloud in the adjacent space onto a horizontal plane, and creates a line segment in which the projected point group gathers in the vicinity of a preset reference or more. A new horizontal plane inner wall surface position is determined by searching. By sequentially repeating the process, the horizontal wall surface position of the wall surface that continues in the horizontal direction from the wall surface element specified in the target partial space is traced.

  Specifically, the adjacent space is set to a rectangular parallelepiped whose planar shape is a rectangle in which the midpoint of the short side is connected to the end of the detected horizontal plane inner wall surface position. Here, the center line along the long side of the rectangle is the direction of the adjacent space in the horizontal plane. Further, the vertical range defined by the height of the adjacent space, that is, the upper surface and the bottom surface of the rectangular parallelepiped is arranged at a position corresponding to the vertical wall surface position.

  First, the tracking unit 32 sets the adjacent space in a direction in which the position of the detected inner surface of the horizontal plane is extended straight. That is, the adjacent space is arranged so that the rectangular center line of the planar shape coincides with the extension line of the detected inner surface of the horizontal plane. And when a point group gathers in the said adjacent space more than the reference | standard set beforehand, the line segment along the distribution in the horizontal surface of the point group in the said adjacent space is made into a new horizontal plane inner wall surface position. On the other hand, in the case where a new horizontal plane inner wall surface position is not detected in the adjacent space set so as to coincide with the extension line of the detected horizontal plane inner wall surface position, the tracking means 32 changes the direction of the adjacent space. Find the position of the inner wall surface. In other words, among the adjacent spaces whose orientations are changed, a search is made for the one in which the point cloud is most concentrated inside, and the obtained line segment along the distribution in the horizontal plane of the point cloud in the adjacent space is determined as a new horizontal plane inner wall surface. Position.

  Information regarding the wall surface element detected in the target space as described above is registered in the storage device 6 by the wall surface element registration means 34. The registered information on the shape of the wall surface element and the information on the connection relationship are used, for example, for the recognition of the wall surface shape of the building and the construction of a three-dimensional model of the building.

  Hereinafter, a processing example of the wall surface extraction system 2 will be described in more detail. 3, 4, 6, and 9 to 16 are process flow diagrams showing a series of processes executed by the arithmetic processing unit 4 in a divided manner. The processing shown in FIG. 3 corresponds to the processing by the partial space setting means 20 and the partial space selection means 22 described above. When the wall surface extraction system 2 starts the wall surface extraction process (S60), the partial space setting unit 20 sequentially creates partial spaces in the target space. For example, when dividing the target space into a mesh shape along the X-axis direction and the Y-axis direction, if the width W and the depth D of the partial space are set as parameters, the intervals between the meshes (the size of the partial space) Thus, the number of partial spaces arranged in the X and Y directions is determined according to the size of the target space. The range of the index indicating the position of the partial space in each of the X and Y directions is determined based on the number of arrays. The partial space setting means 20 changes the index representing the two-dimensional mesh-like arrangement of the partial space in a predetermined order, and is defined by a position (coordinate range in each direction of X and Y) corresponding to the set index. A space is set (S62).

  Here, when the point cloud data is acquired by performing laser irradiation from the vehicle traveling on the road, as described above, the reference plane is based on the portion of the plane corresponding to the road or the like in the three-dimensional shape represented by the point cloud data. Is set, and the center line of the three-dimensional shape is set along the road. In order to simplify the explanation, the center line is assumed to be the X axis (ie, Y = 0), the index in the X direction increases in the direction in which the vehicle travels, and the Y direction corresponding to the road width direction leaves the center line. It is assumed that the absolute value of the index increases according to For example, the index in the Y direction is set to 0 at the position of the center line, set to 1, 2, 3,... To the left with respect to the traveling direction, and set to -1, -2, -3,. be able to. In this case, for example, the subspace setting unit 20 sequentially increments the index in the X direction, and changes the index in the Y direction in both the positive direction and the negative direction of the Y axis at each value of the index in the X direction. . That is, the partial space setting means 20 sets the partial space by increasing the index in the Y direction from 0 in order of a certain index value in the X direction, and sets the partial space by decreasing in order from -1. To do.

  If the index-designated mesh subspace is unprocessed (in the case of “No” in S64), the subspace selection means 22 extracts the point group in the subspace from the storage device 6 such as a hard disk, for example. The data is taken into a work area such as a RAM (Random Access Memory) (S66). If there is an elevation difference of a predetermined threshold value Th1 or more in the point cloud captured in the partial space (in the case of “Yes” in S68), the partial space selection means 22 determines that the partial space is the target partial space, Processing such as searching for the horizontal inner wall surface position and the vertical wall surface position in the target partial space is started (processing proceeds to node B in FIG. 4). For example, the threshold value Th1 can be set small enough to eliminate a partial space including only a substantially flat portion of the ground surface such as a road. Further, the threshold value Th1 may be set according to the height of the feature for which the wall surface is to be detected, so that the wall surface of the low feature that is not of interest is not detected. For example, the threshold value Th1 can be set to the height of the first floor of the building so that relatively low features such as guardrails and postboxes existing in front of the building when viewed from the road are excluded from detection.

  When the difference in level of the point cloud captured from the set partial space is less than the threshold Th1 (in the case of “No” in S68), the wall surface extraction process in the partial space is not performed. In this case, the partial space setting means 20 sets the next partial space (the process returns to node A).

  When the processing has been completed for all the meshes (in the case of “Yes” in S64), the arithmetic processing device 4 finishes the wall surface extraction processing (S70).

  FIG. 4 is a schematic process flow diagram of a portion performed by the block space setting unit 24 and the horizontal plane search unit 26 described above in a series of processes executed by the arithmetic processing unit 4.

Block space setting unit 24 divides the attention subspace for each predetermined height H _B, sets a plurality of blocks spaces stacked vertically, the data of the group of points located in each block in the space from the storage device 6 The data is taken into a work area such as a RAM (S80). For example, when the width W and the depth D of the partial space are 50 cm, the height H _B of the block space can be 20 to 50 cm. The number of stages of the block space set by the division is n, and the index I is used to represent the 1st floor (I = 1, 2,..., N) in order from the bottom.

  The horizontal plane search means 26 selects n-stage block spaces in order and checks whether an edge (horizontal wall surface position) exists in the block space. For example, the horizontal plane search means 26 increases the index I by 1 and selects a block space for examining the presence of an edge (S82, S84). The horizontal plane searching means 26 checks whether an edge exists in the selected block space on the first floor (S86), and if it does not exist (in the case of "No" in S88), selects the next block space ( S84). If I exceeds n, the process returns to node A in FIG. 3 to search for the next target subspace (S90).

  FIG. 5 is a schematic diagram for explaining the edge search processing S86 in the block space. FIG. 5 is a top view of the block space, and shows an example of a rectangle 100 defined by the width W and the depth D of the block space and an arrangement of point groups in the block space in the XY plane. Has been. The horizontal plane search means 26 selects two arbitrary data points PA and PB from the point group in the block space, and uses the line segment L0 in the XY plane having these two points as both ends as an edge candidate line. In addition to the setting, a band-like area EA having a width w (total width 2w) is set on both sides of the line segment L0. The horizontal plane search means 26 counts the number of data points whose coordinates on the XY plane are located in the area EA. Whether or not the data point is in the area EA can be determined by whether or not the length of the perpendicular line from the data point to the line segment L0 on the XY plane is w or less, for example. In addition, the edge condition imposes that the three-dimensional shape represented by the point group in the area EA is not flat. Specifically, the horizontal plane search means 26 is not flat if the height difference (difference between the maximum altitude and the minimum altitude) ΔZ of the data points included in the area EA is equal to or greater than a preset step threshold γ. judge.

  Here, the width w is a parameter and absorbs the variation in the position in the direction orthogonal to the edge of the data point corresponding to the wall surface. For example, w can be about 3 cm. The height difference threshold γ can be set to about 2 cm, for example.

  The horizontal plane search means 26 sets the candidate lines for all combinations of two data points in the block space and performs the above-described determination, and the most data points gather in the area EA, and ΔZ is greater than or equal to the threshold value γ. A line segment L0 is an edge found in the block space. If an edge exists (“Yes” in S88), it is determined whether the edge is a new one that has not been subjected to processing for determining a wall surface element (S92). The determination is performed based on whether or not the same edge as the edge found in the process S86 exists by searching a list in which the edges to be processed for determining the wall surface element are registered. If the same thing does not exist in the list, it is determined as a new edge (in the case of “No” in S92), and the coordinates (ax, ay) and (bx, by) of both ends PA and PB of the edge are registered in the list. (S94). When a new edge is detected in a certain block space in this way, the vertical wall surface position search in the target partial space and the shape of the wall surface element are determined for the edge (node D in FIG. 6). To proceed). On the other hand, the same edge as the edge found in the process S86 is already registered in the list in the process for the other block space, and the process such as the search for the vertical wall surface position may be performed. In this case (“Yes” in S92), processing such as registration of the edge in the list and search for the vertical wall surface position is omitted, and the horizontal plane search means 26 performs processing for the next block space ( Processing returns to node C).

  FIG. 6 is a schematic process flow diagram of the vertical position determining means 28 and the wall surface shape determining means 30. The vertical position determination means 28 sets a vertical search space as a space for searching for the position of the wall surface in the vertical direction at the edge L0 extracted in step S86, and data of point groups located in the vertical search space Is taken from the storage device 6 into a work area such as a RAM (S110). The vertical search space is a rectangular parallelepiped space whose planar shape is a rectangle having the edge L0 as the central axis in the longitudinal direction, and the bottom surface and the top surface thereof are positioned at the same height as the bottom surface and the top surface of the partial space. The dimension ξ of the rectangular short side of the planar shape can be set, for example, to be the same as or slightly larger than the width 2w of the strip-shaped area EA from which the edge is extracted. For example, ξ can be set to 10 cm.

  The vertical position determination means 28 groups the point groups in the vertical search space based on the distribution in the Z direction (S112). Thereby, the hierarchical structure of the wall surface of the vertical direction in the wall surface position in a certain horizontal surface is caught.

In the grouping, for example, the vertical position determining means 28 sets a plurality of sections in the Z direction with a constant step size h from the minimum elevation to the top of the point group taken into the vertical search space or from the maximum elevation to the bottom. Then, the point cloud in the vertical search space is divided into a plurality of hierarchies (ranks) based on the section, and the number of point clouds (score) is totaled for each rank. Here, in accordance with the step size h, the resolution at which two wall surfaces adjacent in the vertical direction can be detected separately is determined, and basically h is set to H _B or less. Here, as h becomes smaller, the resolution becomes higher. However, if h is too small, the score in the section of the height range where the wall surface exists becomes small, and the score in the height range where the wall surface does not exist (basically 0). Is expected to be difficult). h is set in consideration of these factors. The vertical position determining means 28 groups the point clouds in the vertical search space based on the point cloud distribution obtained by counting the number of point clouds for each rank. Specifically, each rank range in which the point cloud is gathered is defined as one group of point clouds, and the rank range is defined as one vertical wall surface position.

  FIG. 7 is an explanatory diagram for explaining grouping, and shows an example of a point cloud distribution table as a result of totaling the number of point clouds for each rank with h being 0.2 m. For example, the vertical position determination means 28 checks the rank in order from the bottom (minimum altitude) to the top, and assigns a group number to each range in which ranks having scores greater than 0 are consecutive. In the example shown in FIG. 7, there are four rank ranges with ranks greater than 0: ranks 1 to 4, ranks 9 to 12, ranks 18 to 22, and ranks 25 to 26. ~ 4 is given. If a rank with a score greater than 0 appears alone, that is, if the score is 0 before and after the rank, the vertical position determination means 28 assigns a group number with the one rank as one rank range. .

  The number of hierarchies determined by grouping is represented as m. In the example shown in FIG. The wall surface shape determining means 30 sequentially selects rank ranges (vertical wall surface positions) set in the m level. For example, the wall surface shape determining means 30 increases the group number J by 1 and selects a rank range for determining the shape of the wall surface element (S114, S116). Then, the data of the point group existing in the selected rank range among the point groups existing in the vertical search space is taken into the work area such as the RAM from the storage device 6 (S120), and the trapezoid as the wall surface element is determined from the point group data. Processing is performed (S122). When the shape of the wall surface element can be determined (in the case of “Yes” in S124), the tracking process for sequentially obtaining the horizontal wall surface position of the wall surface element connected in the lateral direction of the wall surface element is started (FIG. 9). Processing proceeds to node F).

  When the wall surface element determination processing is completed for all m layers (in the case of “Yes” in S118), the process returns to node C in FIG. 4, the next edge is searched, and the same processing is repeated for the edge. Further, when the shape of the wall surface element cannot be determined in a certain hierarchy (“No” in S124), the process returns to the process S116, and the process for the next hierarchy is performed.

  FIG. 8 is a schematic diagram for explaining the process S122 for obtaining the shape of the wall surface element, and shows an example of a state in which a point group existing in the vertical search space is projected onto a vertical plane passing through an edge. ing. In FIG. 8, black points 132 distributed on the vertical plane 130 indicated by rectangles are points obtained by projecting the point group. In the rectangular vertical surface 130, the lateral direction is along the edge, the left side corresponds to the one end PA side of the edge, and the right side corresponds to the other end PB side. The vertical direction of the vertical plane 130 is the vertical direction, and the upper end and the lower end correspond to the upper end and the lower end of the rank range, respectively. The wall surface shape determining means 30 divides the vertical surface 130 into a lattice shape and defines a plurality of small rectangles 134 arranged in rows and columns. For example, the small rectangle 134 can have a width of about 10 cm and a height of about 5 cm.

The wall surface shape determining means 30 selects columns of small rectangles 134 arranged vertically, for example, in order from the left side, and searches for a column in which two or more small rectangles 134 on which the point group is projected exist. When a range in which such a column appears, that is, a column range from the first position (the η _S column) to the last position (the η _E column) is obtained, a process for specifying the shape of the wall surface element is further performed. Done. On the other hand, when such a column range does not exist, it is determined that no wall surface element exists in the hierarchy.

The wall surface shape determining means 30 is one of two parallel sides of the trapezoid based on the point group (black dot 132) projected on the vertical plane 130 (black dot 132). The X and Y coordinates of the vertical line to be determined are determined, and the Z coordinates of both ends of the side (upper base) are determined based on the altitude of the point group distributed in the left end portion. In addition, the X and Y coordinates of the vertical line that is the other of the two parallel sides of the trapezoid (here, this is the lower base of the trapezoid) are determined based on what is located at the right end portion, and distributed to the right end portion. Based on the elevation of the point group, the Z coordinates of both ends of the side (lower base) are determined. For example, the wall surface shape determining means 30 determines the X and Y coordinates of the upper base of the trapezoid and the Z coordinates of both ends thereof based on the distribution of point groups in the two columns of the η _S column and the (η _S +1) column. The X and Y coordinates of the lower base of the trapezoid and the Z coordinates of both ends thereof are determined based on the distribution of the point group in the two columns of the η _E column and the (η _E −1) column. In this case, specifically, the maximum value (corresponding to the point 136) and the minimum value (corresponding to the point 138) among the Z coordinates of the point group in the two columns of the η _S column and the (η _S +1) column. Z coordinates of both ends of the upper base can be used, and the maximum value (corresponding to the point 140) and the minimum value (corresponding to the point 140) among the Z coordinates of the point group in the two columns of the η _E column and the (η _E −1) column ( (Corresponding to point 142) can be the Z coordinates of the lower bottom ends. The X and Y coordinates of the upper base are the X and Y coordinates at the leftmost point 144 of the point group and the boundary line 146 between the η _S column and the (η _S +1) column. The X and Y coordinates can be defined by the X and Y coordinates at the rightmost point 148 in the point group or the boundary 150 between the η _E column and the (η _E −1) column.

  FIG. 9 is a schematic process flow diagram of the tracking means 32. The tracking unit 32 sets an edge detected in the target subspace as a base point for continuous tracking processing (S160), and performs initialization processing for emptying the edge lists A and B (S162). It is checked whether the wall surface element acquired for the base edge has already been registered in the graphic database (S166). If not registered, that is, if it is new (in the case of “No” in S166), the wall surface element is selected. Add to the edge list A (S168).

  Further, the tracking unit 32 tracks edges that are continuous with both ends of the base edge. Specifically, for each vertical wall surface position, a continuous wall surface tracking process is performed in which edges that are continuous in the direction extending from one end PA of the base edge are detected, and a wall surface element corresponding to the detected edge is obtained. Are additionally registered in the edge list A (S170). Similarly, the continuous wall surface tracking process is also performed in the direction extending from the other end PB of the base edge, and the acquired wall surface element is additionally registered in the edge list B (S172). When processing by the tracking unit 32 is finished for a certain base point edge, processing by the wall surface element registration unit 34 is performed (processing proceeds to node G in FIG. 14).

  On the other hand, when the wall surface element at the base edge is already registered in the graphic database in the process S166 (in the case of “Yes” in S166), the arithmetic processing unit 4 performs the tracking process for the wall surface element. The registration process is not performed, and the process proceeds to node H shown in FIG. Processing in this case will be described later.

  10 and 11 are schematic flowcharts of the continuous wall surface tracking process performed in processes S170 and S172. The tracking means 32 sets a rectangular parallelepiped search space as a space for searching for an edge continuous to the already detected edge. The search space is a space adjacent to the search space of the detected edge, and the midpoint of the short side of the rectangle that is the planar shape of the search space is arranged at the end of the detected edge. Further, the range of the Z value (height of the top surface and the bottom surface) of the search space is set according to the vertical position of the wall surface element at the detected edge. For example, considering that the upper side and the lower side of the trapezoid, which is the shape of the detected wall surface element, may have a gradient, the height of the upper surface of the search space is set to the vertical 2 of the detected wall surface element. It is possible to set the height of the side higher than the upper end of the side to which the search space is connected, while setting the height of the bottom surface of the search space lower than the lower end of the side. The length of the long side of the planar shape of the search space can be set to be approximately the same as the width W and the depth D of the partial space, and can be set to 50 cm, for example.

  First, the tracking means 32 extends to the side to track a line segment that is a sharpened edge, and arranges a search space in the direction of the extended line (S180). That is, the search space is arranged so that the center line along the long side of the rectangle which is the planar shape coincides with the extension line. Then, the data of the point group located in the search space is fetched from the storage device 6 into a work area such as a RAM (S180), and an edge is searched for in the search space by the same method as the processing S86 (S182). When an edge exists in the search space (in the case of “Yes” in S184), the tracking unit 32 further performs a process of determining a trapezoid as a wall surface element from the point cloud data in the search space in the same manner as the process S122. Perform (S186). When the shape of the wall surface element can be acquired (in the case of “Yes” in S188), an edge fine adjustment process is performed (S190). Then, the process returns to step S180, and the continuous wall surface tracking process is continued with the edge obtained now as the detected edge.

  When the edge or wall surface element cannot be detected in the search space on the extension line of the detected edge (in the case of “No” in processing S184 and “No” in S188), the process proceeds to the node CB in FIG. Search processing in a direction deviated from the extension line of the detected edge is performed. In the search process, the search space is set by changing the central axis in the horizontal plane from the position of the extension line to the clockwise direction and the counterclockwise direction. For example, a change range (± θ) of the direction of the central axis from the direction of the extension line and its division number k are set, and a plurality of search spaces having different angles in the horizontal plane by θ / k are set. The edge is searched for, and the edge having the most point cloud is selected from the edges obtained in the plurality of search spaces set by changing the angle (S200).

  When the edge where the first point cloud is gathered can be identified (in the case of “Yes” in S202), the tracking unit 32 further determines the trapezoid as the wall surface element from the point cloud data in the search space in the same manner as in the processing S122. Processing is performed (S204). When the shape of the wall surface element can be acquired (in the case of “Yes” in S206), a fine adjustment process described later is performed (S208). Then, the process returns to the node CA in FIG. 10, and the continuous wall surface tracking process is continued with the obtained edge as the detected edge.

  When the edge or wall surface element cannot be detected in the search space with the angle changed (“No” in the process S202 and “No” in the S206), the continuous wall surface tracking process ends, and the process is performed. The process returns to the process S170 or S172 called as a subroutine.

12 and 13 are schematic flowcharts of the edge fine adjustment processing S190 and S208. When a line segment indicating an edge is obtained, the arithmetic processing unit 4 divides the line segment into sections of a certain length (S220), searches for a point (representative point) through which the edge passes in each section, and determines the edge as X Instead of a line segment connecting PA and PB in the -Y plane, a polygonal line connecting representative points obtained in each section is used. Here, the number of sections is N. Processing device 4, the representative point P _K is obtained from the section K selected minimum coordinates (X _{K, Y K)} in the horizontal plane of the representative points P _K and altitude values of wall elements in the section The value ZL _K and the maximum value ZH _K are obtained as representative point information. For example, ZL _K and ZH _K can be obtained by interpolating values at both ends PA and PB of the edge.

  First, the arithmetic processing unit 4 uses the one end PA of the edge as a representative point, and stores representative point information about the PA in the edge representative point list (S222). Subsequently, the section number K is incremented by 1 to select a section (S224, S226), and a representative point is searched for in each section (S230). In the processing S230, the horizontal section is a region near the selected section, a space having a height corresponding to the wall surface element is set in the vertical direction, the point cloud data in the section is taken in, and the horizontal section of the space is set. Find the position where the point cloud is most dense. If the number of points crowded at the position is equal to or larger than the predetermined number (in the case of “Yes” in S232), the position is added to the edge representative point list as a representative point (S234). On the other hand, if the number of points crowded at the position is less than a predetermined number (in the case of “No” in S232), the next representative point is not added to the edge representative point list from the relevant section. Proceed to section processing. When the processing is completed for all the sections (“Yes” in S228), the other end PB of the edge is added to the edge representative point list (S236), and the process proceeds to node CV-R in FIG.

The arithmetic processing unit 4 counts the number m of representative points stored in the edge representative point list (S240). Then, select the pair of the representative point _{P J} and _{P J + 1} adjacent increasing by one the J from 1 to (m-1) (S242~S246) , the representative point information and the representative point information _{P J + 1} in _{P J} It is checked whether the wall surface element represented by is already registered in the graphic database (S248). If not registered (in the case of “No” in S250), the wall surface element is added to the edge list A or B (S252), and the process returns to S244. On the other hand, if the graphic has already been registered in the graphic database (“Yes” in S250), the edge fine adjustment processes S190 and S208 are terminated.

  Now, as described in the explanation of FIG. 9, when the processing by the tracking unit 32 is finished for a certain base edge, the processing by the wall surface element registration unit 34 is performed. FIG. 14 is a schematic process flow diagram of the wall surface element registration means 34. If the edge lists A and B generated by the tracking unit 32 are not empty (“No” in S260 and S264), the wall surface element registration unit 34 stores the wall surface elements stored in these edge lists in the graphic database. (S262, S266), and the process proceeds to node H in FIG.

  The base edge that has been processed so far is the edge detected in step S88 from the block space on the I-th floor specified in step S84. When the arithmetic processing unit 4 finishes the processing at the J-th layer specified in step S116 for the base point edge, it searches for other edges in the height range corresponding to the layer in the target subspace and searches for the wall surface element. The process of determining is performed. Thereby, for example, multiple edges in the same height range can be detected, and wall elements corresponding to each can be determined. FIG. 15 is a process flow diagram after finishing the process for the first edge (referred to as main edge) in the J-th layer. After finishing the processing of the main edge in the J-th layer, the arithmetic processing unit 4 searches for another edge (referred to as a multiple edge) existing in the layer and determines a wall element (multiple edge processing) S270. . When the multi-edge processing S270 is completed, the processing returns to the node E in FIG. 6, and the processing of the main edge in the next hierarchy is started.

  FIG. 16 is a schematic process flow diagram of multiple edge processing. In the multiple edge processing, a search is made for other edges on both sides of the main edge. FIG. 17 is a schematic diagram for explaining multiple edge processing, and shows a planar arrangement of a partial space. In the example shown in FIG. 17A, the target partial space 280 includes an edge 282 where the point cloud is collected most and an edge 284 whose point cloud is less collected than the edge 282. The edge 282 as the main edge is detected by the processing by the horizontal plane searching means 26, the vertical position determining means 28, the wall surface shape determining means 30, the tracking means 32, and the wall surface element registering means 34, and the edge 282 is tracked. Graphic registration processing is performed. In the multiple edge processing, multiple edge search spaces 286 and 288 are set on both sides so as not to include the edge 282 (FIG. 17B). The multi-edge search space is set to a rectangular parallelepiped space whose planar shape is a rectangle having the same size and shape as the partial space and whose Z value range is the same as that of the J-th layer. For example, a multiple edge search space is created in a region on the left side of the main edge (edge 282 in FIG. 17) in any direction extending, and a point cloud in the space is captured (S300), and processing S86 is performed. Similarly, the edge search is performed in the multiple edge search space, and it is determined whether or not an edge exists (S302). When an edge exists (in the case of “Yes” in S302), a space having the same planar shape as the vertical search space in the processing S110 of FIG. 6 is set around the edge in the multiple edge search space. A process of determining the shape of the wall surface element from the point group in the same manner as the process S122 is performed (S304). When the shape of the wall surface element can be acquired (in the case of “Yes” in S306), the continuous wall surface tracking processing S160 to S172 by the tracking unit 32 described in FIG. 9 and the wall surface element registration unit 34 described in FIG. The same processing S308 as the registration processing S260 to S266 is performed. Similarly, a multiple edge search space is created in the area on the right side of the main edge, and processes S310 to S318 similar to the processes S300 to S308 in the case of the left side described above are performed.

  In the above-described embodiment, the program that causes the computer to operate as each unit of the wall surface extraction system 2 is stored in the storage device 6 and the computer reads and executes the program. In this case, the wall surface extraction system 2 includes a communication device, and the communication device acquires a program from a network or the like and provides the program to the arithmetic processing device 4 or the storage device 6. Remember. Further, the program can be provided by being stored in a recording medium such as a CD-ROM (Compact Disc Read Only Memory).

  2 Wall Extraction System, 4 Arithmetic Processing Device, 6 Storage Device, 8 Input Device, 10 Output Device, 20 Partial Space Setting Means, 22 Partial Space Selection Means, 24 Block Space Setting Means, 26 Horizontal Plane Search Means, 28 Vertical Position Determination Means, 30 wall surface shape determining means, 32 tracking means, 34 wall surface element registration means.

Claims (6)

A data analysis device for detecting a wall surface of a feature based on point cloud data representing a three-dimensional shape of a feature surface in a target space,
A partial space setting means for dividing the target space into a two-dimensional mesh shape in a horizontal plane by a vertical plane, and setting a plurality of columnar partial spaces;
A partial space selecting means for selecting, as the partial space of interest, a portion including a point group having a height difference equal to or higher than a preset threshold among the partial spaces;
A block space setting means for dividing the target partial space by a horizontal plane and setting a plurality of block spaces stacked vertically;
A horizontal plane search means for searching for the position of the wall surface in the horizontal plane,
The horizontal plane search means searches for a block where the point group projected onto the horizontal plane gathers in the vicinity of a preset reference or higher among line segments in the horizontal plane for each block space. To establish,
A data analysis device characterized by The data analysis apparatus according to claim 1,
Vertical position determining means for obtaining a vertical position of the wall surface existing at the inner wall surface position in the horizontal plane in the target space;
The vertical position determination means extracts, as the point of interest, a point of the point group whose projected position onto the horizontal plane is within a preset distance from the horizontal plane inner wall surface position, for each horizontal plane inner wall surface position. Determining each range in which the point of interest group is equal to or greater than a predetermined threshold with respect to the direction as a vertical wall surface position at the horizontal wall surface position;
A data analysis device characterized by In the data analysis device according to claim 2,
Wall surface shape determining means for determining the shape of the wall surface existing at each vertical wall surface position at the horizontal wall surface position based on the distribution range of the point of interest group at the vertical wall surface position;
The wall surface shape determining means is a trapezoid on the vertical surface at the position of the inner wall surface in the horizontal plane, the two parallel sides being set in the vertical direction, and approximating the shape obtained by projecting the distribution range onto the vertical surface. The shape of the wall surface;
A data analysis device characterized by In the data analysis device according to claim 2 or 3,
For each vertical wall surface position in the horizontal wall surface position that has already been detected, the tracking means for obtaining a new horizontal wall surface position that continues to the end of the horizontal wall surface position,
The tracking means is a rectangular parallelepiped space having a rectangular shape in which a midpoint of a short side is connected to an end portion of the detected inner surface of the horizontal plane, the height of which corresponds to the vertical wall position. Are set so that the center line along the long side of the rectangle coincides with the extension line of the inner wall surface of the horizontal plane, and the point cloud gathers in the adjacent space above a preset reference. In this case, the adjacent space set by setting a line segment along the distribution in the horizontal plane of the point group in the adjacent space as the new horizontal plane wall surface position, and setting the center line to coincide with the extension line. If a new position on the inner wall surface of the horizontal plane is not detected, the adjacent space where the point cloud is most concentrated inside is searched by changing the direction of the center line in the horizontal plane, and the obtained adjacent space Along the distribution of the point cloud in the horizontal plane Min to a new piece of the horizontal inner wall surface position,
A data analysis device characterized by A data analysis method for detecting a wall surface of a feature based on point cloud data representing a three-dimensional shape of a feature surface in a target space,
A partial space setting step of dividing the target space into a two-dimensional mesh shape in a horizontal plane by a vertical plane, and setting a plurality of columnar partial spaces;
A subspace selection step of selecting, as the subspace of interest, a point including a point group having a height difference equal to or higher than a preset threshold among the subspaces;
A block space setting step of dividing the target partial space by a horizontal plane and setting a plurality of block spaces stacked vertically;
A horizontal plane search step for searching the position of the wall surface in the horizontal plane,
In the horizontal plane search step, for each block space, a search is made for a line segment in the horizontal plane where the point group projected onto the horizontal plane is gathered in the vicinity of a predetermined reference or more to determine the position of the horizontal plane inner wall surface To establish,
A data analysis method characterized by A program for causing a computer to perform data analysis for detecting a wall surface of a feature based on point cloud data representing a three-dimensional shape of a feature surface in a target space, the computer comprising:
A partial space setting means for dividing the target space into a two-dimensional mesh in a horizontal plane by a vertical plane, and setting a plurality of columnar partial spaces;
A partial space selection means for selecting, as the partial space of interest, a portion including a point group having a height difference equal to or higher than a preset threshold value among the partial spaces;
Dividing the target partial space by a horizontal plane, functioning as block space setting means for setting a plurality of block spaces stacked vertically, and search means for searching for the position of the wall surface in the horizontal plane,
The horizontal plane search means searches for a block where the point group projected onto the horizontal plane gathers in the vicinity of a preset reference or higher among line segments in the horizontal plane for each block space. To establish,
A program characterized by

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