Apparatus, method, and program for detecting target equipment

Abstract

The purpose of the present disclosure is to make it possible to automatically detect a target facility from three-dimensional point group data.　The present disclosure is a device and a method which detect a target facility from within a structure by clustering a point group in which each point represents a three-dimensional coordinate, and by extracting a point group of a structure from the point group and using the reflection intensity of the extracted point group of the structure.

Description

[Technical Field]

[0001] The present disclosure relates to a technology for detecting target equipment from three-dimensional point cloud data. [Background technology]

[0002] A technology has been developed that uses a Mobile Mapping System (MMS) equipped with a 3D laser scanner to create 3D models of structures located outdoors (see, for example, Patent Document 1). This technology creates a point cloud and scan lines in a space where no point cloud exists, and then creates a 3D model using the created point cloud and scan lines.

[0003] It is desirable to be able to create 3D models of only the target equipment for which equipment information needs to be calculated. However, current devices only have the function of determining whether the model coordinates of the 3D model are close to the coordinates in the equipment information given in advance. In contrast, if the target equipment is a utility pole, for example, the 3D models created will be a wide range, including utility poles, trees, and streetlights. Therefore, currently, it is necessary to visually determine which 3D model is the pole model of the target equipment, the utility pole. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2017-156179 Summary of the Invention [Problem to be solved by the invention]

[0005] The purpose of this disclosure is to enable automatic detection of target equipment from 3D point cloud data. [Means for solving the problem]

[0006] 3D laser scanners can measure not only the reflected position of laser light but also the reflected intensity of laser light. Therefore, this disclosure enables automatic identification of target equipment by using the reflected intensity of a 3D point cloud.

[0007] Specifically, the apparatus and method of the present disclosure include: By clustering the point cloud, where each point represents a 3D coordinate, the points of the structure are extracted from the point cloud. The target equipment is detected from within the structure using the reflection intensity of the extracted point cloud of the structure.

[0008] Specifically, the program of the present disclosure is a program for realizing each functional unit of the device of the present disclosure in a computer, and is a program for causing a computer to execute each step of the method executed by the device of the present disclosure. [Effects of the Invention]

[0009] According to the present disclosure, it is possible to automatically detect target equipment from three-dimensional point cloud data. [Brief explanation of the drawings]

[0010] [Figure 1] 1 shows an example of point cloud data. [Figure 2] An example of a 3D model of a structure as an object is shown below. [Figure 3] 10 shows an example of the reflection intensity of a point cloud reflected by a utility pole. [Figure 4] An example of the reflection intensity of a point cloud reflected by a tree is shown. [Figure 5] 1 illustrates an example of a system configuration according to an embodiment of the present disclosure. [Figure 6] 1 shows an example of a block configuration of this embodiment. [Figure 7] 10 shows an overview of the processing executed by the extraction processing unit. [Figure 8] 10 shows an example of a flowchart of a process executed by an extraction processing unit. [Figure 9]10 shows an example of a flowchart of a process executed by an extraction processing unit. [Figure 10] An example of the percentage of points whose reflection intensity difference with respect to adjacent scan lines exceeds 2000 is shown below. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. Note that the present disclosure is not limited to the embodiments shown below. These implementation examples are merely illustrative, and the present disclosure can be implemented in various forms with various modifications and improvements based on the knowledge of those skilled in the art. Note that components with the same reference numerals in this specification and drawings indicate the same components.

[0012] The present disclosure relates to an apparatus and method for selectively creating a 3D model of a target facility from point cloud data representing 3D coordinates acquired by a 3D laser scanner. FIG. 1 shows an example of point cloud data. Point cloud data is data that represents the surface shape of a structure as a set of points, with each point representing the 3D coordinates of the structure's surface. Each point in the point cloud data of the present disclosure contains the reflection intensity of laser light reflected from the surface of the structure. By forming L1 to L4 that connect the points of the 3D point cloud data, a 3D model that represents the structure as an object can be created. For example, as shown in FIG. 2, a 3D utility pole model 111 and a cable model 112 can be created.

[0013] In this disclosure, lines L1 to L4 as shown in FIG. 1 are referred to as scan lines. Furthermore, in this disclosure, the point clouds acquired during one rotation of the 3D laser scanner are treated as a single scan line measured at the same time. For example, if the point cloud constituting line L1 is measured at time T1, scan line L1 is treated as a single scan line. Furthermore, the coordinate axes of the 3D coordinates of each point can be any, but for example, the traveling direction of the MMS can be x, the depth direction y, and the height direction z.

[0014] (Summary of the Disclosure) The light reflection intensity varies depending on the surface shape and material of a material. Therefore, the light intensity of points reflected by the same material will have common characteristics. Therefore, in this disclosure, the reflection intensity is verified for each scan line determined to be one cluster to determine whether it is the same material.

[0015] Here, it is not possible to confirm the detailed surface shape from the coordinates of the reflection point cloud emitted from an object at a long distance alone. However, when measuring a point cloud from a relatively close distance, the smaller the angle between the 3D laser scanner and the measurement point (the angle of incidence and reflection of the laser irradiation), the higher the reflection intensity, for the same material. For this reason, the reflection intensity of a point cloud that hits a cylindrical structure (utility pole, cable, etc.) made of a certain material will be high if it hits the center of the cylindrical object and low if it hits the edge.

[0016] For example, if scan line L1 shown in Figure 1 is measured at time T1, then scan line L2 is measured at time T2, and then scan line L3 is measured at time T3, checking the point clouds acquired earlier will theoretically confirm regular changes in scan lines L1 to L3 for cylindrical structures (utility poles, cables, etc.) made of a uniform material. On the other hand, natural objects such as trees often have an irregular surface shape even if they are made of a uniform material, and regular changes in reflection intensity are not observed. Therefore, it is possible to distinguish between artificial objects such as utility poles and cables and natural objects based on the reflection intensity of the point clouds.

[0017] Figures 3 and 4 show examples of the reflection intensity of point clouds actually measured using a 3D laser scanner. In the case of trees, as shown in Figure 3, there are significant irregularities. In contrast, in the case of utility poles, as shown in Figure 4, there are some irregularities, but a curved shape appears.

[0018] Therefore, this disclosure distinguishes between the surface shapes of man-made and natural objects made of the same material from changes in the reflection intensity of 3D point cloud data, enabling more accurate determination of whether an object is a target facility.

[0019] Specifically, the device of this embodiment executes the following processing. Cluster the 3D coordinates of the point cloud using DBSCAN (Density-based spatial clustering of applications with noise) and extract scan lines. -Search for cylindrical objects based on the shape of the scan line. Extract scan lines used for the same cylindrical object from the 3D coordinates of the point cloud that make up the scan lines. Check the change in the reflection intensity of the scan line that is considered to be the same cylinder. If the change in the reflection intensity of the scan line is within the threshold, it is determined to be an artificial object, and if the change is random, it is determined to be a natural object.

[0020] Here, the scan lines of a cylindrical object are unique. For example, in the case of a cylindrical object, as shown in Figure 1, multiple short scan lines of similar lengths are arranged in parallel, which have a curvature compared to the scan lines of a plane. In addition, points d11, d21, d31, and d41 are arranged at positions corresponding to the ends of the cylindrical object, and points d13, d23, d33, and d43 are arranged in a straight line. Therefore, cylindrical objects are searched for based on the characteristics of these scan lines.

[0021] 5 shows an example of a system configuration according to an embodiment of the present disclosure. The system according to this embodiment is an MMS 80 including a 3D laser scanner 81, a GPS receiver 82, an IMU (Inertial Measurement Unit) 83, a camera 84, an odometer 85, a storage medium 86, and a computing device 87. The MMS 80 separates data acquired by various measuring devices (IMU 83, 3D laser scanner 81, camera 84, odometer 85, and GPS receiver 82) into point cloud data and image data, and stores the data in the storage medium 86.

[0022] The three-dimensional laser scanner 81 measures point cloud data of the structure. The GPS receiver 82 determines the geographic location of the MMS 80 . The camera 84 takes a photograph of the structure that the three-dimensional laser scanner 81 measures. The odometer 85 measures the distance traveled by the MMS 80 .

[0023] An example of a block configuration of this embodiment is shown in Figure 6. The calculation device 87 functions as the device of the present disclosure and includes an extraction processing unit 11, a GIS (Geographic Information System) unit 12, and an equipment information calculation unit 13. The calculation device 87 can also be realized by a computer and a program, and the program can be recorded on a recording medium or provided via a network.

[0024] The extraction processing unit 11 generates a three-dimensional model of the target facility from the point cloud data stored in the storage medium 86. The GIS unit 12 acquires geospatial information based on the image data stored in the storage medium 86. As a result, the point cloud of the structure extracted by the extraction processing unit 11 is linked to the geospatial information. The facility information calculation unit 13 calculates facility information based on the three-dimensional model of the structure and the geospatial information. The facility information includes, for example, the deflection of the utility pole, the slack of the cable, and the like.

[0025] In the present disclosure, before creating a three-dimensional model, the extraction processing unit 11 performs clustering of the point cloud, and checks the reflection intensity of the clustered point cloud to determine whether it is an artificial or natural object, thereby detecting the target equipment.

[0026] FIG. 7 shows an outline of the processing executed by the extraction processing unit 11. Step S1: The extraction processing unit 11 extracts scan lines that are candidates for constructing a cylindrical object from the point cloud. DBSCAN is a clustering method that regards a point cloud that satisfies the condition that there are at least a certain number of points within a threshold distance of a certain point as a single mass and clusters it. Step S2: Extract scan lines that make up the same cylindrical object. Step S3: Determine whether the reflection intensity of the points that make up the extracted scan line changes regularly. Step S4: If the change is regular, it is an artificial object, and if the change is not regular, it is a natural object. Therefore, the extraction processing unit 11 creates a three-dimensional model using the point cloud that constitutes the scan line of the artificial object.

[0027] 8 and 9 show examples of flowcharts of the processing executed by the extraction processing unit 11. FIG. The extraction processing unit 11 reads the point cloud (S11), performs clustering based on the three-dimensional coordinates of the point cloud, and extracts scan lines (S12). The extraction processing unit 11 searches for a cylindrical object from the extracted scan line (S21). The extraction processing unit 11 narrows down the scan lines that are candidates for a cylindrical object to scan lines that constitute the same cylindrical object and clusters them (S22 to S25). At this time, a reference scan line is selected from all the candidate scan lines. Furthermore, if a scan line exists within a certain threshold distance from the reference scan line (Yes in S23), it is considered to be a scan line that constitutes the same cylindrical object (S26).

[0028] The extraction processing unit 11 checks the reflection intensity of each of the scan lines that are regarded as scan lines that constitute the same cylindrical object (S31 to S34). For example, the reflection intensity of each point included in each scan line is converted so that it falls within a predetermined range of values ​​(for example, 0 to 66535) (S31). Next, it is determined whether the change in reflection intensity for each scan line is regular (S32), and the total number I of scan lines that are not regular is counted (S33). Next, the ratio of the total number I of scan lines in which the change in reflection intensity is not regular to the total number k of scan lines constituting the same cylindrical object is calculated (S34). If the ratio of the total number I is within a certain threshold (No in step S34), it is regarded as an artificial object, and a three-dimensional model of the cylindrical object is created (S41). On the other hand, if the ratio of the total number I is equal to or greater than a certain threshold (Yes in step S34), the object is regarded as a natural object, and a three-dimensional model is not created (S42).

[0029] Here, the reflection intensity is displayed differently depending on the model of the three-dimensional laser scanner 81. Therefore, in step S31, the extraction processing unit 11 normalizes the reflection intensity A measured by the three-dimensional laser scanner 81 in which the reflection intensity is represented by a ratio. For example, the minimum value a min of the reflection intensity A is set to 0, the maximum value a max of the reflection intensity A is set to 65535, and it is converted to 0 < A < 65535. Similarly, in the case of the three-dimensional laser scanner 81 in which the reflection intensity is displayed as an absolute value, the extraction processing unit 11 converts it so that the minimum value is 0 and the maximum value is 65535. Thereby, even when the point cloud is measured using three-dimensional laser scanners 81 of different models, the extraction processing unit 11 can detect a desired target facility.

[0030] Also, in step S32, the method of comparing the reflection intensities is arbitrary. For example, if the acquired point cloud is arranged from an earlier acquisition time to a later acquisition time, and the number of locations where the reflection intensity changes significantly from the adjacent point is within a certain threshold, it can be exemplified that it is determined as an object with a constant material and a smooth surface. For example, for each i-th point included in one scan line, if the ratio of the difference b (b = A i+1 - A i ) between adjacent points exceeding 2000 is within 15% of the number of the point cloud constituting the scan line, it is determined that it changes regularly.

[0031] In step S34, for example, if the number I of irregular scan lines is within 20% of the total number k of scan lines constituting the same cylindrical object, it is regarded as an artifact (No in S34), and a three-dimensional model is created (S41). On the other hand, if it is 20% or more (Yes in S34), it is regarded as a natural object and no three-dimensional model is created (S42).

[0032] Note that the thresholds in steps S32 and S34 are obtained by extracting five scan lines each of an artifact (utility pole) and a natural object (tree) as shown in FIG. 10 and calculating the ratio of the number of differences b in the reflection intensity exceeding 2000, and are not limited to these numerical values.

[0033] In addition, the determination in step S32 of whether the reflection intensity changes regularly may be made based on the size of the standard deviation or the absolute value of the difference between adjacent points. Alternatively, instead of step S33, the total number of scan lines that change regularly may be counted. In this case, the Yes / No determination in step S32 is reversed.

[0034] Furthermore, the number of scan lines checked in step S32 may be all scan lines, or may be randomly selected. In this case, instead of step S34, the ratio to the number of randomly selected scan lines is used. Even for utility poles, there are locations where the reflection intensity does not change regularly due to the influence of attached objects such as bands, so it is desirable to check several poles and set the ratio of regularly changing scan lines to the number of extracted scan lines as a threshold.

[0035] (Comparison of reflection intensities in step S23) The determination in step S23 can be made according to the shape of the cluster formed in step S12.

[0036] For example, in the case of an object that is long in the vertical direction, such as a utility pole or tree, it is determined whether the scan lines above or below the reference scan line are scan lines that form the same cylindrical object.

[0037] For example, in Figure 1, the scan line L1 acquired at the earliest time is set as the reference scan line, and it is determined whether the scan line L2 acquired during the next rotation is within a threshold distance. The threshold for determination is such that if the point cloud coordinates (x2, y2, z2) constituting scan line L2 are within a threshold distance of the point cloud coordinates (x1, y1, z1) constituting scan line L1, the scan lines are determined to be constituting the same cylindrical object. Here, the threshold distance is determined depending on arbitrary conditions such as the traveling speed of the MMS 80 and the structure, but can be, for example, Δx<50 mm, Δy<50 mm, and Δz<200 mm.

[0038] For example, for an object that is long parallel to the ground, such as a cable, the scan lines next to the reference scan line are checked to see if they constitute the same cylindrical object. In this case, the threshold distances can be, for example, Δx<100 mm, Δy<100 mm, and Δz<50 mm.

[0039] Depending on the structure and the surrounding environment, there may be cylindrical objects whose middle is blocked, resulting in the extraction of scan lines for only the upper and lower ends. Therefore, in step S23, the cylinders of the finally created clusters may be extended along their central axes to extract clusters that constitute the same cylindrical object. The central axis can be estimated, for example, by estimating a circle on a horizontal plane at any height in the point cloud used for the extracted scan lines, and then repeating this in the vertical direction to extract a continuous model of the circle (cylinder). This allows the central axis of the cylinder to be estimated. [Industrial Applicability]

[0040] The present disclosure can be applied to the information and communications industry. [Explanation of symbols]

[0041] 11: Extraction processing section 12:GIS Department 13: Equipment information calculation department 81: 3D laser scanner 82: GPS receiver 83:IMU 84: Camera 85: Odometer 86:Storage medium 87: Arithmetic device

Claims

1. a first functional unit that extracts a point cloud of a structure from the point cloud by clustering the point cloud, each point representing a three-dimensional coordinate; a second functional unit that calculates a difference or ratio of reflection intensity between adjacent points of the extracted point cloud of the structure for each point included in the same scan line; a third functional unit that determines that the structure is an artificial structure when a difference or ratio of reflection intensity between adjacent points on the same scan line is within a certain value; An apparatus comprising:

2. The same scan line is a scan line composed of point clouds measured at the same time.

10. The apparatus of claim 1.

3. Further comprising a fourth functional unit that creates a three-dimensional model using a point cloud corresponding to the artificial structure.

10. The apparatus of claim 1.

4. The device, By clustering the point cloud, where each point represents a three-dimensional coordinate, the point cloud of the structure is extracted from the point cloud. Calculating a difference or ratio of reflection intensity between adjacent points of the extracted point cloud of the structure for each point included in the same scan line; If the difference or ratio of reflection intensity between adjacent points on the same scan line is within a certain value, the structure is determined to be an artificial structure. method.

5. A program for causing a computer to realize each of the functional units of the device according to any one of claims 1 to 3.

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