Material estimation device, material estimation system, and material estimation method

Abstract

Provided are a material estimation device, etc., whereby the material of an object can be estimated on the basis of particle size of the object. A material estimation device (2) comprises: an information acquisition means (21) for acquiring particle size estimation information used to estimate the size of particles in a particulate body constituting an object, on the basis of laser light radiated to the object and reflected light reflected by the object; a particle size estimation means (22) for estimating the size of particles in the particulate body using the particle size estimation information; and a material estimation means (23) for estimating the material of the object on the basis of the result of estimation by the particle size estimation means.

Description

[Technical Field]

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

[0002] Patent Document 1 discloses a technique for determining whether a target object (for example, a product or a robot's work target) is covered by a translucent container using a ToF (Time of Flight) distance sensor. The distance sensor uses a laser (see paragraph

[0019] of Patent Document 1). Note that the techniques described in Patent Document 2 and Patent Document 3 are also known as related techniques. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2015 / 125478 [Patent Document 2] Japanese Patent Application Laid-Open No. 2016-188822 [Patent Document 3] International Publication No. 2016 / 152288 Summary of the Invention [Problem to be solved by the invention]

[0004] As described above, the technology described in Patent Document 1 determines whether a target object (hereinafter sometimes referred to as "target object") is covered by a translucent container. In other words, when the target object is made of powder or granular material, the technology described in Patent Document 1 does not estimate the material of the target object based on the particle size of the powder or granular material (hereinafter sometimes referred to as "grain size" or "particle size"). In other words, the technology described in Patent Document 1 does not have a means for estimating the material of the target object based on the grain size of the target object. Therefore, the technology described in Patent Document 1 has the problem of being unable to estimate the material of the target object based on the grain size of the target object.

[0005] In view of the above-mentioned problems, an object of the present invention is to provide a material estimation device and the like that can estimate the material of an object based on the grain size of the object. [Means for solving the problem]

[0006] The material estimation device of the present invention comprises an information acquisition means for acquiring particle size estimation information used to estimate the size of particles in the powder or granular material that constitutes the object based on laser light irradiated onto the object and light reflected by the object, a particle size estimation means for estimating the size of particles in the powder or granular material using the particle size estimation information, and a material estimation means for estimating the material of the object based on the results of estimation by the particle size estimation means.

[0007] The material estimation system of the present invention comprises an information acquisition means for acquiring particle size estimation information used to estimate the size of particles in the powder or granular material that constitutes the object based on laser light irradiated onto the object and light reflected by the object, a particle size estimation means for estimating the size of particles in the powder or granular material using the particle size estimation information, and a material estimation means for estimating the material of the object based on the results of estimation by the particle size estimation means.

[0008] In the material estimation method of the present invention, an information acquisition means acquires particle size estimation information used to estimate the size of particles in the powder or granular material that constitutes the object based on laser light irradiated onto the object and light reflected by the object, a particle size estimation means estimates the size of particles in the powder or granular material using the particle size estimation information, and a material estimation means estimates the material of the object based on the results of estimation by the particle size estimation means. [Effects of the Invention]

[0009] According to the present invention, it is possible to provide a material estimation device and the like that can estimate the material of an object based on the grain size of the object. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a block diagram showing a material estimation system according to the first embodiment. [Figure 2] FIG. 2 is a block diagram showing a LiDAR device in the material estimation system according to the first embodiment. [Figure 3] FIG. 3 is a block diagram showing the material estimation device according to the first embodiment. [Figure 4] FIG. 4 is a block diagram showing an output device in the material estimation system according to the first embodiment. [Figure 5] FIG. 5 is a block diagram showing the hardware configuration of the material estimation apparatus according to the first embodiment. [Figure 6] FIG. 6 is a block diagram showing another hardware configuration of the material estimation apparatus according to the first embodiment. [Figure 7] FIG. 7 is a block diagram showing another hardware configuration of the material estimation apparatus according to the first embodiment. [Figure 8] FIG. 8 is a flowchart showing the operation of the material estimation device according to the first embodiment. [Figure 9] FIG. 9 is a block diagram showing a material estimation system according to the second embodiment. [Figure 10] FIG. 10 is a block diagram showing a material estimation apparatus according to the second embodiment. [Figure 11] FIG. 11 is a flowchart showing the operation of the material estimation apparatus according to the second embodiment. [Figure 12] FIG. 12 is a block diagram showing a material estimation apparatus according to the third embodiment. [Figure 13] FIG. 13 is a block diagram showing a material estimation 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 material estimation system according to the first embodiment. Fig. 2 is a block diagram showing a LiDAR device in the material estimation system according to the first embodiment. Fig. 3 is a block diagram showing a material estimation device according to the first embodiment. Fig. 4 is a block diagram showing an output device in the material estimation system according to the first embodiment. The material estimation system according to the first embodiment will be described with reference to Figs. 1 to 4.

[0013] As shown in FIG. 1, the material estimation system 100 includes a LiDAR device 1 and a material estimation device 2. That is, as shown in FIG. 1, the material estimation 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 also 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] Here, the object is composed of a collection of minute solid particles. That is, the object is composed of powder and granular material. Specifically, the object is, for example, a pile of raw materials in a raw material yard, or a concrete product. The pile of raw materials is, for example, a sand pile or a gravel pile. The concrete product is, for example, a concrete road, a concrete wall, or a concrete block.

[0016] As shown in Fig. 1, the material estimation system 100 includes a material estimation apparatus 2. The material estimation apparatus 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 material estimation apparatus 2 is configured by a computer. Note that this computer may be provided in a so-called "cloud." As shown in Fig. 3, the material estimation apparatus 2 includes an information acquisition unit 21, a particle size estimation unit 22, a material estimation unit 23, and an output control unit 24.

[0017] Typically, the following information is obtained 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). That is, information indicating the distance D between the position of the point where the LiDAR device 1 is installed and the position of the point where the laser light emitted in each direction is reflected by an object (including the target object) (hereinafter sometimes referred to as the "reflection point") is obtained. Such information is used, for example, to estimate the shape of the target object. Hereinafter, such information may be referred to as "distance information" or "first information."

[0018] In response to this, the information acquisition unit 21 acquires information other than distance 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). This information is used by the particle size estimation unit 22, which will be described later, to estimate the particle size of the powder or granular material that makes up the target object. In other words, this information is used to estimate the particle size (i.e., the particle size) of the target object. Hereinafter, this information may be referred to as "information for particle size estimation" or "second information."

[0019] Specifically, for example, the particle size estimation information may include information indicating the intensity of received light corresponding to the laser light emitted in each direction (hereinafter, sometimes referred to as "intensity information"). That is, the information acquisition unit 21 detects the intensity of the received light. As a result, intensity information indicating the detected intensity is acquired by the information acquisition unit 21. Note that, if 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 information acquisition unit 21. In this case, the LiDAR device 1 may output the intensity information, and the output intensity information may be acquired by the information acquisition unit 21.

[0020] The particle size estimation information may also include information indicating the polarization of the received light corresponding to the laser light emitted in each direction (hereinafter, sometimes 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 information acquisition unit 21 acquires the output polarization information.

[0021] Furthermore, the particle size estimation information may include information indicating the frequency shift of the received light corresponding to the laser light emitted in each direction (hereinafter, this may be referred to as "frequency shift information"). That is, the information acquisition unit 21 detects the frequency shift of the received light based on the frequency components contained in the received light. As a result, frequency shift information indicating the detected frequency shift is acquired by the information acquisition unit 21. Note that, if 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 information acquisition unit 21. In this case, the LiDAR device 1 may output frequency shift information, and the output frequency shift information may be acquired by the information acquisition unit 21.

[0022] Furthermore, the particle size estimation information may include information indicating statistics (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 amount information"). That is, the information acquisition unit 21 calculates each of the plurality of distances D. ToF or FMCW (Frequency Modulated Continuous Wave), for example, is used to calculate each distance D. The information acquisition unit 21 calculates the statistical amount based on the calculated distance D. As a result, statistical amount information indicating the calculated statistical amount is acquired by the information acquisition unit 21. Note that, when the LiDAR device 1 includes a signal processing unit, the statistical amount may be calculated by the signal processing unit of the LiDAR device 1 instead of being calculated by the information acquisition unit 21. In this case, the LiDAR device 1 may output statistical amount information, and the output statistical amount information may be acquired by the information acquisition unit 21.

[0023] That is, the granularity estimation information includes at least one of intensity information, polarization information, frequency shift information, and statistical information. Thus, the granularity estimation 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).

[0024] The particle size estimation unit 22 estimates the size of particles in the powder or granular material that constitutes the target object, using the particle size estimation information acquired by the information acquisition unit 21. In other words, the particle size estimation unit 22 estimates the particle size (i.e., particle size) of the target object.

[0025] That is, it is believed that the intensity of the corresponding received light changes depending on the granularity of the object. It is also believed that the polarization of the corresponding received light changes depending on the granularity of the object. It is also believed that the frequency shift of the corresponding received light changes depending on the granularity of the object. It is also believed that the statistics of the corresponding distance D change depending on the granularity of the object.

[0026] Therefore, a database (hereinafter sometimes referred to as "first database") is prepared in advance, which indicates the correspondence between 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 the particle size of the object. The first database may be stored inside the material estimation device 2, or may be stored in an external device (not shown). The particle size estimation unit 22 uses the acquired particle size estimation information to determine the particle size of the object based on the first database.

[0027] The first database may be updated based on the information used for estimation by the particle size estimation unit 22 (i.e., the acquired particle size estimation information) and the results of estimation by the particle size estimation unit 22. The first database may be updated inside the material estimation device 2. Alternatively, the first database may be updated by an external device (not shown).

[0028] Alternatively, a model (hereinafter sometimes referred to as a "first model") is prepared in advance. The model outputs a value indicating the granularity of an object when a value indicating at least one of the statistical quantities of the intensity of received light, the polarization of received light, the frequency shift of received light, and the distance D is input. The first model may be stored inside the material estimation device 2 or in an external device (not shown). The granularity estimation unit 22 inputs a value corresponding to the acquired granularity estimation information to the first model. In response to the input, the first model outputs a value indicating the granularity of the object to the granularity estimation unit 22. The granularity estimation unit 22 determines the granularity of the object based on the value output by the first model. Here, the first model is, for example, a predetermined statistical model or a machine learning model generated by prior machine learning. The machine learning model is generated, for example, by supervised learning. The supervised learning uses, for example, training data corresponding to at least one of the statistical quantities of the intensity of received light, the polarization of received light, the frequency shift of received light, and the distance D, and a correct label corresponding to the granularity of the object.

[0029] The first model may be updated based on the information used for estimation by the particle size estimation unit 22 (i.e., the acquired particle size estimation information) and the results of estimation by the particle size estimation unit 22. The first model may be updated inside the material estimation device 2. Alternatively, the first model may be updated by an external device (not shown). When the first model is a machine learning model, the external device may be, for example, a computer for machine learning. The structure of the first model or the values ​​of individual parameters (e.g., weights) in the first model may be updated by such a computer.

[0030] The material estimation unit 23 estimates the material of the object based on the result of estimation by the particle size estimation unit 22.

[0031] Specifically, for example, a database (hereinafter sometimes referred to as the "second database") indicating the correspondence between the grain size of an object and the material of the object (sand, gravel, concrete, etc.) is prepared in advance. The second database may be stored inside the material estimation device 2, or may be stored in an external device (not shown). The material estimation unit 23 acquires information (hereinafter sometimes referred to as the "grain size information") indicating the result of estimation by the grain size estimation unit 22. The material estimation unit 23 uses the acquired grain size information to determine the material of the object based on the second database.

[0032] The second database may be updated based on the information used for estimation by the material estimation unit 23 (i.e., the acquired particle size information) and the results of estimation by the material estimation unit 23. The second database may be updated inside the material estimation device 2. Alternatively, the second database may be updated by an external device (not shown).

[0033] Alternatively, for example, a model (hereinafter sometimes referred to as the "second model") that outputs a value indicating the material of an object when a value indicating the grain size of the object is input is prepared in advance. The second model may be stored inside the material estimation device 2 or may be stored in an external device (not shown). The material estimation unit 23 acquires grain size information. The material estimation unit 23 inputs a value corresponding to the acquired grain size information into the second model. In response to this input, the second model outputs a value indicating the material of the object to the material estimation unit 23. The material estimation unit 23 determines the material of the object based on the value output by the second model. Here, the second 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, learning data corresponding to the grain size of the object and a correct label corresponding to the material of the object.

[0034] The second model may be updated based on the information used for estimation by the material estimation unit 23 (i.e., the acquired particle size information) and the results of estimation by the material estimation unit 23. The second model may be updated internally in the material estimation device 2. Alternatively, the second model may be updated by an external device (not shown). When the second model is a machine learning model, the external device may be, for example, a computer for machine learning. The structure of the second model or the values ​​of individual parameters (e.g., weights) in the second model may be updated by such a computer.

[0035] For example, assume that the candidate objects include a sand pile, a gravel pile, and a concrete product. Also assume that the second database or the second model includes a value indicating the grain size corresponding to sand, a value indicating the grain size corresponding to gravel, and a value indicating the grain size corresponding to concrete. In this case, the material estimation unit 23 uses the acquired grain size information to determine whether the material of the object is sand, gravel, or concrete. This allows the object to be determined as either a sand pile, a gravel pile, or a concrete product.

[0036] The output control unit 24 controls the output of information indicating the result of estimation by the material estimation unit 23 (hereinafter, sometimes referred to as "material information"). That is, the material information includes information indicating the material of the object. The material information is output using an output device 3 (described later) (see FIG. 1).

[0037] Specifically, for example, the output control unit 24 executes control to display an image corresponding to the material information. A predetermined GUI (Graphical User Interface) may be used to display such an image. Alternatively, for example, the output control unit 24 executes control to output a sound corresponding to the material information. This makes it possible to notify a person of the material of the object. Alternatively, for example, the output control unit 24 executes control to transmit a signal corresponding to the material information to another device (not shown) or another system (not shown). A predetermined API (Application Programming Interface) may be used to provide the material information by transmitting such a signal. This makes it possible to notify the other device or another system of the material of the object.

[0038] As shown in Fig. 1, the material estimation system 100 includes an output device 3. The output device 3 is communicatively connected to the material estimation apparatus 2 via wire or wirelessly. As shown in Fig. 4, the output device 3 includes an output unit 31. The output unit 31 outputs material information under the control of the output control unit 24. The output unit 31 is configured with at least one of a display, a speaker, and a transceiver, for example. In other words, the output device 3 is configured with at least one of a display device, an audio output device, and a communication device.

[0039] In this manner, the material estimation system 100 is configured.

[0040] 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 particle size estimating unit 22 may be referred to as "particle size estimating means." The material estimating unit 23 may be referred to as "material estimating means." The output control unit 24 may be referred to as "output control means."

[0041] Next, the hardware configuration of the material estimation device 2 will be described with reference to FIGS.

[0042] As shown in each of FIGS. 5 to 7, the material estimation device 2 uses a computer 41.

[0043] 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 particle size estimation unit 22, the material estimation 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 particle size estimation unit 22, a function F3 of the material estimation unit 23, and a function F4 of the output control unit 24.

[0044] 6, the computer 41 includes a processing circuit 53. The processing circuit 53 executes processing to cause the computer 41 to function as the information acquisition unit 21, the particle size estimation unit 22, the material estimation unit 23, and the output control unit 24. This realizes functions F1 to F4.

[0045] 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.

[0046] 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).

[0047] 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.

[0048] 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).

[0049] 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.

[0050] Next, a description will be given of the operation of the material estimation system 100. More specifically, the description will focus on the operation of the material estimation device 2 with reference to the flowchart shown in FIG.

[0051] First, the information acquisition unit 21 acquires information for particle size estimation (step ST1). Specific examples of the information for particle size estimation have already been described, so a repeated description will be omitted.

[0052] Next, the particle size estimation unit 22 estimates the particle size of the powder or granular material constituting the target object using the particle size estimation information acquired in step ST1 (step ST2). A specific example of such a particle size estimation method has already been described, and therefore, a repeated description will be omitted.

[0053] Next, the material estimation unit 23 estimates the material of the object based on the result of estimation in step ST2 (step ST3). A specific example of such a material estimation method has already been described, so a repeated description will be omitted.

[0054] Next, the output control unit 24 executes control to output the material information based on the result of the estimation in step ST3 (step ST4), thereby outputting the material information.

[0055] Next, a modification of the material estimation system 100 will be described.

[0056] The information for particle size estimation 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 estimate the particle size of an object.

[0057] The method of estimating the particle size in the particle size estimation unit 22 is not limited to the above specific example, and any method may be used as long as it estimates the particle size of the object using the acquired particle size estimation information.

[0058] The method of estimating the material in the material estimation unit 23 is not limited to the above specific example, and any method may be used as long as it estimates the material of the object based on the grain size of the object.

[0059] The material estimation 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 material estimation device 2 may be configured integrally with the LiDAR device 1.

[0060] The material estimation device 2 may include an output unit 31. In this case, the output device 3 is not required. In other words, the material estimation device 2 may be configured integrally with the output device 3.

[0061] The databases used for estimation in material estimation device 2 are not limited to the first database and the second database. For example, the following database (hereinafter sometimes referred to as the "third database") may be generated based on the history of the second information acquired by information acquisition unit 21 and the history of the results of estimation by material estimation unit 23. That is, the third database is a database indicating the correspondence between at least one of the statistical quantities of the intensity of received light, the polarization of received light, the frequency shift of received light, and the distance D and the material of the object. The third database may be generated within material estimation device 2 or may be generated by an external device (not shown). After the third database is generated, material estimation unit 23 uses the second information acquired by information acquisition unit 21 to estimate the material of the object based on the generated third database. That is, in this case, the second information is used to estimate the material of the object instead of the grain size of the object.

[0062] After the third database is generated, the generated third database may be updated based on the second information acquired by the information acquisition unit 21 and the result of estimation by the material estimation unit 23. The updating of the third database may be performed inside the material estimation device 2. Alternatively, the updating of the third database may be performed by an external device (not shown).

[0063] The models used for estimation in the material estimation apparatus 2 are not limited to the first model and the second model. For example, the following model (hereinafter sometimes referred to as the "third model") may be generated based on the history of the second information acquired by the information acquisition unit 21 and the history of the results of estimation by the material estimation unit 23. That is, the third model is a model that outputs a value indicating the material of an object when a value indicating at least one of the statistics of the intensity of received light, the polarization of received light, the frequency shift of received light, and the distance D is input. The third model may be generated inside the material estimation apparatus 2 or may be generated by an external device (not shown). The third model is generated using predetermined machine learning (e.g., supervised learning). After the third model is generated, the material estimation unit 23 inputs a value corresponding to the second information acquired by the information acquisition unit 21 into the generated third model. In response to this input, the third model outputs a value indicating the material of the object to the material estimation unit 23. The material estimation unit 23 estimates the material of the object based on the value output by the third model. That is, in this case, the second information is used to estimate the material of the object instead of estimating the grain size of the object.

[0064] After the third model is generated, the generated third model may be updated based on the second information acquired by the information acquisition unit 21 and the result of estimation by the material estimation unit 23. The updating of the third model may be performed inside the material estimation apparatus 2. Alternatively, the updating of the third model may be performed by an external device (not shown). The external device may be, for example, a computer for machine learning. The structure of the third model or the values ​​of individual parameters (e.g., weights) in the third model may be set or updated by such a computer.

[0065] Next, the effects of the material estimation system 100 will be described.

[0066] As described above, the information acquisition unit 21 acquires particle size estimation information used to estimate the particle size (particle size) of the powder or granular material that constitutes the object, based on the laser light irradiated onto the object and the light reflected by the object. The particle size estimation unit 22 estimates the particle size (particle size) of the powder or granular material using the particle size estimation information. The material estimation unit 23 estimates the material of the object based on the estimation result by the particle size estimation unit 22.

[0067] In this way, by using laser light and reflected light (i.e., by using LiDAR), it is possible to detect, for example, the intensity of the received light, the polarization of the received light, or the frequency shift of the received light, or to calculate the statistics of the distance D. This makes it possible to acquire information for particle size estimation. Furthermore, by using the acquired information for particle size estimation, it is possible to estimate the particle size of the object. This is because the values ​​of the parameters included in the information for particle size estimation (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 particle size of the object. As a result, it is possible to estimate the material of the object based on the estimated particle size.

[0068] Furthermore, material estimation unit 23 estimates the material of the object using a predetermined database (second database or third database). Material estimation system 100 updates the database (second database or third database) based on the results of estimation by material estimation unit 23. By updating such a database, for example, it is possible to gradually improve the accuracy of estimation by material estimation unit 23.

[0069] The particle size estimation 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 the statistical amount of the distance D to the object. By using this information, the particle size of the object can be estimated.

[0070] Furthermore, the material estimation system 100 outputs information (material information) indicating the result of estimation by the material estimation unit 23. This allows a person to be informed of the material of the object, or the material of the object to be notified to another device or system.

[0071] [Second embodiment] Fig. 9 is a block diagram showing a material estimation system according to a second embodiment. Fig. 10 is a block diagram showing a material estimation device according to the second embodiment. The material estimation system according to the second embodiment will be described with reference to Figs. 9 and 10. In Fig. 9, blocks similar to those shown in Fig. 1 are given the same reference numerals, and descriptions thereof will be omitted. In Fig. 10, blocks similar to those shown in Fig. 3 are given the same reference numerals, and descriptions thereof will be omitted.

[0072] 9, the material estimation system 100a includes a LiDAR device 1, a material estimation device 2a, and an output device 3. As shown in Fig. 10, the material estimation device 2a includes an information acquisition unit 21a, a particle size estimation unit 22, a material estimation unit 23a, an output control unit 24a, and a shape estimation unit 25.

[0073] The information acquiring unit 21a acquires second information (i.e., particle size estimation 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 (i.e., the received light) received by the LiDAR device 1. In addition, the information acquiring unit 21a acquires first information (i.e., distance information) based on these lights.

[0074] Specifically, for example, the information acquiring unit 21a calculates the distance D using ToF. That is, in this case, the LiDAR device 1 emits pulsed laser light in each direction. The information acquiring unit 21a calculates the 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. In this way, distance information indicating the distance D is acquired.

[0075] Alternatively, for example, the information acquisition unit 21a calculates the distance D by FMCW. That is, in this case, the LiDAR device 1 has a function of performing predetermined frequency modulation on the laser light emitted in each direction and a function of performing coherent detection on the corresponding received light. The information acquisition unit 21a 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.

[0076] 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").

[0077] 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 information acquisition unit 21a. In this case, the LiDAR device 1 may output distance information, and the output distance information may be acquired by the information acquisition unit 21a.

[0078] Furthermore, by generating distance information, a plurality of distances D corresponding to a plurality of emission directions are calculated. The calculated plurality of distances D may be used to generate statistical information.

[0079] The shape estimation unit 25 estimates the shape (more specifically, the outer shape) of the object using the distance information acquired by the information acquisition unit 21a. Specifically, for example, the shape estimation unit 25 calculates coordinate values ​​indicating the positions of individual reflection points using the acquired distance information. The shape estimation unit 25 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 shape of the object. In this way, the shape of the object is detected.

[0080] 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 shape estimation unit 25 may extract the point cloud corresponding to the target object from the plotted point cloud by grouping the point clouds based on the inter-point distance or the result of plane detection. As a result, the shape estimation unit 25 may exclude the point cloud corresponding to the other object from the three-dimensional model.

[0081] The material estimation unit 23a estimates the material of the object based on the result of estimation by the particle size estimation unit 22. The method of estimating the material in this case is the same as that described in the first embodiment, and therefore will not be described again.

[0082] Alternatively, the material estimation unit 23a estimates the material of the object based on the results of estimation by the particle size estimation unit 22 and the results of estimation by the shape estimation unit 25. Hereinafter, such an estimation method will be described.

[0083] Specifically, for example, a database (hereinafter sometimes referred to as the "fourth database") indicating the correspondence between the grain size and shape of an object and the material of the object is prepared in advance. The fourth database may be stored inside the material estimation device 2a, or may be stored in an external device (not shown). The material estimation unit 23a acquires information indicating the result of estimation by the grain size estimation unit 22 (i.e., grain size information), and also acquires information indicating the result of estimation by the shape estimation unit 25 (hereinafter sometimes referred to as "shape information"). The material estimation unit 23a estimates the material of the object based on the fourth database using the acquired grain size information and the acquired shape information.

[0084] The fourth database may be updated based on the information used for estimation by the material estimation unit 23a (i.e., the acquired particle size information and the acquired shape information) and the results of estimation by the material estimation unit 23. The fourth database may be updated inside the material estimation device 2a. Alternatively, the fourth database may be updated by an external device (not shown).

[0085] Alternatively, for example, a model (hereinafter sometimes referred to as a "fourth model") that outputs a value indicating the material of an object when a value indicating the grain size and a value indicating the shape of the object are input is prepared in advance. The fourth model may be stored inside the material estimation device 2a or may be stored in an external device (not shown). The material estimation unit 23a acquires grain size information and shape information. The material estimation unit 23a inputs values ​​corresponding to the acquired grain size information and values ​​corresponding to the acquired shape information to the fourth model. In response to this input, the fourth model outputs a value indicating the material of the object to the material estimation unit 23a. The material estimation unit 23a determines the material of the object based on the value output by the fourth model. Here, the fourth 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, learning data corresponding to the grain size and shape of the object and a correct label corresponding to the material of the object.

[0086] The fourth model may be updated based on the information used in the estimation by the material estimation unit 23a (i.e., the acquired particle size information and the acquired shape information) and the results of the estimation by the material estimation unit 23a. The fourth model may be updated inside the material estimation device 2a. Alternatively, the fourth model may be updated by an external device (not shown). When the fourth model is a machine learning model, the external device may be, for example, a computer for machine learning. The structure of the fourth model or the values ​​of individual parameters (e.g., weights) in the fourth model may be updated by such a computer.

[0087] For example, suppose that the candidate objects include a sand pile, a gravel pile, and a concrete product. In this case, it is highly likely that it is possible to determine whether the object is a pile of raw materials (sand pile or gravel pile) or another object (concrete product) based on the shape of the object. When the object is a pile of raw materials, it is highly likely that it is possible to determine whether the object is a sand pile or a gravel pile based on the grain size of the object. That is, it is possible to determine whether the object is a sand pile, a gravel pile, or a concrete product based on the grain size and shape of the object. In other words, it is possible to determine whether the material of the object is sand, gravel, or concrete. Such determination is realized by the material estimation unit 23a using the fourth database or the fourth model.

[0088] The following mainly describes an example in which the material estimation unit 23a estimates the material of the object based on the results of estimation by the particle size estimation unit 22 and the results of estimation by the shape estimation unit 25.

[0089] The output control unit 24a executes control to output information indicating the result of estimation by the material estimation unit 23a (i.e., material information). In addition to this, the output control unit 24a may execute control to output information indicating the result of estimation by the shape estimation unit 25 (i.e., shape information). That is, the output device 3 may output shape information in addition to outputting material information. This makes it possible to inform a person of the material and shape of the object. Alternatively, the material and shape of the object can be notified to another device (not shown) or another system (not shown).

[0090] The following description will be centered on an example in which the output control unit 24a executes control to output material information and shape information.

[0091] In this way, the material estimation system 100a is configured.

[0092] Hereinafter, the information acquisition unit 21a may be referred to as "information acquisition means." Furthermore, the material estimation unit 23a may be referred to as "material estimation means." Furthermore, the output control unit 24a may be referred to as "output control means." Furthermore, the shape estimation unit 25 may be referred to as "shape estimation means."

[0093] The hardware configuration of the material estimation device 2a is the same as that described in the first embodiment with reference to Figures 5 to 7. Therefore, a detailed description will be omitted.

[0094] That is, the material estimation device 2a has a function F1a of the information acquisition unit 21a, a function F2 of the particle size estimation unit 22, a function F3a of the material estimation unit 23a, a function F4a of the output control unit 24a, and a function F5 of the shape estimation unit 25. The functions F1a, F2, F3a, F4a, and F5 may be realized by the processor 51 and memory 52, or may be realized by the processing circuit 53.

[0095] Here, the processor 51 may include dedicated processors corresponding to the functions F1a, F2, F3a, F4a, and F5, the memory 52 may include dedicated memories corresponding to the functions F1a, F2, F3a, F4a, and F5, and the processing circuit 53 may include dedicated processing circuits corresponding to the functions F1a, F2, F3a, F4a, and F5.

[0096] Next, the operation of the material estimation system 100a will be described. More specifically, the operation of the material estimation device 2a will be mainly described with reference to the flowchart shown in Fig. 11. In Fig. 11, steps that are the same as those shown in Fig. 8 are given the same reference numerals.

[0097] First, the information acquisition unit 21a acquires distance information and information for granularity estimation (step ST1a). A specific example of the method for acquiring distance information is as described above. Also, a specific example of the information for granularity estimation is as described in the first embodiment. Therefore, a repeated explanation will be omitted.

[0098] Next, the shape estimation unit 25 estimates the shape of the object using the distance information acquired in step ST1 (step ST5). A specific example of such a shape estimation method has already been described, so a repeated description will be omitted.

[0099] Next, the particle size estimation unit 22 estimates the particle size of the powder or granular material constituting the target object using the particle size estimation information acquired in step ST1 (step ST2). A specific example of such a particle size estimation method is as explained in the first embodiment, and therefore will not be explained again.

[0100] The order of execution of the process of step ST5 and the process of step ST2 is arbitrary. That is, as shown in Fig. 11, the process of step ST2 may be executed after the process of step ST5. Alternatively, the process of step ST5 may be executed after the process of step ST2. Alternatively, the process of step ST5 and the process of step ST2 may be executed in parallel.

[0101] Next, the material estimation unit 23a estimates the material of the object based on the results of estimation in step ST5 and the results of estimation in step ST2 (step ST3a). A specific example of such a method for estimating the material has already been described, and therefore a repeated description will be omitted.

[0102] Next, the output control unit 24a executes control to output the shape information and material information (step ST4a), whereby the shape information and material information are output.

[0103] Next, a modification of the material estimation system 100a will be described.

[0104] The material estimation system 100a can employ various modified examples similar to those described in the first embodiment. In addition to this, the material estimation system 100a can employ the following modified examples.

[0105] The databases used for estimation in the material estimation device 2a are not limited to the first database and the fourth database. For example, the following database (hereinafter sometimes referred to as the “fifth database”) may be generated based on the history of the second information acquired by the information acquisition unit 21a, the history of the results of estimation by the shape estimation unit 25, and the history of the results of estimation by the material estimation unit 23a. That is, the fifth database is a database indicating a correspondence relationship between at least one of the statistics of the intensity of the received light, the polarization of the received light, the frequency shift of the received light, and the distance D, as well as the shape of the object and the material of the object. The fifth database may be generated within the material estimation device 2a or may be generated by an external device (not shown). After the fifth database is generated, the material estimation unit 23a estimates the material of the object based on the generated fifth database using the second information acquired by the information acquisition unit 21a and information indicating the results of estimation by the shape estimation unit 25 (i.e., shape information). That is, in this case, the second information is used to estimate the material of the object instead of estimating the grain size of the object.

[0106] After the fifth database is generated, the generated fifth database may be updated based on the second information acquired by the information acquisition unit 21a, the results of estimation by the shape estimation unit 25, and the results of estimation by the material estimation unit 23a. The updating of the fifth database may be performed inside the material estimation device 2a. Alternatively, the generation and updating of the fifth database may be performed by an external device (not shown).

[0107] The models used for estimation in the material estimation device 2a are not limited to the first model and the fourth model. For example, the following model (hereinafter sometimes referred to as the “fifth model”) may be generated based on the history of particle size estimation information acquired by the information acquisition unit 21a, the history of the results of estimation by the shape estimation unit 25, and the history of the results of estimation by the material estimation unit 23. That is, the fifth model is a model that outputs a value indicating the material of an object when a value indicating at least one of the statistics of the intensity of received light, the polarization of received light, the frequency shift of received light, and the distance D, and a value indicating the shape of the object are input. The fifth model may be generated within the material estimation device 2a or may be generated by an external device (not shown). A predetermined machine learning method (e.g., supervised learning) is used to generate the fifth model. After the fifth model is generated, the material estimation unit 23a inputs a value corresponding to the particle size estimation information acquired by the information acquisition unit 21a and a value corresponding to the result of estimation by the shape estimation unit 25 into the generated fifth model. In response to this input, the fifth model outputs a value indicating the material of the object to the material estimation unit 23a. The material estimation unit 23a estimates the material of the object based on the value output by the fifth model. That is, in this case, the second information is used to estimate the material of the object, instead of estimating the grain size of the object.

[0108] After the fifth model is generated, the generated fifth model may be updated based on the particle size estimation information acquired by the information acquisition unit 21a, the results of estimation by the shape estimation unit 25, and the results of estimation by the material estimation unit 23a. The updating of the fifth model may be performed inside the material estimation device 2a. Alternatively, the generation and updating of the fifth model may be performed by an external device (not shown). The external device may be, for example, a computer for machine learning. The structure of the fifth model or the values ​​of individual parameters (e.g., weights) in the fifth model may be set or updated by such a computer.

[0109] Next, the effects of the material estimation system 100a will be described.

[0110] By using the material estimation system 100a, various effects similar to those described in the first embodiment can be obtained. In addition to these, the following effects can be obtained.

[0111] As described above, the information acquisition unit 21a further acquires distance information regarding the distance D to the object based on the laser light and the reflected light. The shape estimation unit 25 estimates the shape of the object using the distance information. The material estimation unit 23a estimates the material of the object based on the results of estimation by the shape estimation unit 25 and the results of estimation by the particle size estimation unit 22.

[0112] In this way, by using the laser light and the reflected light (i.e., by using LiDAR), the distance D to each reflection point can be calculated. This allows distance information to be acquired. Furthermore, by using the acquired distance information, the shape of the object can be estimated. This is because, for example, a three-dimensional model of the object can be generated. The result of such shape estimation can then be used to estimate the material of the object. By estimating the material of the object based on the shape and granularity of the object, the number of parameters used to estimate the material increases compared to when estimating the material of the object based on the granularity of the object. This allows, for example, to improve the accuracy of estimating the material.

[0113] Furthermore, by using laser light and reflected light (i.e., by using LiDAR), the grain size of an object can be estimated using the same device (e.g., LiDAR device 1) as the device used to estimate the shape of the object. This allows the number of devices included in material estimation system 100a to be reduced compared to when grain size is estimated using a device different from the device used to estimate the shape. As a result, the configuration of material estimation system 100a can be simplified.

[0114] [Third embodiment] Fig. 12 is a block diagram showing a material estimation device according to a third embodiment. The material estimation device according to the third embodiment will be described with reference to Fig. 12. Fig. 13 is a block diagram showing a material estimation system according to the third embodiment. The material estimation system according to the third embodiment will be described with reference to Fig. 13. In each of Figs. 12 and 13, blocks similar to those shown in Fig. 3 are designated by the same reference numerals, and descriptions thereof will be omitted.

[0115] Here, the material estimation apparatus 2 according to the first embodiment and the material estimation apparatus 2a according to the second embodiment are each an example of the material estimation apparatus 2b according to the third embodiment. Furthermore, the material estimation system 100 according to the first embodiment and the material estimation system 100a according to the second embodiment are each an example of the material estimation system 100b according to the third embodiment.

[0116] 12, material estimation device 2b includes information acquisition unit 21, particle size estimation unit 22, and material estimation unit 23. In this case, output control unit 24 may be provided outside material estimation device 2b.

[0117] 13, material estimation system 100b includes information acquisition unit 21, particle size estimation unit 22, and material estimation unit 23. In this case, LiDAR device 1 may be provided outside material estimation system 100b. In other words, light emission unit 11 and light reception unit 12 may be provided outside material estimation system 100b. In this case, output control unit 24 may be provided outside material estimation system 100b. In this case, output device 3 may be provided outside material estimation system 100b. In other words, output unit 31 may be provided outside material estimation system 100b.

[0118] In these cases, the same effects as those described in the first embodiment can be obtained.

[0119] That is, the information acquisition unit 21 acquires particle size estimation information used to estimate the particle size (particle size) of the powder or granular material that constitutes the object, based on the laser light irradiated onto the object and the light reflected by the object. The particle size estimation unit 22 estimates the particle size (particle size) of the powder or granular material using the particle size estimation information. The material estimation unit 23 estimates the material of the object based on the estimation result by the particle size estimation unit 22.

[0120] In this way, by using laser light and reflected light (i.e., by using LiDAR), it is possible to detect, for example, the intensity of the received light, the polarization of the received light, or the frequency shift of the received light, or to calculate the statistics of the distance D. This makes it possible to acquire information for particle size estimation. Furthermore, by using the acquired information for particle size estimation, it is possible to estimate the particle size of the object. This is because the values ​​of the parameters included in the information for particle size estimation (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 particle size of the object. As a result, it is possible to estimate the material of the object based on the estimated particle size.

[0121] Note that material estimation device 2b may include information acquisition unit 21a, material estimation unit 23a, and shape estimation unit 25, instead of information acquisition unit 21 and material estimation unit 23. Also, material estimation system 100b may include information acquisition unit 21a, material estimation unit 23a, and shape estimation unit 25, instead of information acquisition unit 21 and material estimation unit 23.

[0122] Furthermore, material estimation system 100b may include light emitting unit 11 and light receiving unit 12. Furthermore, material estimation system 100b may include output control unit 24 or output control unit 24a. Furthermore, material estimation system 100b may include output unit 31.

[0123] Here, each functional unit of the material estimation system 100b may be configured as an independent device. These devices may be geographically or network-distributed. These devices may include, for example, an edge computer and a cloud computer.

[0124] 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.

[0125] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes.

[0126] [Note] [Appendix 1] an information acquisition means for acquiring particle size estimation information used to estimate the particle size of a powder or granular material constituting the object based on the laser light irradiated onto the object and the light reflected by the object; a particle size estimation means for estimating the size of particles in the powder or granular material using the particle size estimation information; a material estimation means for estimating the material of the object based on the result of estimation by the particle size estimation means; A material estimation device comprising: [Appendix 2] the information acquisition means further acquires distance information relating to a distance to the object based on the laser light and the reflected light; a shape estimation means for estimating a shape of the object using the distance information; The material estimation means estimates the material of the object based on the result of estimation by the shape estimation means and the result of estimation by the particle size estimation means. 2. A material estimation device according to claim 1, [Appendix 3] the material estimation means estimates the material of the object using a predetermined database; The database is updated based on the result of estimation by the material estimation means. 3. A material estimation device according to claim 1 or 2. [Appendix 4] 4. The material estimation device according to claim 1, 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 amount information indicating a statistical amount of a distance to the object. [Appendix 5] 5. The material estimation device according to claim 1, wherein information indicating a result of estimation by said material estimation means is output. [Appendix 6] an information acquisition means for acquiring particle size estimation information used to estimate the particle size of a powder or granular material constituting the object based on the laser light irradiated onto the object and the light reflected by the object; a particle size estimation means for estimating the size of particles in the powder or granular material using the particle size estimation information; a material estimation means for estimating the material of the object based on the result of estimation by the particle size estimation means; A material estimation system comprising: [Appendix 7] the information acquisition means further acquires distance information relating to a distance to the object based on the laser light and the reflected light; a shape estimation means for estimating a shape of the object using the distance information; The material estimation means estimates the material of the object based on the result of estimation by the shape estimation means and the result of estimation by the particle size estimation means. 7. A material estimation system according to claim 6, [Appendix 8] the material estimation means estimates the material of the object using a predetermined database; The database is updated based on the result of estimation by the material estimation means. 8. The material estimation system according to claim 6 or 7, [Appendix 9] The material estimation system according to any one of Supplementary Note 6 to Supplementary Note 8, wherein the particle size estimation 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 amount of a distance to the object. [Appendix 10] 10. The material estimation system according to any one of claims 6 to 9, wherein information indicating a result of estimation by said material estimation means is output. [Appendix 11] an information acquisition means acquires particle size estimation information used to estimate the size of particles in powder or granular material constituting the object based on the laser light irradiated onto the object and the light reflected by the object; a particle size estimation means for estimating the size of particles in the powder or granular material using the particle size estimation information; A material estimation means estimates the material of the object based on the result of estimation by the particle size estimation means. Material estimation method. [Appendix 12] the information acquisition means further acquires distance information relating to a distance to the object based on the laser light and the reflected light; a shape estimation means for estimating a shape of the object using the distance information; The material estimation means estimates the material of the object based on the result of estimation by the shape estimation means and the result of estimation by the particle size estimation means. 12. The material estimation method according to claim 11, [Appendix 13] the material estimation means estimates the material of the object using a predetermined database; The database is updated based on the result of estimation by the material estimation means. 13. The material estimation method according to claim 11 or 12. [Appendix 14] 14. The material estimation method according to claim 11, wherein the particle size estimation 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 amount of a distance to the object. [Appendix 15] 15. The material estimation method according to any one of claims 11 to 14, wherein information indicating the result of estimation by said material estimation means is output. [Appendix 16] Computer, an information acquisition means for acquiring particle size estimation information used to estimate the particle size of a powder or granular material constituting the object based on the laser light irradiated onto the object and the light reflected by the object; a particle size estimation means for estimating the size of particles in the powder or granular material using the particle size estimation information; a material estimation means for estimating the material of the object based on the result of estimation by the particle size estimation means; A recording medium on which a program to function as a [Appendix 17] the information acquisition means acquires distance information relating to a distance to the object based on the laser light and the reflected light; the program causes the computer to function as shape estimation means that estimates a shape of the object using the distance information; The material estimation means estimates the material of the object based on the result of estimation by the shape estimation means and the result of estimation by the particle size estimation means. 17. The recording medium according to claim 16, [Appendix 18] the material estimation means estimates the material of the object using a predetermined database; The program updates the database based on the result of estimation by the material estimation means. 18. The recording medium according to claim 16 or 17. [Appendix 19] The recording medium according to any one of appendices 16 to 18, wherein the particle size estimation 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 to the object. [Appendix 20] 20. The recording medium according to claim 16, wherein the program causes the computer to function as output control means for executing control to output information indicating a result of estimation by the material estimation means. [Explanation of symbols]

[0127] 1 LiDAR device 2,2a,2b Material estimation device 3 Output Devices 11 Light output section 12 Light receiving part 21,21a Information acquisition section 22 Particle size estimation section 23,23a Material estimation part 24, 24a Output control section 25 Shape estimation section 31 Output section 41 Computer 51 processors 52 memory 53 Processing circuit 100, 100a, 100b Material Estimation System

Claims

1. an information acquisition means for acquiring particle size estimation information used to estimate the particle size of a powder or granular material constituting the object based on the laser light irradiated onto the object and the light reflected by the object; a particle size estimation means for estimating the size of particles in the powder or granular material using the particle size estimation information; a material estimation means for estimating the material of the object based on the result of estimation by the particle size estimation means; Equipped with the particle size estimation information includes frequency shift information indicating a frequency shift of the received reflected light; Material estimation device.

2. the information acquisition means further acquires distance information relating to a distance to the object based on the laser light and the reflected light; a shape estimation means for estimating a shape of the object using the distance information; The material estimation means estimates the material of the object based on the result of estimation by the shape estimation means and the result of estimation by the particle size estimation means.

2. The material estimation device according to claim 1.

3. the material estimation means estimates the material of the object using a predetermined database; The database is updated based on the result of estimation by the material estimation means.

2. The material estimation device according to claim 1.

4. outputting information indicating the result of estimation by the material estimation means; 4. The material estimation device according to claim 1, wherein the material is a material having a high density.

5. an information acquisition means for acquiring particle size estimation information used to estimate the particle size of a powder or granular material constituting the object based on the laser light irradiated onto the object and the light reflected by the object; a particle size estimation means for estimating the size of particles in the powder or granular material using the particle size estimation information; a material estimation means for estimating the material of the object based on the result of estimation by the particle size estimation means; Equipped with the particle size estimation information includes frequency shift information indicating a frequency shift of the received reflected light; Material estimation system.

6. the information acquisition means further acquires distance information relating to a distance to the object based on the laser light and the reflected light; a shape estimation means for estimating a shape of the object using the distance information; The material estimation means estimates the material of the object based on the result of estimation by the shape estimation means and the result of estimation by the particle size estimation means.

6. The material estimation system according to claim 5.

7. the material estimation means estimates the material of the object using a predetermined database; The database is updated based on the result of estimation by the material estimation means.

7. The material estimation system according to claim 5 or 6.

8. an information acquisition means acquires particle size estimation information used to estimate the size of particles in powder or granular material constituting the object based on the laser light irradiated onto the object and the light reflected by the object; a particle size estimation means for estimating the size of particles in the powder or granular material using the particle size estimation information; A material estimation method, wherein a material estimation means estimates a material of the object based on a result of estimation by the particle size estimation means, the particle size estimation information includes frequency shift information indicating a frequency shift of the received reflected light; Material estimation method.

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