Shape detection device, shape detection system, and shape detection method

Abstract

Provided are a shape detecting device, etc., capable of detecting both an external shape of a target object and a surface shape of the target object. A shape detecting device (2) comprises: a first information acquiring means for acquiring first information relating to a distance to a target object, on the basis of laser light that is shone onto the target object, and reflected light reflected by the target object; an information acquiring means (21) for acquiring, on the basis of the laser light and the reflected light, second information that is different from the first information and that is used to detect the surface shape of the target object; a first shape detecting means (22) for detecting the external shape of the target object using the first information; and a second shape detecting means (23) for detecting the surface shape using the second information.

Description

[Technical Field]

[0001] The present invention relates to a shape detection device and the like. [Background technology]

[0002] A technique for detecting the outer shape of an object using distance information obtained by LiDAR (Light Detection and Ranging) is known (see, for example, Patent Document 1). Related techniques include those described in Patent Documents 2 to 4. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-001775 [Patent Document 2] Japanese Patent Application Laid-Open No. 2014-137339 [Patent Document 3] Japanese Patent Application Laid-Open No. 2011-196736 [Patent Document 4] Japanese Patent Application Laid-Open No. 2002-340537 Summary of the Invention [Problem to be solved by the invention]

[0004] Hereinafter, an object to be detected by LiDAR may be referred to as an "object." At least one of the concave and convex portions on the surface of an object may be collectively referred to as "irregularities." When using distance information obtained by LiDAR as in the technology described in Patent Document 1, irregularities on the surface of the object that are larger than the distance resolution of the LiDAR (i.e., the distance resolution in the distance information) are detected as part of the outer shape. On the other hand, there has been a problem in that irregularities on the surface of the object that are smaller than the distance resolution (hereinafter sometimes referred to as "surface shape" or "microirregularities") are not detected.

[0005] In view of the above-mentioned problems, an object of the present invention is to provide a shape detection device and the like that can detect both the outer shape and the surface shape of an object. [Means for solving the problem]

[0006] The shape detection device of the present invention comprises a first information acquisition means that acquires first information regarding the distance to the object based on laser light irradiated onto the object and light reflected by the object, a second information acquisition means that acquires second information, which is different from the first information and is used to detect the surface shape of the object, based on the laser light and the reflected light, a first shape detection means that detects the outer shape of the object using the first information, and a second shape detection means that detects the surface shape using the second information.

[0007] The shape detection system of the present invention comprises a first information acquisition means that acquires first information regarding the distance to an object based on laser light irradiated onto the object and light reflected by the object, an information acquisition means that acquires second information based on the laser light and reflected light, the second information being different from the first information and used to detect the surface shape of the object, a first shape detection means that detects the outer shape of the object using the first information, and a second shape detection means that detects the surface shape using the second information.

[0008] In the shape detection method of the present invention, a first information acquisition means acquires first information regarding the distance to the object based on laser light irradiated onto the object and reflected light reflected by the object, a second information acquisition means acquires second information, which is different from the first information and is used to detect the surface shape of the object, based on the laser light and reflected light, a first shape detection means detects the outer shape of the object using the first information, and a second shape detection means detects the surface shape using the second information. [Effects of the Invention]

[0009] According to the present invention, it is possible to provide a shape detection device and the like that can detect both the outer shape and the surface shape of an object. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a block diagram showing a shape detection system according to the first embodiment. [Figure 2] FIG. 2 is a block diagram showing the LiDAR device in the shape detection system according to the first embodiment. [Figure 3A] FIG. 3A is a block diagram showing the shape detection device according to the first embodiment. [Figure 3B] FIG. 3B is a block diagram showing an information acquisition unit in the engagement shape detection device according to the first embodiment. [Figure 4] FIG. 4 is a block diagram showing an abnormality detection device using the shape detection system according to the first embodiment. [Figure 5] FIG. 5 is a block diagram showing the hardware configuration of the shape detection device according to the first embodiment. [Figure 6] FIG. 6 is a block diagram showing another hardware configuration of the shape detection device according to the first embodiment. [Figure 7] FIG. 7 is a block diagram showing another hardware configuration of the shape detection device according to the first embodiment. [Figure 8] FIG. 8 is a flowchart showing the operation of the shape detecting device according to the first embodiment. [Figure 9] FIG. 9 is a block diagram showing a shape detection system according to the second embodiment. [Figure 10] FIG. 10 is a block diagram showing a deterioration detection device using a shape detection system according to the second embodiment. [Figure 11] FIG. 11 is a block diagram showing a shape detection device according to the third embodiment. [Figure 12] FIG. 12 is a block diagram showing a shape detection system according to the third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0012] [First embodiment] FIG. 1 is a block diagram showing a shape detection system 100 according to the first embodiment. FIG. 2 is a block diagram showing a LiDAR device 1 in the shape detection system 100. FIG. 3A is a block diagram showing a shape detection device 2 according to the first embodiment. FIG. 3B is a block diagram showing an information acquisition unit in the shape detection device according to the first embodiment. FIG. 4 is a block diagram showing an abnormality detection device 3 that uses the shape detection system 100. The shape detection system 100 will be described with reference to FIGS. 1 to 4.

[0013] As shown in FIG. 1, the shape detection system 100 includes a LiDAR device 1 and a shape detection device 2. That is, as shown in FIG. 1, the shape detection system 100 includes the LiDAR device 1. As shown in FIG. 2, the LiDAR device 1 includes a light emitting unit 11 and a light receiving unit 12. The light emitting unit 11 is configured by an optical transmitter for LiDAR. The light receiving unit 12 is configured by an optical receiver for LiDAR. Note that the LiDAR device 1 may include a signal processing unit (not shown) for LiDAR in addition to the light emitting unit 11 and the light receiving unit 12. The signal processing unit is configured by, for example, a dedicated circuit.

[0014] The light emitting unit 11 emits laser light toward an object. The emitted laser light is irradiated onto the object. Here, in the LiDAR device 1, the direction in which the laser light is emitted by the light emitting unit 11 is variable. The light emitting unit 11 sequentially emits laser light in multiple directions. As a result, the laser light is irradiated so as to scan the object. The irradiated laser light is reflected by the object. A backscattered component (i.e., backscattered light) of the reflected light (hereinafter sometimes referred to as "reflected light") is received by the light receiving unit 12. Hereinafter, the light of the reflected light received by the light receiving unit 12 may be referred to as "received light."

[0015] As shown in FIG. 1, the shape detection system 100 includes a shape detection device 2. The shape detection device 2 is communicatively connected to the LiDAR device 1 via wired or wireless communication. As will be described later with reference to FIGS. 5 to 7, the shape detection device 2 is configured by a computer. Note that such a computer may be provided in a so-called "cloud." As shown in FIG. 3A, the shape detection device 2 includes an information acquisition unit 21, a first shape detection unit 22, a second shape detection unit 23, and an output control unit 24.

[0016] The information acquisition unit 21 acquires information based on the laser light emitted by the LiDAR device 1 (i.e., the laser light irradiated onto the target object) and the reflected light received by the LiDAR device 1 (i.e., the received light). Here, as shown in FIG. 3B , the information acquisition unit 21 includes a first information acquisition unit 21_1 and a second information acquisition unit 21_2. The information acquired by the information acquisition unit 21 includes information acquired by the first information acquisition unit 21_1 (hereinafter sometimes referred to as "first information") and information acquired by the second information acquisition unit 21_2 (hereinafter sometimes referred to as "second information"). Specific examples of the first information and the second information will be described below.

[0017] <First information> The first information includes information (hereinafter sometimes referred to as "distance information") indicating the distance D between the position of the point where the LiDAR device 1 is installed and the position of the point (hereinafter sometimes referred to as "reflection point") where the laser light emitted in each direction is reflected by an object (including a target object).

[0018] Specifically, for example, the first information acquisition unit 21_1 calculates the distance D using ToF (Time of Flight). That is, in this case, the LiDAR device 1 emits pulsed laser light in each direction. The first information acquisition unit 21_1 calculates a one-way propagation distance (i.e., distance D) corresponding to the round-trip propagation time of these lights based on the time difference ΔT between the time T1 when the LiDAR device 1 emits the laser light in each direction and the time T2 when the LiDAR device 1 receives the corresponding reflected light. Thereby, distance information indicating the distance D is acquired.

[0019] Alternatively, for example, the first information acquisition unit 21_1 calculates the distance D by FMCW (Frequency Modulated Continuous Wave). That is, in this case, the LiDAR device 1 has a function of performing predetermined frequency modulation on laser light emitted in each direction and a function of performing coherent detection on the corresponding received light. The first information acquisition unit 21_1 calculates the corresponding distance D based on the frequency difference between these lights (so-called "beat frequency"). In this way, distance information indicating the distance D is acquired.

[0020] In addition, various known techniques can be used to acquire distance information. Detailed descriptions of these techniques will be omitted. For example, the distance D may be calculated based on the phase difference between the emitted laser light and the received reflected light (so-called "indirect ToF").

[0021] Note that, when the LiDAR device 1 includes a signal processing unit, the distance D may be calculated by the signal processing unit of the LiDAR device 1 instead of by the first information acquisition unit 21_1. In this case, the LiDAR device 1 may output distance information, and the output distance information may be acquired by the first information acquisition unit 21_1.

[0022] <Second information> The second information may include information indicating the intensity of received light corresponding to the laser light emitted in each direction (hereinafter, may be referred to as "intensity information"). That is, the second information acquisition unit 21_2 detects the intensity of the received light. As a result, intensity information indicating the detected intensity is acquired by the second information acquisition unit 21_2. Note that, when the LiDAR device 1 includes a signal processing unit, the intensity of the received light may be detected by the signal processing unit of the LiDAR device 1 instead of being detected by the second information acquisition unit 21_2. In this case, the LiDAR device 1 may output the intensity information, and the output intensity information may be acquired by the second information acquisition unit 21_2.

[0023] The second information may also include information indicating the polarization of the received light corresponding to the laser light emitted in each direction (hereinafter, this information may be referred to as "polarization information"). That is, in this case, the LiDAR device 1 has a function of detecting the polarization of the received light. The LiDAR device 1 outputs information indicating the detected polarization (i.e., polarization information). The second information acquisition unit 21_2 acquires the output polarization information.

[0024] Furthermore, the second information may include information indicating a frequency shift of the received light corresponding to the laser light emitted in each direction (hereinafter, may be referred to as "frequency shift information"). That is, the second information acquisition unit 21_2 detects a frequency shift of the received light based on frequency components contained in the received light. As a result, frequency shift information indicating the detected frequency shift is acquired by the second information acquisition unit 21_2. Note that, when the LiDAR device 1 includes a signal processing unit, the frequency shift of the received light may be detected by the signal processing unit of the LiDAR device 1 instead of being detected by the second information acquisition unit 21_2. In this case, the LiDAR device 1 may output frequency shift information, and the output frequency shift information may be acquired by the second information acquisition unit 21_2.

[0025] Furthermore, the second information may include information indicating a statistical quantity (e.g., variance) of a plurality of distances D corresponding to a plurality of emission directions (hereinafter, this information may be referred to as "statistical quantity information"). That is, the second information acquisition unit 21_2 calculates the statistical quantity based on the distances D calculated when acquiring the first information. As a result, statistical quantity information indicating the calculated statistical quantity is acquired by the second information acquisition unit 21_2. Note that, when the LiDAR device 1 includes a signal processing unit, the statistical quantity may be calculated by the signal processing unit of the LiDAR device 1 instead of being calculated by the second information acquisition unit 21_2. In this case, the LiDAR device 1 may output the statistical quantity information, and the output statistical quantity information may be acquired by the second information acquisition unit 21_2.

[0026] That is, the second information includes at least one of intensity information, polarization information, frequency shift information, and statistical information. In addition to acquiring distance information, the information acquisition unit 21 acquires at least one of intensity information, polarization information, frequency shift information, and statistical information. In this way, the second information is information obtained using the LiDAR device 1, and is information different from the first information regarding the distance D (i.e., distance information).

[0027] The first shape detection unit 22 detects the outer shape of the object using the first information acquired by the first information acquisition unit 21_1. Specifically, for example, the first shape detection unit 22 calculates coordinate values ​​indicating the positions of individual reflection points using distance information included in the acquired first information. The first shape detection unit 22 plots points corresponding to the calculated coordinate values ​​in a virtual three-dimensional space. This generates a three-dimensional model composed of a point cloud and corresponding to the outer shape of the object. In this way, the outer shape of the object is detected.

[0028] Note that when the laser light is irradiated onto an object other than the target object (for example, another object surrounding the target object), a point cloud corresponding to the other object may be plotted in addition to the point cloud corresponding to the target object. In such a case, the first shape detection unit 22 may extract the point cloud corresponding to the target object from the plotted point clouds by grouping the point clouds based on the inter-point distance or the result of plane detection. As a result, the first shape detection unit 22 may exclude the point cloud corresponding to the other object from the three-dimensional model.

[0029] The second shape detection unit 23 detects the surface shape of the object using the second information acquired by the second information acquisition unit 21_2. That is, the second shape detection unit 23 detects irregularities on the surface of the object that are small compared to the distance resolution in the distance information included in the first information (i.e., minute irregularities). Minute irregularities include, for example, protrusions caused by foreign matter adhering to the surface of the object, depressions caused by scratches on the surface of the object, or depressions (cracks, etc.) caused by deterioration of the object.

[0030] That is, the intensity of the corresponding received light usually differs depending on the presence or absence of minute irregularities and their size (so-called "surface roughness"). For example, if there are no minute irregularities, the intensity of the received light should be an approximately constant value corresponding to the distance D. On the other hand, if there are minute irregularities, the intensity of the received light should be a value different from the approximately constant value. Furthermore, in this case, the difference in intensity from the approximately constant value should be a value different depending on the size of the minute irregularities.

[0031] Furthermore, the polarization of the corresponding received light usually differs depending on the surface roughness. For example, if there are no minute irregularities, the polarization of the received light should not change with respect to a predetermined polarization (more specifically, the polarization of the emitted laser light). On the other hand, if there are minute irregularities, the polarization of the received light should change with respect to the predetermined polarization. Furthermore, in this case, the amount of change in polarization should be a different value depending on the size of the minute irregularities.

[0032] Furthermore, the statistics of the corresponding distance D usually differ depending on the surface roughness. For example, the variance when there are no minute irregularities should be smaller than the variance when there are minute irregularities. Furthermore, when there are minute irregularities, the variance when the minute irregularities are small should be smaller than the variance when the minute irregularities are large.

[0033] Therefore, for example, a database is prepared in advance that indicates the correspondence between a value indicating at least one of the statistical quantities of the intensity of the received light, the polarization of the received light, the frequency shift of the received light, and the distance D, and a value indicating the presence or absence and size of minute irregularities. The database may be stored inside the shape detection device 2, or may be stored in an external device (not shown). The second shape detection unit 23 uses the acquired second information to determine the presence or absence and size of minute irregularities based on the database.

[0034] The database may be updated based on the information used for detection by the second shape detection unit 23 (i.e., the acquired second information) and the results of detection by the second shape detection unit 23. The database may be updated by the shape detection device 2 (e.g., the second shape detection unit 23). Alternatively, the database may be updated by an external device (not shown).

[0035] Alternatively, for example, a model is prepared in advance that outputs a value indicating the presence or absence and size of minute irregularities when a value indicating at least one of the statistical quantities of the intensity of the received light, the polarization of the received light, the frequency shift of the received light, and the distance D is input. The model may be stored inside the shape detection device 2 or may be stored in an external device (not shown). The second shape detection unit 23 inputs a value corresponding to the acquired second information to the model. In response to this input, the model outputs a value indicating the presence or absence and size of minute irregularities to the second shape detection unit 23. The second shape detection unit 23 determines the presence or absence and size of minute irregularities based on the value output by the model.

[0036] Here, the model is, for example, a predetermined statistical model or a machine learning model generated by prior machine learning. Such a machine learning model is generated, for example, by supervised learning. Such supervised learning uses, for example, training data corresponding to at least one of the statistical quantities of the intensity of the received light, the polarization of the received light, the frequency shift of the received light, and the distance D, and ground truth labels corresponding to the presence and size of minute irregularities.

[0037] The model may be updated based on information used for detection by the second shape detection unit 23 (i.e., the acquired second information) and the result of detection by the second shape detection unit 23. The model may be updated inside the shape detection device 2. Alternatively, the model may be updated by an external device (not shown). If the model is a machine learning model, the external device may be, for example, a computer for machine learning. The structure of the model or the values ​​of individual parameters (e.g., weights) in the model may be updated by such a computer.

[0038] Here, the amount of change in the values ​​of the parameters included in the second information (intensity of received light, polarization of received light, frequency shift of received light, and statistics of distance D) depending on the presence or absence and size of minute irregularities may differ depending on the material of the surface of the object. Therefore, a database or model used to detect the surface shape may be prepared for each such material. The second shape detection unit 23 may select a database or model corresponding to the material of the surface of the object from among databases or models prepared in advance, and use the selected database or model.

[0039] Furthermore, the presence and size of minute irregularities may differ for each region on the surface of the object, so the second shape detection unit 23 may detect the presence and size of minute irregularities for each such region.

[0040] Specifically, for example, the second shape detection unit 23 divides the surface of the object into a plurality of regions based on the three-dimensional model generated by the first shape detection unit 22. The second shape detection unit 23 determines the presence and size of minute irregularities in each region using information related to the received light corresponding to each region in the second information. The method for determining the presence and size of minute irregularities in each region is as described above. In this manner, the surface shape of each region is detected.

[0041] Alternatively, for example, the second shape detection unit 23 divides the surface of the object into regions where minute irregularities exist and regions where minute irregularities do not exist based on the detection results of the three-dimensional model and the surface shape. The second shape detection unit 23 also divides the regions where minute irregularities exist according to the size of the minute irregularities. In this way, the surface of the object is divided into multiple regions according to the presence or absence and size of minute irregularities. In other words, the surface shape of each region is detected.

[0042] The output control unit 24 executes control to output information (hereinafter sometimes referred to as "shape information") indicating the results of detection by the first shape detection unit 22 and the second shape detection unit 23. That is, the shape information includes information indicating the outer shape of the object (hereinafter sometimes referred to as "first shape information") and information indicating the surface shape of the object (hereinafter sometimes referred to as "second shape information").

[0043] In this manner, the main part of the shape detection system 100 is configured.

[0044] The shape information is output, for example, by the shape detection device 2 to the anomaly detection device 3 (see FIG. 1). The anomaly detection device 3 is connected to the shape detection device 2 via wired or wireless communication so as to be able to communicate freely. The anomaly detection device 3 is configured by a computer. Such a computer may be provided in the cloud.

[0045] As shown in Fig. 4, the anomaly detection device 3 includes an anomaly detection unit 31. The anomaly detection unit 31 detects an anomaly in an object. The anomalies to be detected here are anomalies on the surface of the object, including an anomaly that causes a convex portion that should not exist (for example, the adhesion of a foreign object) or an anomaly that causes a concave portion that should not exist (for example, the occurrence of a scratch). When detecting such an anomaly, the anomaly detection unit 31 uses the shape information outputted above.

[0046] That is, the abnormality detection unit 31 determines the type of the object using the first shape information of the output shape information. Specifically, for example, the abnormality detection unit 31 determines the type of the object by performing pattern matching based on the outer shape of the object. Here, a database indicating the correspondence between the surface roughness and the presence or absence of an abnormality (for example, adhesion of foreign matter or occurrence of scratches) for each type of object is prepared in advance. The abnormality detection unit 31 determines the presence or absence of an abnormality (for example, adhesion of foreign matter or occurrence of scratches) on the surface of the object based on the database using the second shape information of the output shape information.

[0047] As described above, the second shape detection unit 23 can detect the presence and size of minute irregularities for each region on the surface of the object. In this case, the abnormality detection unit 31 may determine the location of an abnormality on the surface of the object based on the second shape information.

[0048] The anomaly detection device 3 may have a function to output information indicating the result of detection by the anomaly detection unit 31 (hereinafter, sometimes referred to as "anomaly detection information") to the outside. As a result, for example, an image corresponding to the anomaly detection information is displayed on a display device (not shown). As a result, the user can visually know the abnormality of the object. Here, the displayed image may be an image obtained by superimposing a second image indicating the result of detection by the anomaly detection unit 31 on a first image indicating the three-dimensional model generated by the first shape detection unit 22.

[0049] Hereinafter, the light emitting unit 11 may be referred to as "light emitting means." The light receiving unit 12 may be referred to as "light receiving means." The information acquiring unit 21 may be referred to as "information acquiring means." The first information acquiring unit 21_1 may be referred to as "first information acquiring means." The second information acquiring unit 21_2 may be referred to as "second information acquiring means." The first shape detecting unit 22 may be referred to as "first shape detecting means." The second shape detecting unit 23 may be referred to as "second shape detecting means." The output control unit 24 may be referred to as "output control means." The abnormality detecting unit 31 may be referred to as "abnormality detecting means."

[0050] Next, the hardware configuration of the shape detection device 2 will be described with reference to FIGS.

[0051] As shown in each of FIGS. 5 to 7, the shape detection device 2 uses a computer 41.

[0052] 5, the computer 41 includes a processor 51 and a memory 52. ​​The memory 52 stores programs for causing the computer 41 to function as the information acquisition unit 21, the first shape detection unit 22, the second shape detection unit 23, and the output control unit 24. The processor 51 reads and executes the programs stored in the memory 52. ​​This realizes a function F1 of the information acquisition unit 21, a function F2 of the first shape detection unit 22, a function F3 of the second shape detection unit 23, and a function F4 of the output control unit 24.

[0053] 6, the computer 41 includes a processing circuit 53. The processing circuit 53 executes processing for causing the computer 41 to function as the information acquisition unit 21, the first shape detection unit 22, the second shape detection unit 23, and the output control unit 24. This realizes functions F1 to F4.

[0054] 7, the computer 41 includes a processor 51, a memory 52, and a processing circuit 53. In this case, some of the functions F1 to F4 are realized by the processor 51 and the memory 52, and the remaining functions of the functions F1 to F4 are realized by the processing circuit 53.

[0055] The processor 51 is configured by one or more processors. Each processor is, for example, a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor, a microcontroller, or a digital signal processor (DSP).

[0056] The memory 52 is composed of one or more memories. Each memory may be a volatile memory or a non-volatile memory. That is, each memory may be, for example, a random access memory (RAM), a read-only memory (ROM), a flash memory, an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a solid-state drive, a hard disk drive, a flexible disk, a compact disk, a digital versatile disk (DVD), a Blu-ray disk, a magneto optical (MO) disk, or a mini disk.

[0057] The processing circuit 53 is composed of one or more processing circuits, each of which uses, for example, an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), an FPGA (Field Programmable Gate Array), an SoC (System on a Chip), or a system LSI (Large Scale Integration).

[0058] The processor 51 may include a dedicated processor corresponding to each of the functions F1 to F4. The memory 52 may include a dedicated memory corresponding to each of the functions F1 to F4. The processing circuit 53 may include a dedicated processing circuit corresponding to each of the functions F1 to F4.

[0059] Next, a description will be given of the operation of the shape detection system 100. More specifically, the description will focus on the operation of the shape detection device 2 with reference to the flowchart shown in FIG.

[0060] First, the information acquisition unit 21 acquires the first information and the second information (step ST1). Specific examples of the first information and the second information have already been described, and therefore will not be described again.

[0061] Next, the first shape detection unit 22 detects the outer shape of the object using the first information acquired in step ST1 (step ST2). A specific example of the method for detecting the outer shape of the object has already been described, and therefore, a repeated description will be omitted.

[0062] Next, the second shape detection unit 23 detects the surface shape of the object using the second information acquired in step ST1 (step ST3). A specific example of the method for detecting the surface shape of the object has already been described, and therefore a repeated description will be omitted.

[0063] Next, the output control unit 24 executes control to output shape information based on the detection results in steps ST2 and ST3 (step ST4). As described above, the shape information is output to, for example, the abnormality detection device 3. As a result, the shape information is used to detect an abnormality in the object.

[0064] The order of execution of the process of step ST2 and the process of step ST3 is arbitrary. That is, as shown in Fig. 8, the process of step ST3 may be executed after the process of step ST2. Alternatively, the process of step ST2 may be executed after the process of step ST3. Alternatively, the process of step ST2 and the process of step ST3 may be executed in parallel.

[0065] Next, a modification of the shape detection system 100 will be described.

[0066] The second information is not limited to the above specific examples, and may include any information that is obtained using the LiDAR device 1, is different from distance information, and is used to detect the surface shape.

[0067] The method for detecting the outer shape in first shape detection unit 22 is not limited to the above specific example, and may be any method that detects the outer shape of an object using the acquired first information.

[0068] The method for detecting the surface shape in second shape detection unit 23 is not limited to the above specific example, and may be any method that detects the surface shape of the object using the acquired second information.

[0069] The output destination of the shape information is not limited to the abnormality detection device 3. The shape information may be output to any device or system that uses the shape information. In the second embodiment described later, an example in which the shape information is output to another device (more specifically, a deterioration detection device) will be described.

[0070] In other words, the use of shape information is not limited to detecting abnormalities in an object. Shape information may be used for any service or application that uses shape information. In a second embodiment described later, an example in which shape information is used for another service (more specifically, a service that detects deterioration of an object) will be described.

[0071] The shape detection device 2 may include a light emitting unit 11 and a light receiving unit 12. In this case, the LiDAR device 1 is not required. In other words, the shape detection device 2 may be configured integrally with the LiDAR device 1.

[0072] The shape detection device 2 may include an abnormality detection unit 31 instead of the output control unit 24. In this case, the shape detection device 2 may have a function of outputting information indicating the result of detection by the abnormality detection unit 31 (i.e., abnormality detection information). In this case, the abnormality detection device 3 is not necessary. In other words, the shape detection device 2 may be configured integrally with the abnormality detection device 3.

[0073] Next, the effects of the shape detection system 100 will be described.

[0074] As described above, the first information acquisition unit 21_1 acquires first information about the distance to the object based on the laser light irradiated onto the object and the light reflected by the object. The second information acquisition unit 21_2 acquires second information, which is different from the first information and is used to detect the surface shape of the object, based on the laser light and the reflected light. The first shape detection unit 22 detects the outer shape of the object using the first information. The second shape detection unit 23 detects the surface shape using the second information.

[0075] In this way, by acquiring the first information, the outer shape of the object can be detected. This is because, for example, a three-dimensional model of the object can be generated. Furthermore, by acquiring the second information, the surface shape of the object can be detected. This is because the values ​​of parameters included in the second information (e.g., the intensity of the received light, the polarization of the received light, the frequency shift of the received light, or the statistics of the distance D) change depending on the presence and size of minute irregularities. In other words, by acquiring the first information and the second information, both the outer shape and the surface shape of the object can be detected. As a result, it is possible to realize an application (e.g., detecting an abnormality in the object) that uses both the detection results of the outer shape of the object and the detection results of the surface shape of the object.

[0076] Furthermore, by using laser light and reflected light (i.e., by using LiDAR), the surface shape of an object can be detected using the same device (e.g., LiDAR device 1) as the device used to detect the outer shape (e.g., LiDAR device 1). This allows the number of devices included in the shape detection system 100 to be reduced compared to when the surface shape of an object is detected using a device different from the device used to detect the outer shape. As a result, the configuration of the shape detection system 100 can be simplified.

[0077] The second shape detection means detects the presence and size of irregularities (fine irregularities) on the surface of the object using the second information. By using the second information, it is possible to detect not only the presence or absence of fine irregularities but also the size of the fine irregularities (i.e., surface roughness).

[0078] The second information also includes at least one of intensity information indicating the intensity of the received reflected light (received light), polarization information indicating the polarization of the received reflected light (received light), frequency shift information indicating the frequency shift of the received reflected light (received light), and statistical amount information indicating a statistical amount (e.g., variance) of the distance D. By using this information, the presence or absence and size of minute irregularities can be detected.

[0079] Furthermore, the shape detection system 100 outputs information (shape information) indicating the results of detection by the first shape detection unit 22 and the second shape detection unit 23. This allows the results of such detection to be used for any purpose. Specifically, for example, the results of such detection can be used to detect abnormalities in the object.

[0080] Furthermore, the detection results by the first shape detection unit 22 and the second shape detection unit 23 are used to detect abnormalities in the object. This makes it possible to realize a service for detecting abnormalities in the object. Specifically, for example, it is possible to realize a service for detecting the attachment of foreign matter or the occurrence of scratches for each type of object.

[0081] [Second embodiment] Fig. 9 is a block diagram showing a shape detection system 100 according to the second embodiment. Fig. 10 is a block diagram showing a deterioration detection device 4 that uses the shape detection system 100. The shape detection system 100 will be described with reference to Fig. 9 and Fig. 10. In Fig. 9, elements that are the same as those shown in Fig. 1 are given the same reference numerals and descriptions thereof will be omitted.

[0082] As described in the first embodiment, the use of the shape information is not limited to detecting an abnormality in an object. For example, the shape information may be used to detect deterioration of an object.

[0083] That is, the output control unit 24 of the shape detection device 2 executes control to output the shape information. The shape information is output to the deterioration detection device 4 (see FIG. 9). The deterioration detection device 4 is connected to the shape detection device 2 via wired or wireless communication so as to be able to communicate freely. The deterioration detection device 4 is configured by a computer. Such a computer may be provided in the cloud.

[0084] As shown in FIG. 10, the deterioration detection device 4 includes a deterioration detection unit 61. Hereinafter, the deterioration detection unit 61 may be referred to as "deterioration detection means." The deterioration detection unit 61 detects deterioration of the object. The deterioration to be detected here is deterioration of the material on the surface of the object, and includes the disappearance or reduction of minute irregularities that should be present (e.g., wear), or the appearance of minute irregularities that should not be present (e.g., cracks). The deterioration detection unit 61 uses the shape information output above when detecting such deterioration.

[0085] That is, the deterioration detection unit 61 determines the type of the object using the first shape information of the output shape information. Specifically, for example, the deterioration detection unit 61 determines the type of the object by performing pattern matching based on the outer shape of the object. Here, a database indicating the correspondence between surface roughness and the presence or absence of deterioration (e.g., wear or cracks) for each type of object is prepared in advance. The deterioration detection unit 61 determines the presence or absence of deterioration (e.g., wear or cracks) on the surface of the object based on the database using the second shape information of the output shape information.

[0086] As described in the first embodiment, the second shape detection unit 23 of the shape detection device 2 can detect the presence and size of minute irregularities for each region on the surface of the object. In this case, the deterioration detection unit 61 may determine the location of deterioration on the surface of the object based on the second shape information.

[0087] The deterioration detection device 4 may have a function to output information indicating the result of detection by the deterioration detection unit 61 (hereinafter, sometimes referred to as "deterioration detection information") to the outside. As a result, for example, an image corresponding to the deterioration detection information is displayed on a display device (not shown). As a result, the user can visually know the deterioration of the object. Here, the displayed image may be an image obtained by superimposing a second image indicating the result of detection by the deterioration detection unit 61 on a first image indicating the three-dimensional model generated by the first shape detection unit 22 of the shape detection device 2.

[0088] Next, a modification of the shape detection system 100 will be described.

[0089] As described in the first embodiment, various modifications can be adopted for the shape detection system 100. In addition to this, the shape detection system 100 can also adopt the following modifications.

[0090] That is, the shape detection device 2 may be equipped with a deterioration detection unit 61 instead of the output control unit 24. In this case, the shape detection device 2 may have a function of outputting information indicating the result of detection by the deterioration detection unit 61 (i.e., deterioration detection information). In this case, the deterioration detection device 4 is not necessary. In other words, the shape detection device 2 may be configured integrally with the deterioration detection device 4.

[0091] Furthermore, the shape information may be output to both the abnormality detection device 3 and the deterioration detection device 4. Alternatively, the shape detection device 2 may include both the abnormality detection unit 31 and the deterioration detection unit 61 instead of the output control unit 24. This makes it possible to realize both a service for detecting an abnormality in an object and a service for detecting deterioration in an object.

[0092] Next, the effects of the shape detection system 100 will be described.

[0093] By using the shape detection system 100, various effects can be obtained as described in the first embodiment. In addition to these, the following effects can be obtained.

[0094] That is, the detection results by the first shape detection unit 22 and the second shape detection unit 23 are used to detect deterioration of the object. This makes it possible to realize a service for detecting deterioration of the object. Specifically, for example, it is possible to realize a service for detecting wear or cracks for each type of object.

[0095] [Third embodiment] Fig. 11 is a block diagram showing a shape detection device 2a according to a third embodiment. The shape detection device 2a will be described with reference to Fig. 11. Fig. 12 is a block diagram showing a shape detection system 100a according to the third embodiment. The shape detection system 100a will be described with reference to Fig. 12. In Figs. 11 and 12, blocks similar to those shown in Figs. 3A and 3B are designated by the same reference numerals, and descriptions thereof will be omitted.

[0096] Here, the shape detection device 2 according to each of the first and second embodiments is an example of a shape detection device 2a according to the third embodiment. Also, the shape detection system 100 according to each of the first and second embodiments is an example of a shape detection system 100a according to the third embodiment.

[0097] 11, the shape detection device 2a includes a first information acquisition unit 21_1, a second information acquisition unit 21_2, a first shape detection unit 22, and a second shape detection unit 23. In this case, an output control unit 24 may be provided outside the shape detection device 2a.

[0098] 12, the shape detection system 100a includes a first information acquisition unit 21_1, a second information acquisition unit 21_2, a first shape detection unit 22, and a second shape detection unit 23. In this case, the LiDAR device 1 may be provided outside the shape detection system 100a. In other words, the light emission unit 11 and the light reception unit 12 may be provided outside the shape detection system 100a. In this case, the output control unit 24 may be provided outside the shape detection system 100a.

[0099] In these cases, the same effects as those described in the first embodiment can be obtained.

[0100] That is, the first information acquisition unit 21_1 acquires first information about the distance to the object based on the laser light irradiated onto the object and the light reflected by the object. The second information acquisition unit 21_2 acquires second information, which is different from the first information and is used to detect the surface shape of the object, based on the laser light and the reflected light. The first shape detection unit 22 detects the outer shape of the object using the first information. The second shape detection unit 23 detects the surface shape using the second information.

[0101] In this way, by acquiring the first information, the outer shape of the object can be detected. This is because, for example, a three-dimensional model of the object can be generated. Furthermore, by acquiring the second information, the surface shape of the object can be detected. This is because the values ​​of parameters included in the second information (e.g., the intensity of the received light, the polarization of the received light, the frequency shift of the received light, or the statistics of the distance D) change depending on the presence and size of minute irregularities. In other words, by acquiring the first information and the second information, both the outer shape and the surface shape of the object can be detected. As a result, applications that use both the detection results of the outer shape of the object and the detection results of the surface shape of the object (e.g., detection of abnormalities in the object or detection of deterioration in the object) can be realized.

[0102] The shape detection system 100a may include a light emitting unit 11 and a light receiving unit 12 in addition to the first information acquiring unit 21_1, the second information acquiring unit 21_2, the first shape detecting unit 22, and the second shape detecting unit 23. The shape detection system 100a may include an output control unit 24 in addition to these functional units. The shape detection system 100a may include an abnormality detecting unit 31 or a deterioration detecting unit 61 instead of the output control unit 24.

[0103] Here, each functional unit of the shape detection system 100a may be configured by an independent device. These devices may be geographically or network-distributed. These devices may include, for example, an edge computer and a cloud computer.

[0104] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

[0105] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes.

[0106] [Note] [Appendix 1] a first information acquisition means for acquiring first information about a distance to the object based on the laser light irradiated onto the object and the light reflected by the object; a second information acquisition means for acquiring second information, which is different from the first information and is used to detect the surface shape of the object, based on the laser light and the reflected light; a first shape detection means for detecting an outer shape of the object using the first information; a second shape detection means for detecting the surface shape using the second information; A shape detection device comprising: [Appendix 2] The shape detection device described in Appendix 1, characterized in that the second shape detection means uses the second information to detect the presence or absence and size of unevenness on the surface of the object. [Appendix 3] The shape detection device described in Appendix 1 or Appendix 2, characterized in that the second information includes at least one of intensity information indicating the intensity of the received reflected light, polarization information indicating the polarization of the received reflected light, frequency shift information indicating the frequency shift of the received reflected light, and statistical information indicating a statistical quantity of the distance. [Appendix 4] 4. The shape detection device according to claim 1, wherein information indicating the results of detection by the first shape detection means and the second shape detection means is output. [Appendix 5] 5. A shape detection device according to any one of claims 1 to 4, characterized in that the results of detection by the first shape detection means and the second shape detection means are used to detect abnormalities in the object. [Appendix 6] A shape detection device described in any one of Supplementary Note 1 to Supplementary Note 5, characterized in that the detection results by the first shape detection means and the second shape detection means are used to detect deterioration of the object. [Appendix 7] a first information acquisition means for acquiring first information about a distance to the object based on the laser light irradiated onto the object and the light reflected by the object; a second information acquisition means for acquiring second information, which is different from the first information and is used to detect the surface shape of the object, based on the laser light and the reflected light; a first shape detection means for detecting an outer shape of the object using the first information; a second shape detection means for detecting the surface shape using the second information; A shape detection system comprising: [Appendix 8] The shape detection system described in Appendix 7, wherein the second shape detection means uses the second information to detect the presence or absence and size of irregularities on the surface of the object. [Appendix 9] The shape detection system described in Appendix 7 or Appendix 8, characterized in that the second information includes at least one of intensity information indicating the intensity of the received reflected light, polarization information indicating the polarization of the received reflected light, frequency shift information indicating the frequency shift of the received reflected light, and statistical information indicating a statistical quantity of the distance. [Appendix 10] 10. The shape detection system according to any one of Supplementary Note 7 to Supplementary Note 9, wherein information indicating the results of detection by the first shape detection means and the second shape detection means is output. [Appendix 11] A shape detection system described in any one of Supplementary Note 7 to Supplementary Note 10, characterized in that the detection results by the first shape detection means and the second shape detection means are used to detect abnormalities in the object. [Appendix 12] A shape detection system described in any one of Appendix 7 to Appendix 11, characterized in that the detection results by the first shape detection means and the second shape detection means are used to detect deterioration of the object. [Appendix 13] a first information acquisition means for acquiring first information relating to a distance to the object based on the laser light irradiated onto the object and the light reflected by the object; a second information acquisition means acquires, based on the laser light and the reflected light, second information that is different from the first information and is used to detect the surface shape of the object; a first shape detection means for detecting an outer shape of the object using the first information; A second shape detection means detects the surface shape using the second information. Shape detection methods. [Appendix 14] The shape detection method described in Appendix 13, wherein the second shape detection means uses the second information to detect the presence or absence and size of irregularities on the surface of the object. [Appendix 15] The shape detection method described in Appendix 13 or Appendix 14, characterized in that the second information includes at least one of intensity information indicating the intensity of the received reflected light, polarization information indicating the polarization of the received reflected light, frequency shift information indicating the frequency shift of the received reflected light, and statistical information indicating a statistical quantity of the distance. [Appendix 16] 16. The shape detection method according to any one of claims 13 to 15, wherein information indicating the results of detection by the first shape detection means and the second shape detection means is output. [Appendix 17] A shape detection method described in any one of Supplementary Note 13 to Supplementary Note 16, characterized in that the detection results by the first shape detection means and the second shape detection means are used to detect abnormalities in the object. [Appendix 18] A shape detection method described in any one of Supplementary Note 13 to Supplementary Note 17, characterized in that the detection results by the first shape detection means and the second shape detection means are used to detect deterioration of the object. [Appendix 19] Computer, a first information acquisition means for acquiring first information about a distance to the object based on the laser light irradiated onto the object and the light reflected by the object; a second information acquisition means for acquiring second information, which is different from the first information and is used to detect the surface shape of the object, based on the laser light and the reflected light; a first shape detection means for detecting an outer shape of the object using the first information; a second shape detection means for detecting the surface shape using the second information; A recording medium on which a program to function as a [Appendix 20] The recording medium described in Appendix 19, characterized in that the second shape detection means uses the second information to detect the presence or absence and size of unevenness on the surface of the object. [Appendix 21] The recording medium according to claim 19 or 20, wherein the second information includes at least one of intensity information indicating an intensity of the received reflected light, polarization information indicating a polarization of the received reflected light, frequency shift information indicating a frequency shift of the received reflected light, and statistical information indicating a statistical quantity of the distance. [Appendix 22] The recording medium described in any one of Appendix 19 to Appendix 21, characterized in that the program causes the computer to function as an output control means that executes control to output information indicating the results of detection by the first shape detection means and the second shape detection means. [Appendix 23] A recording medium described in any one of Appendix 19 to Appendix 22, characterized in that the results of detection by the first shape detection means and the second shape detection means are used to detect abnormalities in the object. [Appendix 24] A recording medium described in any one of Appendix 19 to Appendix 23, characterized in that the results of detection by the first shape detection means and the second shape detection means are used to detect deterioration of the object. [Explanation of symbols]

[0107] 1 LiDAR device 2,2a Shape detection device 3. Anomaly detection device 4. Deterioration detection device 11 Light output section 12 Light receiving part 21 Information Acquisition Department 21_1 1st Information Acquisition Department 21_2 2nd information acquisition section 22 First shape detection unit 23 Second shape detection unit 24 Output control section 31 Abnormality detection unit 41 Computer 51 processors 52 memory 53 Processing circuit 61 Deterioration detection unit 100,100a Shape detection system

Claims

1. a first information acquisition means for acquiring first information relating to a distance to the object based on the laser light irradiated onto the object and the light reflected by the object; a second information acquisition means for acquiring second information, which is information different from the first information and is used to detect the surface shape of the object, based on the laser light and the reflected light; a first shape detection means for detecting an outer shape of the object using the first information; a second shape detection means for detecting the surface shape using the second information; Equipped with the second information includes at least one of frequency shift information indicating a frequency shift of the received reflected light and statistical amount information indicating a statistical amount of the distance; Shape detection device.

2. 2. The shape detection device according to claim 1, wherein the second shape detection means detects the presence or absence and size of irregularities on the surface of the object using the second information.

3. A shape detection device as described in claim 1 or claim 2, wherein the statistical quantity is variance.

4. 4. The shape detection device according to claim 1, wherein information indicating the results of detection by the first shape detection means and the second shape detection means is output.

5. 5. The shape detection device according to claim 1, wherein the results of detection by the first shape detection means and the second shape detection means are used to detect an abnormality in the object.

6. 6. The shape detection device according to claim 1, wherein the results of detection by the first shape detection means and the second shape detection means are used to detect deterioration of the object.

7. a first information acquisition means for acquiring first information relating to a distance to the object based on the laser light irradiated onto the object and the light reflected by the object; a second information acquisition means for acquiring second information, which is information different from the first information and is used to detect the surface shape of the object, based on the laser light and the reflected light; a first shape detection means for detecting an outer shape of the object using the first information; a second shape detection means for detecting the surface shape using the second information; Equipped with the second information includes at least one of frequency shift information indicating a frequency shift of the received reflected light and statistical amount information indicating a statistical amount of the distance; Shape detection system.

8. 8. The shape detection system according to claim 7, wherein the second shape detection means detects the presence and size of irregularities on the surface of the object using the second information.

9. A shape detection system as described in claim 7 or claim 8, wherein the statistical quantity is variance.

10. a first information acquisition means for acquiring first information relating to a distance to the object based on the laser light irradiated onto the object and the light reflected by the object; a second information acquisition means, based on the laser light and the reflected light, acquires second information that is different from the first information and is used to detect the surface shape of the object; a first shape detection means for detecting an outer shape of the object using the first information; a second shape detection means for detecting the surface shape using the second information; the second information includes at least one of frequency shift information indicating a frequency shift of the received reflected light and statistical amount information indicating a statistical amount of the distance; Shape detection methods.

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