WO2023171659A1 - Information processing device, distance measuring system, control method, program, and storage medium - Google Patents

Information processing device, distance measuring system, control method, program, and storage medium Download PDF

Info

Publication number
WO2023171659A1
WO2023171659A1 PCT/JP2023/008529 JP2023008529W WO2023171659A1 WO 2023171659 A1 WO2023171659 A1 WO 2023171659A1 JP 2023008529 W JP2023008529 W JP 2023008529W WO 2023171659 A1 WO2023171659 A1 WO 2023171659A1
Authority
WO
WIPO (PCT)
Prior art keywords
distance
measurement data
liquid surface
reflective member
water surface
Prior art date
Application number
PCT/JP2023/008529
Other languages
French (fr)
Japanese (ja)
Inventor
逸平 難波田
令司 松本
Original Assignee
パイオニア株式会社
パイオニアスマートセンシングイノベーションズ株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by パイオニア株式会社, パイオニアスマートセンシングイノベーションズ株式会社 filed Critical パイオニア株式会社
Publication of WO2023171659A1 publication Critical patent/WO2023171659A1/en

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C13/00—Surveying specially adapted to open water, e.g. sea, lake, river or canal
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C3/00—Measuring distances in line of sight; Optical rangefinders
    • G01C3/02—Details
    • G01C3/06—Use of electric means to obtain final indication
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F23/00—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm
    • G01F23/22—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm by measuring physical variables, other than linear dimensions, pressure or weight, dependent on the level to be measured, e.g. by difference of heat transfer of steam or water
    • G01F23/28—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm by measuring physical variables, other than linear dimensions, pressure or weight, dependent on the level to be measured, e.g. by difference of heat transfer of steam or water by measuring the variations of parameters of electromagnetic or acoustic waves applied directly to the liquid or fluent solid material
    • G01F23/284—Electromagnetic waves
    • G01F23/292—Light, e.g. infrared or ultraviolet
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • G01S17/06—Systems determining position data of a target
    • G01S17/46—Indirect determination of position data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/30—Assessment of water resources

Definitions

  • the present disclosure relates to technology for processing measured data.
  • Patent Document 1 discloses that by appropriately controlling the emission direction (scanning direction) of repeatedly emitted light pulses, the surrounding space is scanned, and by observing the returned light, information about objects existing in the surroundings is obtained.
  • a lidar is disclosed that generates point cloud data representing information such as distance and reflectance.
  • the main object of the present disclosure is to provide an information processing device, a distance measuring system, a control method, a program, and a storage medium storing the program that can accurately measure the distance to a liquid surface.
  • the claimed invention is: an acquisition means for acquiring measurement data generated by an optical measurement device that performs distance measurement by receiving reflected light of the emitted light directed toward the liquid surface; Extracting means for extracting reflective member measurement data obtained by measuring a reflective member provided at a position where the light reflected on the liquid surface is irradiated from the measurement data; Calculation means for calculating a distance to the liquid surface based on the reflective member measurement data;
  • This is an information processing device having:
  • the claimed invention is The computer is Obtain measurement data generated by an optical measurement device that measures distance by receiving the reflected light emitted from the liquid surface. extracting reflective member measurement data obtained by measuring a reflective member provided at a position where the light reflected on the liquid surface is irradiated from the measurement data; calculating a distance to the liquid surface based on the reflective member measurement data; This is a control method.
  • the claimed invention is Obtain measurement data generated by an optical measurement device that measures distance by receiving the reflected light emitted from the liquid surface. extracting reflective member measurement data obtained by measuring a reflective member provided at a position where the light reflected on the liquid surface is irradiated from the measurement data; This is a program that causes a computer to execute a process of calculating a distance to the liquid surface based on the reflective member measurement data.
  • FIG. 1 shows a schematic configuration of a ranging system according to an embodiment.
  • 1 shows a schematic configuration of a rider according to an embodiment. It is an example of the flowchart based on an Example. A cross-sectional view of a road irradiated with laser light emitted by a lidar is shown.
  • This is a first configuration example of a ranging system according to a modification.
  • This is a second configuration example of a ranging system according to a modification.
  • An example of correspondence between the structure of a reflective object and obtained point cloud data according to a modified example is shown.
  • FIG. 3 is a configuration diagram of a rider system according to a modified example.
  • the information processing device includes an acquisition unit that acquires measurement data generated by an optical measurement device that is directed toward a liquid surface and performs distance measurement by receiving reflected light of emitted light; extracting means for extracting, from the measurement data, reflecting member measurement data obtained by measuring a reflecting member provided at a position where the light reflected by the liquid surface is irradiated; calculation means for calculating the distance to.
  • the information processing device can accurately measure the distance to the liquid surface based on the measurement data of the reflecting member irradiated with the light reflected by the liquid surface.
  • the optical measuring device directly faces the liquid surface, and the reflecting member is provided close to the optical measuring device. According to this aspect, the information processing device can suitably acquire measurement data regarding the reflecting member irradiated with the light reflected on the liquid surface.
  • the reflecting member is provided at a position where the distance from the liquid surface is the same as that of the optical measuring device, and the calculating means is configured to calculate a measured distance indicated by the reflecting member measurement data. 1/2 is calculated as the distance to the liquid surface.
  • the information processing device can accurately measure the distance to the liquid surface based on the measurement data of the reflecting member.
  • the information processing device further includes estimating means for estimating the state of the liquid level based on the measurement data.
  • the estimating means estimates the degree of ripples on the liquid surface based on the distance to the liquid surface calculated in time series by the calculating means. According to this aspect, the information processing device can suitably estimate the degree of ripples on the liquid surface.
  • a pattern corresponding to a position is formed on the reflective member, and the calculation means recognizes the measured position on the reflective member based on the reflective member measurement data, A distance to the liquid surface is calculated based on the position. According to this aspect, the information processing device can accurately measure the distance to the liquid surface.
  • a distance measurement system in another preferred embodiment, includes an optical measurement device that is directed toward a liquid surface and measures distance by receiving reflected light of emitted light, and an information processing device according to any of the above. and has. This aspect allows the distance measuring system to accurately measure the distance to the liquid surface.
  • the computer acquires measurement data generated by an optical measurement device that measures distance by receiving reflected light of emitted light directed toward a liquid surface; , extracting reflective member measurement data obtained by measuring a reflective member provided at a position where the light reflected by the liquid surface is irradiated, and calculating a distance to the liquid surface based on the reflective member measurement data.
  • the computer can accurately measure the distance to the liquid surface.
  • measurement data generated by an optical measurement device that performs distance measurement by receiving reflected light emitted from a liquid surface is obtained, and from the measurement data, the A computer performs a process of extracting reflective member measurement data obtained by measuring a reflective member provided at a position where the light reflected on the liquid surface is irradiated, and calculating a distance to the liquid surface based on the reflective member measurement data.
  • This is a program that is executed by By executing this program, the computer can accurately measure the distance to the liquid surface.
  • the program is stored on a storage medium.
  • FIG. 1 shows a schematic configuration of a ranging system according to this embodiment.
  • the distance measuring system is a system that measures the distance to an arbitrary liquid surface (liquid surface) such as a river or lake, and includes a lidar 100, a reflecting member 200 (200A, 200B), and a hood. 300.
  • the liquid surface will be the water surface.
  • the distance measuring system measures the distance from the lidar 100 to the water surface (simply called "water surface distance") by measuring the reflective member 200 provided near the lidar 100 by utilizing the reflection of the laser beam from the lidar 100 on the water surface. ) is calculated.
  • the lidar 100 irradiates a laser beam over a predetermined angular range (field of view) in the horizontal and vertical directions, and receives light that is reflected back from an object (also referred to as "reflected light"). By doing so, the distance from the lidar 100 to the object is measured discretely, and point cloud data indicating the three-dimensional position of the object is generated.
  • the point cloud data is generated based on the irradiation direction corresponding to the reflected light received by the lidar 100 and the response delay time (so-called time of flight) from emitting the laser beam to receiving the reflected light. be done.
  • the lidar 100 is not limited to a scan-type lidar that scans a viewing range with a laser beam, but also a flash-type lidar that generates three-dimensional data by diffusely irradiating a laser beam over a viewing range of a two-dimensional array sensor. It may be.
  • the boundaries of the field of view of the rider 100 are indicated by lines 90 and 91.
  • the rider 100 in this embodiment is directly facing the water surface. That is, the center direction of the visual range of the rider 100 is perpendicular to the water surface.
  • the reflective members 200 are members with high reflectance such as retroreflective materials, and are installed at the same position as the rider 100 in the height direction perpendicular to the water surface, and parallel to the water surface. It is installed at a position adjacent to the rider 100 on a horizontal plane.
  • the reflecting member 200 reflects the laser light emitted by the lidar 100 reflected on the water surface and returned to the water surface again.
  • the surface of the reflective member 200 that is irradiated with laser light is directed toward the water surface.
  • the normal direction of the surface of the reflecting member 200 that is irradiated with the laser beam is perpendicular to the water surface.
  • the reflecting members 200A and 200B are installed in a direction along a direction in which the rider 100 has a wide field of view (for example, the horizontal direction).
  • the hood 300 is a member that covers the side surfaces of the rider 100 and the reflective member 200, and is provided to avoid disturbances such as sunlight.
  • the hood 300 is provided so as not to prevent laser light reflected from the water surface from entering the rider 100 and the reflecting member 200. Note that the hood 300 is not an essential component.
  • FIG. 1 shows, as an example, a flow in which a portion of the laser light is reflected from the water surface, the reflecting member 200A, and the water surface in this order, and enters the lidar 100 using broken line arrows. Similarly, there is also laser light that is reflected in this order from the water surface, the reflecting member 200B, and the water surface and enters the lidar 100.
  • the reflected light that is reflected in the order of the water surface, the reflective member 200, and the water surface and enters the lidar 100 makes two round trips between the lidar 100 and the water surface, so the lidar 100 receives the reflected light.
  • the measured distance is approximately twice the water surface distance.
  • the lidar 100 generates point cloud data by measuring the virtual virtual reflective member 200a existing in the water using the reflected light from the reflective member 200A, and generates point cloud data by measuring the virtual virtual reflective member 200a existing in the water using the reflected light from the reflective member 200B. Point cloud data obtained by measuring the reflective member 200b is generated.
  • the length of the arrow 92 corresponds to the distance from the rider 100 to the virtual reflective members 200a and 200b
  • the length of the arrow 93 corresponds to the distance from the rider 100 to the water surface
  • the length of the arrow 94 corresponds to the distance from the rider 100 to the water surface.
  • the length corresponds to the distance from the water surface to the virtual reflective member 200a and the virtual reflective member 200b.
  • arrow 93 and arrow 94 have the same length
  • arrow 92 has twice the length of arrow 93 or arrow 94. Therefore, the rider 100 can accurately calculate the water surface distance by determining the measured distance between the virtual reflective member 200a and the virtual reflective member 200b, and setting 1/2 of the measured distance as the water surface distance.
  • the lidar 100 accurately detects the water surface distance based on point cloud data based on reflected light that is reflected by the reflecting member 200 and enters the lidar 100. In this case, due to the characteristics of the rider 100, the distance to the water surface can be calculated without requiring illumination even in situations with poor visibility such as at night. Furthermore, because the water surface and the rider 100 face each other directly, the rider 100 faces downward, so it is less susceptible to the effects of raindrops and the like.
  • FIG. 2 shows an example of the structure of the rider 100 according to this embodiment.
  • the rider 100 is a scan type rider.
  • the lidar 100 mainly includes a transmitter 1, a receiver 2, a beam splitter 3, a scanner 5, a piezo sensor 6, a controller 7, and a memory 8.
  • the transmitter 1 is a light source that emits pulsed laser light toward the beam splitter 3.
  • the transmitter 1 includes, for example, an infrared laser light emitting element.
  • the transmitter 1 is driven based on a drive signal “Sg1” supplied from the controller 7.
  • the receiving unit 2 is, for example, an avalanche photodiode, generates a detection signal “Sg2” corresponding to the amount of received light, and supplies the generated detection signal Sg2 to the control unit 7.
  • the beam splitter 3 transmits the pulsed laser light emitted from the transmitter 1. Furthermore, the beam splitter 3 reflects the reflected light reflected by the scanner 5 toward the receiving section 2 .
  • the scanner 5 is, for example, an electrostatically driven mirror (MEMS mirror), and the tilt (that is, the angle of optical scanning) changes within a predetermined range based on the drive signal "Sg3" supplied from the control unit 7. Then, the scanner 5 reflects the laser light that has passed through the beam splitter 3 toward the outside of the lidar 100 and reflects the reflected light that is incident from the outside of the lidar 100 toward the beam splitter 3.
  • MEMS mirror electrostatically driven mirror
  • the scanner 5 is provided with a piezo sensor 6.
  • the piezo sensor 6 detects distortion caused by stress in the torsion bar that supports the mirror portion of the scanner 5.
  • the piezo sensor 6 supplies the generated detection signal “Sg4” to the control unit 7.
  • the detection signal Sg4 is used to detect the orientation of the scanner 5.
  • the memory 8 is composed of various types of volatile memory and nonvolatile memory such as RAM (Random Access Memory), ROM (Read Only Memory), and flash memory.
  • the memory 8 stores programs necessary for the control unit 7 to execute predetermined processing. Furthermore, the memory 8 stores various parameters referenced by the control unit 7.
  • the memory 8 also stores point cloud data generated by the control unit 7.
  • the control unit 7 includes various processors such as a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit).
  • the control unit 7 executes a predetermined process by executing a program stored in the memory 8.
  • the control unit 7 is an example of a computer that executes a program. Note that the control unit 7 is not limited to being implemented as software based on a program, and may be implemented as a combination of hardware, firmware, and software. Further, the control unit 7 may be a user-programmable integrated circuit such as an FPGA (Field-Programmable Gate Array) or a microcontroller; Specific Integrated Circuit) etc. It may be.
  • FPGA Field-Programmable Gate Array
  • control unit 7 includes a transmission drive block 70, a scanner drive block 71, a point cloud data generation block 72, and a point cloud data processing block 73.
  • the transmission drive block 70 outputs a drive signal Sg1 that drives the transmission section 1.
  • the drive signal Sg1 includes information for controlling the light emission time of the laser light emitting element included in the transmitter 1 and the light emission intensity of the laser light emitting element.
  • the transmission drive block 70 controls the light emission intensity of the laser light emitting element included in the transmitter 1 based on the drive signal Sg1.
  • the scanner drive block 71 outputs a drive signal Sg3 for driving the scanner 5.
  • This drive signal Sg3 includes a horizontal drive signal corresponding to the resonance frequency of the scanner 5 and a vertical drive signal for vertical scanning. Further, the scanner drive block 71 detects the scanning angle of the scanner 5 (that is, the direction in which the laser beam is emitted) by monitoring the detection signal Sg4 output from the piezo sensor 6.
  • the point cloud data generation block 72 Based on the detection signal Sg2 supplied from the receiving unit 2, the point cloud data generation block 72 indicates, for each emitted pulsed light, the distance and direction to the object irradiated with the laser light, using the lidar 100 as a reference point. generate point cloud data. In this case, the point cloud data generation block 72 calculates the time from when the laser beam is emitted until the receiving unit 2 detects the reflected light as the time of flight of the light. Then, the point cloud data generation block 72 generates point cloud data indicating a set of the distance according to the calculated flight time and the emission direction of the laser beam corresponding to the reflected light received by the receiving unit 2. The point cloud data is supplied to a point cloud data processing block 73.
  • the point cloud data obtained by one scan within the field of view will be referred to as one frame of point cloud data.
  • the point cloud data can be regarded as an image whose pixels are measured points (points to be measured) and whose pixel values are the measured distances of each point to be measured.
  • each pixel has a different emitting direction of laser light at an elevation/depression angle when arranged in the vertical direction, and a different emitting direction of laser light at a horizontal angle when arranged in a horizontal direction. Then, for each pixel, coordinate values in the three-dimensional coordinate system are determined based on the corresponding set of emission direction and measurement distance.
  • the point cloud data generation block 72 may perform a process of removing noise data generated by erroneously detecting an object in the point cloud data, and generate point cloud data from which the noise data has been removed.
  • the transmitter 1, the receiver 2, the beam splitter 3, the scanner 5, the transmission drive block 70, the scanner drive block 71, and the point cloud data generation block 72 are an example of an "optical measurement device.”
  • the point cloud data processing block 73 calculates the water surface distance based on the point cloud data supplied from the point cloud data generation block 72.
  • the point cloud data processing block 73 converts point cloud data (also referred to as "reflecting member measurement data") generated by receiving reflected light from the reflecting members 200 (200A, 200B) into point cloud data. It is extracted from the point cloud data supplied from the data generation block 72.
  • the reflective member measurement data is data representing the virtual reflective members 200a and 200b in FIG. 1 as virtual measured points.
  • the point cloud data processing block 73 calculates 1/2 of the measured distance indicated by the reflective member measurement data as the water surface distance.
  • the point cloud data processing block 73 stores the calculated water surface distance in the memory 8.
  • the point cloud data processing block 73 is an example of an "acquisition means”, “extraction means”, “calculation means”, “estimation means”, “determination means”, and a computer that executes a program.
  • the point cloud data processing block 73 may extract reflective member measurement data based on the number, size, and installation location of the reflective members 200 (including the relative positional relationship between the reflective members 200). For example, the point cloud data processing block 73 performs clustering of each measured point based on the measured distance of each measured point represented by the point cloud data. In this case, the point cloud data processing block 73 may determine clusters of measured points based on arbitrary clustering processing. Then, the point cloud data processing block 73 extracts data representing clusters having a predetermined number or more of elements from among the determined clusters as reflective member measurement data.
  • the above-mentioned predetermined number is determined in advance so as to correspond to the size of the reflecting member 200. Further, the point cloud data processing block 73 may further extract cluster data that satisfies conditions regarding the number and relative positions of the reflective members 200 as the reflective member measurement data. In this case, the point cloud data processing block 73 uses, for example, a set of clusters of the same number as the number of reflective members 200, which satisfy the relative positional relationship of the reflective members 200 measured in advance, as the reflective member measurement data. Extract. Thereby, the point cloud data processing block 73 can prevent data representing objects floating on the water surface from being erroneously extracted as reflective member measurement data.
  • FIG. 3 is an example of a flowchart executed by the rider 100.
  • the point cloud data generation block 72 generates point cloud data based on the detection signal Sg2 (step S01).
  • the point cloud data generation block 72 generates the points of the frame corresponding to the current processing time (also referred to as the "current frame") based on the detection signal Sg2 generated by one scan in the scanning target range of the lidar 100. Generate group data.
  • the point cloud data generation block 72 executes a noise removal process, which is a process of removing noise data from the point group data generated in step S01 (step S02).
  • the point cloud data generation block 72 may perform arbitrary noise removal processing.
  • the point cloud data generation block 72 regards data in which the intensity of reflected light received by the receiving unit 2 is less than a predetermined threshold as noise data and removes it from the point cloud data.
  • the point cloud data processing block 73 extracts reflective member measurement data generated based on the laser beam reflected by the reflective member 200 from the point cloud data after the noise removal process (step S03). Then, the point cloud data processing block 73 calculates the water surface distance based on the measured distance represented by the extracted reflective member measurement data (step S04). In this case, the point cloud data processing block 73 calculates, for example, 1/2 of the average value of the measured distances for each measured point represented by the reflective member measurement data as the water surface distance. Note that, instead of using the average value of the measured distance for each measured point represented by the reflective member measurement data, the point cloud data processing block 73 uses a representative value other than the average value of the measured distance for each measured point represented by the reflective member measurement data. A value (eg median value) may also be used.
  • the point cloud data processing block 73 may estimate the situation of a river or the like to be measured based on the point cloud data.
  • the point cloud data processing block 73 estimates the degree of ripples on the water surface as the measurement target situation.
  • the point cloud data processing block 73 calculates the water surface distance in time series based on the point cloud data obtained for each frame period based on the point cloud data obtained in a predetermined period, and calculates the water surface distance within the predetermined period. Calculate the maximum value and minimum value.
  • the point cloud data processing block 73 estimates the degree of ripples based on the maximum and minimum values of the water surface distance.
  • the point cloud data processing block 73 calculates, for example, the difference between the maximum value and the minimum value of the water surface distance as an index representing the degree of ripples. According to this example, the point cloud data processing block 73 can accurately estimate the degree of undulation in a river or the like in addition to the water surface distance.
  • the point cloud data processing block 73 performs processing when the shape of the reflective member 200 indicated by the point cloud data is disordered (for example, when the degree of similarity with the shape of the actual reflective member 200 is less than a predetermined degree), or If the water surface distance also changes in a short time (for example, if the variance value of the water surface distance in a predetermined period is greater than or equal to a predetermined value), it may be estimated that the water surface is undulating.
  • the point cloud data processing block 73 calculates the flow velocity based on objects floating on the water surface (water surface floating objects) as the measurement target situation.
  • the point cloud data processing block 73 detects the detected position of the floating object on the water surface in a frame based on the point cloud data obtained every frame period. Calculate each cycle. Then, the point cloud data processing block 73 calculates the flow velocity based on the transition of the detected position of the water surface floating object for each frame period.
  • the point cloud data processing block 73 performs clustering on the point cloud data obtained for each frame period, and performs clustering on the point cloud data obtained for each frame period, and performs clustering on the point cloud data obtained at each frame period. Detect clusters of points as clusters representing objects floating on the water surface. According to this example, the point cloud data processing block 73 can accurately estimate the flow velocity of a river or the like in addition to the water surface distance.
  • the rider 100 may be installed at an angle with respect to the water surface.
  • FIG. 4 shows a first configuration example of a ranging system according to this modification.
  • the direction of the lidar 100 with respect to the water surface is set at an angle " ⁇ 0" (0 ⁇ 90), and the reflective member 200C is placed at a position where the laser beam emitted from the lidar 100 and reflected on the water surface is irradiated. It is provided.
  • the ground surface, the water surface at high water level, and the water surface at low water level are shown, and the distance between the ground surface and the reflective member 200C is "h1", and the distance from the ground surface to the water surface at low water level is " W", and the distance of the rider 100 from the ground surface is "h0".
  • the measured distance to the virtual reflecting member 200c is set to "d". Further, the virtual reflective member 200c represents a false point of the reflective member 200C measured at a high water level, and the virtual reflective member 201c represents a false point of the reflective member 200C measured at a low water level.
  • the rider 100 and the reflecting member 200C are provided at separate positions, and the rider 100 is installed at an angle with respect to the water surface.
  • the reflecting member 200C may be provided with a hood 300 shown in FIG. 1 for condensing light reflected from the water surface onto the reflecting member 200C.
  • the rider 100 may be provided with the hood 300 shown in FIG. 1 for condensing light reflected from the water surface onto the rider 100.
  • the laser beam emitted by the lidar 100 is reflected in the order of the water surface, the reflecting member 200C, and the water surface, and the reflected light is received by the lidar 100.
  • the lidar 100 generates point cloud data representing the virtual virtual reflective member 200c existing in the water based on the light reception signal generated by receiving the above-mentioned reflected light.
  • the above-mentioned reflected light makes two round trips between the rider 100 and the water surface, and since the rider 100 is tilted at a predetermined angle with respect to the water surface, the reflected light is based on the measured distance of the virtual reflecting member 200c. It is possible to calculate the water surface distance.
  • FIG. 5 shows a second configuration example of the ranging system according to this modification.
  • the reflecting member 200C is inclined by an angle " ⁇ 1" with respect to the ground surface and the water surface.
  • "h1" represents the distance from the lowest position of the reflecting member 200C to the ground surface.
  • the reflective member 200C As shown in FIG. 5, if the reflective member 200C is not horizontal, the measured distance (height) of the reflective member 200C will change depending on the position of the light, so the water level cannot be measured accurately.
  • the reflecting member 200C whose installation position and angle are known, is provided with features corresponding to the position (height) within the reflecting member 200C. Then, the lidar 100 recognizes the position (height) of the measured portion by comparing the pattern of the reflective member 200C indicated by the obtained point cloud data with the above characteristics.
  • FIG. 6 is a diagram showing the correspondence between the reflective member 200C provided with features depending on the position and the point group data of the reflective member 200C detected by the lidar 100.
  • a pattern of highly reflective rectangular areas is provided on the surface of the reflective member 200C, and the rectangular area gradually becomes smaller from the lower end to the upper end of the reflective member 200C.
  • the lidar 100 recognizes from the pattern of the measured points of the point cloud data that the area 250 of the reflective member 200C indicated by the broken line has been detected, and extends from the lower end of the reflective member 200C to the detection area 250.
  • the distance "y1" is calculated.
  • the rider 100 stores in advance information indicating the correspondence between the size of the highly reflective rectangular area provided on the surface of the reflective member 200C and the position within the reflective member 200C, thereby determining the distance y1. calculate.
  • the rider 100 calculates the water surface distance by using the distance y1.
  • the rider 100 can suitably calculate the water surface distance.
  • the rider 100 may determine the water surface distance measurement method based on the undulating state of the water surface.
  • the rider 100 when ripples of a predetermined degree or more occur on the water surface, the rider 100 extracts point cloud data (also referred to as "water surface measurement data") obtained by measuring the water surface, and uses the water surface measurement data. Calculate the water surface distance based on Generally, when the water surface is rippled, the amount of return light that returns directly from the water surface to the lidar 100 increases relatively due to diffuse reflection, making it possible to acquire water surface measurement data. Therefore, when ripples of a predetermined degree or more occur on the water surface, the rider 100 performs clustering of point cloud data, for example, and extracts data of clusters having a length of a predetermined length or more as water surface measurement data.
  • point cloud data also referred to as "water surface measurement data”
  • the rider 100 calculates the average of the measured distances represented by the water surface measurement data as the water surface distance.
  • the rider 100 extracts the reflective member measurement data and calculates the water surface distance based on the reflective member measurement data. Note that, in reality, both direct reflection from the water surface and reflection from the reflective member are measured, both with and without ripples. Therefore, instead of the above example, the rider 100 may calculate the water surface distance based on the water surface measurement data regardless of the rippling condition of the water surface, and may calculate the water surface distance based on both the water surface measurement data and the reflective member measurement data. It may be calculated.
  • the rider 100 calculates the final water surface distance by averaging or weighted averaging the water surface distance calculated based on the water surface measurement data and the water surface distance calculated based on the reflective member measurement data.
  • the higher the degree of foaming the greater the weight given to the water surface distance calculated based on the water surface measurement data.
  • the point cloud data processing block 73 may determine, for example, based on the point cloud data, whether or not ripples on the water surface are occurring to a predetermined degree or more. In this case, the point cloud data processing block 73 may perform the above-mentioned determination by, for example, learning to determine the presence or absence of ripples on the water surface to a predetermined degree or more from the reflection pattern of the point cloud data.
  • the rider 100 can accurately measure the distance on the water surface using a method according to the ripples on the water surface.
  • the point cloud data processing block 73 may determine the reliability of the water surface distance calculated from the difference between the two measurement values.
  • the difference between the water surface distance calculated based on the water surface measurement data, taking into account the positional relationship between the lidar 100 and the reflecting member 200, and the water surface distance calculated based on the reflective object measurement data is evaluated. This determines the reliability of the calculated water surface distance. For example, when the lidar 100 and the reflective member 200 are arranged as shown in FIG. ) is known, the distance calculated based on the water surface measurement data, taking into account the positional relationship between the lidar 100 and the reflective member 200, and the water surface distance calculated based on the reflective object measurement data are: Theoretically, the distances are the same.
  • the point cloud data processing block 73 may adopt only the water surface distance determined to have high reliability as the measurement result, or may avoid adopting the water surface distance determined to have low reliability as the measurement result. You can also do this.
  • the point cloud data processing block 73 can calculate the water surface distance with higher accuracy.
  • FIG. 1 the arrangement of the rider 100 and the reflecting member 200 is not limited to this.
  • the arrangement shown in FIGS. 4 and 5 may be used, or various other arrangements may be used.
  • the reliability was determined in two stages, "high” and “low,” but the method for determining the reliability is not limited to this. There may be three levels of “high,” “medium,” and “low,” or there may be four or more levels.
  • the configuration of rider 100 is not limited to the configuration shown in FIG. 2.
  • a device different from the lidar 100 may have functions corresponding to the point cloud data processing block 73 and the point cloud data processing block 73 of the control unit 7.
  • FIG. 7 is a configuration diagram of a rider system according to a modification.
  • the lidar system includes a lidar 100X and an information processing device 400.
  • the lidar 100X supplies the point cloud data generated by the point cloud data generation block 72 to the information processing device 400.
  • the information processing device 400 includes a control unit 7A and a memory 8.
  • the memory 8 stores information necessary for the control unit 7A to execute processing.
  • the control unit 7A includes a point cloud data acquisition block 72A and a point cloud data processing block 73.
  • the point cloud data acquisition block 72A receives the point cloud data generated by the point cloud data generation block 72 of the lidar 100X, and supplies the received point cloud data to the point cloud data processing block 73.
  • the point cloud data processing block 73 performs the same processing as the point cloud data processing block 73 of the above-described embodiment on the point cloud data supplied from the point cloud data acquisition block 72A.
  • the point cloud data acquisition block 72 acquires point cloud data generated by an optical measurement device that performs distance measurement by receiving reflected light of emitted light directed toward the water surface. . Then, the point cloud data processing block 73 extracts reflective member measurement data obtained by measuring the reflective member 200 provided at a position where the light reflected on the water surface is irradiated from the point cloud data. Then, the point cloud data processing block 73 calculates the water surface distance based on the reflective member measurement data. This aspect allows the point cloud data processing block 73 to accurately measure the water surface distance.
  • Non-transitory computer-readable media include various types of tangible storage media.
  • Examples of non-transitory computer-readable media include magnetic storage media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical storage media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, Includes CD-R/W, semiconductor memory (for example, mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, and RAM (Random Access Memory).

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Electromagnetism (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Thermal Sciences (AREA)
  • Fluid Mechanics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Hydrology & Water Resources (AREA)
  • Optical Radar Systems And Details Thereof (AREA)

Abstract

In the present invention, a point group data processing block 73 acquires point group data generated by an optical measuring device that is directed at a liquid surface and measures distance by receiving the reflected light of the emitted light. The point group data processing block 73 extracts, from the point group data, reflective member measurement data obtained by measuring a reflective member 200 provided at a position where the light reflected by the liquid surface is projected. The point group data processing block 73 then calculates the distance to the liquid surface on the basis of the reflective member measurement data.

Description

情報処理装置、測距システム、制御方法、プログラム及び記憶媒体Information processing device, ranging system, control method, program and storage medium
　本開示は、計測したデータを処理する技術に関する。 The present disclosure relates to technology for processing measured data.
　従来から、計測対象物に光を照射して該計測対象物からの反射光を検出し、該計測対象物に光を照射するタイミングと、該計測対象物からの反射光を検出するタイミングとの時間差により計測対象物までの距離を算出する測距装置が知られている。例えば、特許文献１には、繰り返し出射される光パルスの出射方向（走査方向）を適切に制御することにより周辺空間を走査し、その戻り光を観測することにより、周辺に存在する物体に関する情報である距離、反射率などの情報を表す点群データを生成するライダが開示されている。 Conventionally, a measurement target object is irradiated with light and reflected light from the measurement object is detected, and the timing of irradiating the measurement object with light and the timing of detecting the reflected light from the measurement object are known. 2. Description of the Related Art Distance measuring devices that calculate the distance to a measurement target based on a time difference are known. For example, Patent Document 1 discloses that by appropriately controlling the emission direction (scanning direction) of repeatedly emitted light pulses, the surrounding space is scanned, and by observing the returned light, information about objects existing in the surroundings is obtained. A lidar is disclosed that generates point cloud data representing information such as distance and reflectance.
特開２０１８－００９８３１号公報JP2018-009831A
　一般に、ライダなどのレーザ光を利用した測距装置により液体面までの距離を計測する場合、液体面を直接計測した計測データを得ることが難しく、正確な計測ができないといった問題があった。 Generally, when measuring the distance to a liquid surface using a distance measuring device such as a lidar that uses laser light, there is a problem in that it is difficult to obtain measurement data that directly measures the liquid surface, and accurate measurements cannot be made.
　本発明の解決しようとする課題としては、上記のものが一例として挙げられる。本開示は、液体面までの距離を正確に計測することが可能な情報処理装置、測距システム、制御方法、プログラム及びプログラムを記憶した記憶媒体を提供することを主な目的とする。 Examples of the problems to be solved by the present invention include those mentioned above. The main object of the present disclosure is to provide an information processing device, a distance measuring system, a control method, a program, and a storage medium storing the program that can accurately measure the distance to a liquid surface.
　請求項に記載の発明は、
　液体面に向けられ、出射した光の反射光を受光することで測距を行う光計測装置が生成した計測データを取得する取得手段と、
　前記計測データから、前記液体面で反射された前記光が照射される位置に設けられた反射部材を計測した反射部材計測データを抽出する抽出手段と、
　前記反射部材計測データに基づき、前記液体面までの距離を算出する算出手段と、
を有する情報処理装置である。 The claimed invention is:
an acquisition means for acquiring measurement data generated by an optical measurement device that performs distance measurement by receiving reflected light of the emitted light directed toward the liquid surface;
Extracting means for extracting reflective member measurement data obtained by measuring a reflective member provided at a position where the light reflected on the liquid surface is irradiated from the measurement data;
Calculation means for calculating a distance to the liquid surface based on the reflective member measurement data;
This is an information processing device having:
　また、請求項に記載の発明は、
　コンピュータが、
　液体面に向けられ、出射した光の反射光を受光することで測距を行う光計測装置が生成した計測データを取得し、
　前記計測データから、前記液体面で反射された前記光が照射される位置に設けられた反射部材を計測した反射部材計測データを抽出し、
　前記反射部材計測データに基づき、前記液体面までの距離を算出する、
制御方法である。 In addition, the claimed invention is
The computer is
Obtain measurement data generated by an optical measurement device that measures distance by receiving the reflected light emitted from the liquid surface.
extracting reflective member measurement data obtained by measuring a reflective member provided at a position where the light reflected on the liquid surface is irradiated from the measurement data;
calculating a distance to the liquid surface based on the reflective member measurement data;
This is a control method.
　また、請求項に記載の発明は、
　液体面に向けられ、出射した光の反射光を受光することで測距を行う光計測装置が生成した計測データを取得し、
　前記計測データから、前記液体面で反射された前記光が照射される位置に設けられた反射部材を計測した反射部材計測データを抽出し、
　前記反射部材計測データに基づき、前記液体面までの距離を算出する処理をコンピュータに実行させるプログラムである。 In addition, the claimed invention is
Obtain measurement data generated by an optical measurement device that measures distance by receiving the reflected light emitted from the liquid surface.
extracting reflective member measurement data obtained by measuring a reflective member provided at a position where the light reflected on the liquid surface is irradiated from the measurement data;
This is a program that causes a computer to execute a process of calculating a distance to the liquid surface based on the reflective member measurement data.
実施例に係る測距システムの概略構成を示す。1 shows a schematic configuration of a ranging system according to an embodiment. 実施例に係るライダの概略構成を示す。1 shows a schematic configuration of a rider according to an embodiment. 実施例に係るフローチャートの一例である。　　　ライダが出射したレーザ光が照射される道路の断面図を示す。It is an example of the flowchart based on an Example. A cross-sectional view of a road irradiated with laser light emitted by a lidar is shown. 変形例に係る測距システムの第１構成例である。This is a first configuration example of a ranging system according to a modification. 変形例に係る測距システムの第２構成例である。This is a second configuration example of a ranging system according to a modification. 変形例に係る反射物の構造と得られる点群データの対応例を示す。An example of correspondence between the structure of a reflective object and obtained point cloud data according to a modified example is shown. 変形例に係るライダシステムの構成図である。FIG. 3 is a configuration diagram of a rider system according to a modified example.
　本発明の好適な実施形態では、情報処理装置は、液体面に向けられ、出射した光の反射光を受光することで測距を行う光計測装置が生成した計測データを取得する取得手段と、前記計測データから、前記液体面で反射された前記光が照射される位置に設けられた反射部材を計測した反射部材計測データを抽出する抽出手段と、前記反射部材計測データに基づき、前記液体面までの距離を算出する算出手段と、を有する。この態様では、情報処理装置は、液体面で反射された光が照射された反射部材の計測データに基づき、液体面までの距離を正確に計測することができる。 In a preferred embodiment of the present invention, the information processing device includes an acquisition unit that acquires measurement data generated by an optical measurement device that is directed toward a liquid surface and performs distance measurement by receiving reflected light of emitted light; extracting means for extracting, from the measurement data, reflecting member measurement data obtained by measuring a reflecting member provided at a position where the light reflected by the liquid surface is irradiated; calculation means for calculating the distance to. In this aspect, the information processing device can accurately measure the distance to the liquid surface based on the measurement data of the reflecting member irradiated with the light reflected by the liquid surface.
　上記情報処理装置の一態様では、前記光計測装置は液体面と正対し、前記反射部材は、前記光計測装置に近接して設けられる。この態様により、情報処理装置は、液体面で反射された光が照射された反射部材に関する計測データを好適に取得することができる。 In one aspect of the information processing device, the optical measuring device directly faces the liquid surface, and the reflecting member is provided close to the optical measuring device. According to this aspect, the information processing device can suitably acquire measurement data regarding the reflecting member irradiated with the light reflected on the liquid surface.
　上記情報処理装置の他の一態様では、前記反射部材は、前記液体面からの距離が前記光計測装置と同一となる位置に設けられ、前記算出手段は、前記反射部材計測データが示す計測距離の１／２を前記液体面までの距離として算出する。この態様により、情報処理装置は、反射部材の計測データに基づき、液体面までの距離を正確に計測することができる。 In another aspect of the information processing device, the reflecting member is provided at a position where the distance from the liquid surface is the same as that of the optical measuring device, and the calculating means is configured to calculate a measured distance indicated by the reflecting member measurement data. 1/2 is calculated as the distance to the liquid surface. With this aspect, the information processing device can accurately measure the distance to the liquid surface based on the measurement data of the reflecting member.
　上記情報処理装置の他の一態様では、情報処理装置は、前記計測データに基づき、前記液体面の状況を推定する推定手段をさらに有する。好適な例では、前記推定手段は、前記算出手段が時系列により算出した前記液体面までの距離に基づき、前記液体面の波立ちの度合いを推定する。この態様によれば、情報処理装置は、液体面の波立ちの度合いを好適に推定することができる。 In another aspect of the information processing device, the information processing device further includes estimating means for estimating the state of the liquid level based on the measurement data. In a preferred example, the estimating means estimates the degree of ripples on the liquid surface based on the distance to the liquid surface calculated in time series by the calculating means. According to this aspect, the information processing device can suitably estimate the degree of ripples on the liquid surface.
　上記情報処理装置の他の一態様では、前記反射部材には位置に応じたパターンが形成され、前記算出手段は、前記反射部材計測データに基づき、前記反射部材において計測された位置を認識し、当該位置に基づき前記液体面までの距離を算出する。この態様によれば、情報処理装置は、液体面までの距離を正確に計測することができる。 In another aspect of the information processing device, a pattern corresponding to a position is formed on the reflective member, and the calculation means recognizes the measured position on the reflective member based on the reflective member measurement data, A distance to the liquid surface is calculated based on the position. According to this aspect, the information processing device can accurately measure the distance to the liquid surface.
　本発明の他の好適な実施形態では、測距システムは、液体面に向けられ、出射した光の反射光を受光することで測距を行う光計測装置と、上記いずれか記載の情報処理装置と、を有する。この態様により、測距システムは、液体面までの距離を正確に計測することができる。 In another preferred embodiment of the present invention, a distance measurement system includes an optical measurement device that is directed toward a liquid surface and measures distance by receiving reflected light of emitted light, and an information processing device according to any of the above. and has. This aspect allows the distance measuring system to accurately measure the distance to the liquid surface.
　本発明の他の好適な実施形態では、コンピュータが、液体面に向けられ、出射した光の反射光を受光することで測距を行う光計測装置が生成した計測データを取得し、前記計測データから、前記液体面で反射された前記光が照射される位置に設けられた反射部材を計測した反射部材計測データを抽出し、前記反射部材計測データに基づき、前記液体面までの距離を算出する、制御方法である。コンピュータは、この制御方法を実行することで、液体面までの距離を正確に計測することができる。 In another preferred embodiment of the present invention, the computer acquires measurement data generated by an optical measurement device that measures distance by receiving reflected light of emitted light directed toward a liquid surface; , extracting reflective member measurement data obtained by measuring a reflective member provided at a position where the light reflected by the liquid surface is irradiated, and calculating a distance to the liquid surface based on the reflective member measurement data. , is a control method. By executing this control method, the computer can accurately measure the distance to the liquid surface.
　本発明の他の好適な実施形態では、液体面に向けられ、出射した光の反射光を受光することで測距を行う光計測装置が生成した計測データを取得し、前記計測データから、前記液体面で反射された前記光が照射される位置に設けられた反射部材を計測した反射部材計測データを抽出し、前記反射部材計測データに基づき、前記液体面までの距離を算出する処理をコンピュータに実行させるプログラムである。コンピュータは、このプログラムを実行することで、液体面までの距離を正確に計測することができる。好適には、上記プログラムは、記憶媒体に記憶される。 In another preferred embodiment of the present invention, measurement data generated by an optical measurement device that performs distance measurement by receiving reflected light emitted from a liquid surface is obtained, and from the measurement data, the A computer performs a process of extracting reflective member measurement data obtained by measuring a reflective member provided at a position where the light reflected on the liquid surface is irradiated, and calculating a distance to the liquid surface based on the reflective member measurement data. This is a program that is executed by By executing this program, the computer can accurately measure the distance to the liquid surface. Preferably, the program is stored on a storage medium.
　以下、図面を参照して本発明の好適な実施例について説明する。 Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings.
　（１）システム構成
　図１は、本実施例に係る測距システムの概略構成を示す。本実施例に係る測距システムは、河川や湖などの任意の液体の面（液体面）までの距離を測定するシステムであって、ライダ１００と、反射部材２００（２００Ａ、２００Ｂ）と、フード３００と、を有する。以後では、一例として、液体面は水面とする。測距システムは、ライダ１００のレーザ光が水面にて反射することを利用してライダ１００の近傍に設けられた反射部材２００を計測することで、ライダ１００から水面までの距離（単に「水面距離」とも呼ぶ。）を算出する。 (1) System configuration FIG. 1 shows a schematic configuration of a ranging system according to this embodiment. The distance measuring system according to this embodiment is a system that measures the distance to an arbitrary liquid surface (liquid surface) such as a river or lake, and includes a lidar 100, a reflecting member 200 (200A, 200B), and a hood. 300. Hereinafter, as an example, the liquid surface will be the water surface. The distance measuring system measures the distance from the lidar 100 to the water surface (simply called "water surface distance") by measuring the reflective member 200 provided near the lidar 100 by utilizing the reflection of the laser beam from the lidar 100 on the water surface. ) is calculated.
　ライダ１００は、水平方向および垂直方向の所定の角度範囲（視野範囲）に対してレーザ光を照射し、当該レーザ光が物体に反射されて戻った光（「反射光」とも呼ぶ。）を受光することで、ライダ１００から物体までの距離を離散的に測定し、当該物体の３次元位置を示す点群データを生成する。点群データは、ライダ１００が受光した反射光に対応する照射方向と、レーザ光を出射してから反射光を受光するまでの応答遅延時間（所謂、飛行時間：Ｔｉｍｅ　ｏｆ　Ｆｌｉｇｈｔ）とに基づき生成される。ライダ１００は、視野範囲に対してレーザ光をスキャンするスキャン型のライダに限らず、２次元アレイ状のセンサの視野範囲にレーザ光を拡散照射することによって３次元データを生成するフラッシュ型のライダであってもよい。図１では、ライダ１００の視野範囲の境界が線９０及び線９１により示されている。そして、本実施例におけるライダ１００は、水面と正対している。即ち、ライダ１００の視野範囲の中心方向は、水面と垂直となっている。 The lidar 100 irradiates a laser beam over a predetermined angular range (field of view) in the horizontal and vertical directions, and receives light that is reflected back from an object (also referred to as "reflected light"). By doing so, the distance from the lidar 100 to the object is measured discretely, and point cloud data indicating the three-dimensional position of the object is generated. The point cloud data is generated based on the irradiation direction corresponding to the reflected light received by the lidar 100 and the response delay time (so-called time of flight) from emitting the laser beam to receiving the reflected light. be done. The lidar 100 is not limited to a scan-type lidar that scans a viewing range with a laser beam, but also a flash-type lidar that generates three-dimensional data by diffusely irradiating a laser beam over a viewing range of a two-dimensional array sensor. It may be. In FIG. 1, the boundaries of the field of view of the rider 100 are indicated by lines 90 and 91. The rider 100 in this embodiment is directly facing the water surface. That is, the center direction of the visual range of the rider 100 is perpendicular to the water surface.
　反射部材２００（２００Ａ、２００Ｂ）は、再帰性反射材などの高い反射率を有する部材であり、水面と垂直な高さ方向において、ライダ１００と同じ位置に設置されており、かつ、水面と平行な水平面上において、ライダ１００と隣接する位置に設置されている。反射部材２００は、ライダ１００が出射したレーザ光が水面で反射して戻った光を再び水面に反射する。反射部材２００がレーザ光により照射される面は、水面に向けられている。言い換えると、反射部材２００がレーザ光により照射される面の法線方向は、水面と垂直となっている。なお、一つの例では、反射部材２００Ａ、２００Ｂは、ライダ１００の視野が広い方向（例えば水平方向）に沿った方向に設置される。 The reflective members 200 (200A, 200B) are members with high reflectance such as retroreflective materials, and are installed at the same position as the rider 100 in the height direction perpendicular to the water surface, and parallel to the water surface. It is installed at a position adjacent to the rider 100 on a horizontal plane. The reflecting member 200 reflects the laser light emitted by the lidar 100 reflected on the water surface and returned to the water surface again. The surface of the reflective member 200 that is irradiated with laser light is directed toward the water surface. In other words, the normal direction of the surface of the reflecting member 200 that is irradiated with the laser beam is perpendicular to the water surface. In one example, the reflecting members 200A and 200B are installed in a direction along a direction in which the rider 100 has a wide field of view (for example, the horizontal direction).
　フード３００は、ライダ１００及び反射部材２００の側面を覆う部材であり、太陽光などの外乱を避けるために設けられる。フード３００は、水面で反射したレーザ光がライダ１００及び反射部材２００へ入射するのを阻害しないように設けられる。なお、フード３００は、必須の構成ではない。 The hood 300 is a member that covers the side surfaces of the rider 100 and the reflective member 200, and is provided to avoid disturbances such as sunlight. The hood 300 is provided so as not to prevent laser light reflected from the water surface from entering the rider 100 and the reflecting member 200. Note that the hood 300 is not an essential component.
　図１において、ライダ１００が出射したレーザ光が水面で反射し、その反射光が反射部材２００で反射されて再び水面に到達し、当該水面で反射された光がライダ１００に入射する。図１は、一例として、レーザ光の一部が水面、反射部材２００Ａ、水面の順に反射してライダ１００に入射する流れを破線矢印により示している。同様に、水面、反射部材２００Ｂ、水面の順に反射してライダ１００に入射するレーザ光も存在する。 In FIG. 1, the laser light emitted by the lidar 100 is reflected on the water surface, the reflected light is reflected by the reflecting member 200 and reaches the water surface again, and the light reflected on the water surface enters the lidar 100. FIG. 1 shows, as an example, a flow in which a portion of the laser light is reflected from the water surface, the reflecting member 200A, and the water surface in this order, and enters the lidar 100 using broken line arrows. Similarly, there is also laser light that is reflected in this order from the water surface, the reflecting member 200B, and the water surface and enters the lidar 100.
　そして、水面、反射部材２００、水面の順に反射してライダ１００に入射した反射光は、ライダ１００と水面との間を２往復していることから、ライダ１００が当該反射光を受光することで計測した距離は、水面距離の約２倍となる。この場合、ライダ１００は、反射部材２００Ａの反射光により、水中に存在する仮想の仮想反射部材２００ａを計測した点群データを生成し、反射部材２００Ｂの反射光により、水中に存在する仮想の仮想反射部材２００ｂを計測した点群データを生成する。ここで、矢印９２の長さは、ライダ１００から仮想反射部材２００ａ及び仮想反射部材２００ｂまでの距離に相当し、矢印９３の長さは、ライダ１００から水面までの距離に相当し、矢印９４の長さは、水面から仮想反射部材２００ａ及び仮想反射部材２００ｂまでの距離に相当する。そして、矢印９３と矢印９４は同一長さであり、矢印９２は矢印９３又は矢印９４の２倍の長さとなる。従って、ライダ１００は、仮想反射部材２００ａ及び仮想反射部材２００ｂの計測距離を求め、その１／２を水面距離として定めることで、水面距離を的確に算出することができる。 The reflected light that is reflected in the order of the water surface, the reflective member 200, and the water surface and enters the lidar 100 makes two round trips between the lidar 100 and the water surface, so the lidar 100 receives the reflected light. The measured distance is approximately twice the water surface distance. In this case, the lidar 100 generates point cloud data by measuring the virtual virtual reflective member 200a existing in the water using the reflected light from the reflective member 200A, and generates point cloud data by measuring the virtual virtual reflective member 200a existing in the water using the reflected light from the reflective member 200B. Point cloud data obtained by measuring the reflective member 200b is generated. Here, the length of the arrow 92 corresponds to the distance from the rider 100 to the virtual reflective members 200a and 200b, the length of the arrow 93 corresponds to the distance from the rider 100 to the water surface, and the length of the arrow 94 corresponds to the distance from the rider 100 to the water surface. The length corresponds to the distance from the water surface to the virtual reflective member 200a and the virtual reflective member 200b. Further, arrow 93 and arrow 94 have the same length, and arrow 92 has twice the length of arrow 93 or arrow 94. Therefore, the rider 100 can accurately calculate the water surface distance by determining the measured distance between the virtual reflective member 200a and the virtual reflective member 200b, and setting 1/2 of the measured distance as the water surface distance.
　ここで、図１に示される構成による効果について補足説明する。水面で反射後に直接ライダ１００に入射した光を受光することで得られたデータに基づき水面距離を直接算出する構成も考えられる。しかしながら、一般的に、ライダ１００が検出可能な水面上の点はまばらであり、そのような点を検出して水面距離を算出することは困難である。以上を勘案し、本実施例に係るライダ１００は、反射部材２００で反射されてライダ１００に入射する反射光に基づく点群データに基づき、水面距離を的確に検出する。この場合、ライダ１００の特性上、夜間など視認性の悪い状況でも照明を要することなく水面距離を算出することができる。また、水面とライダ１００とを正対させる関係上、ライダ１００は下向きとなるため、雨滴などの影響を受けにくい。 Here, the effects of the configuration shown in FIG. 1 will be supplementarily explained. It is also possible to consider a configuration in which the water surface distance is directly calculated based on data obtained by receiving light that is directly incident on the lidar 100 after being reflected on the water surface. However, generally, the points on the water surface that can be detected by the rider 100 are sparse, and it is difficult to detect such points and calculate the water surface distance. In consideration of the above, the lidar 100 according to the present embodiment accurately detects the water surface distance based on point cloud data based on reflected light that is reflected by the reflecting member 200 and enters the lidar 100. In this case, due to the characteristics of the rider 100, the distance to the water surface can be calculated without requiring illumination even in situations with poor visibility such as at night. Furthermore, because the water surface and the rider 100 face each other directly, the rider 100 faces downward, so it is less susceptible to the effects of raindrops and the like.
（２）ライダの構成
　図２は、本実施例に係るライダ１００の構成の一例を示す。ここでは、一例として、ライダ１００は、スキャン型のライダであるものとする。 (2) Structure of Rider FIG. 2 shows an example of the structure of the rider 100 according to this embodiment. Here, as an example, it is assumed that the rider 100 is a scan type rider.
　ライダ１００は、主に、送信部１と、受信部２と、ビームスプリッタ３と、スキャナ５と、ピエゾセンサ６と、制御部７と、メモリ８と、を有する。 The lidar 100 mainly includes a transmitter 1, a receiver 2, a beam splitter 3, a scanner 5, a piezo sensor 6, a controller 7, and a memory 8.
　送信部１は、パルス状のレーザ光をビームスプリッタ３に向けて出射する光源である。送信部１は、例えば、赤外線レーザ発光素子を含む。送信部１は、制御部７から供給される駆動信号「Ｓｇ１」に基づき駆動する。 The transmitter 1 is a light source that emits pulsed laser light toward the beam splitter 3. The transmitter 1 includes, for example, an infrared laser light emitting element. The transmitter 1 is driven based on a drive signal “Sg1” supplied from the controller 7.
　受信部２は、例えばアバランシェフォトダイオード（Ａｖａｌａｎｃｈｅ　ＰｈｏｔｏＤｉｏｄｅ）であり、受光した光量に対応する検出信号「Ｓｇ２」を生成し、生成した検出信号Ｓｇ２を制御部７へ供給する。 The receiving unit 2 is, for example, an avalanche photodiode, generates a detection signal “Sg2” corresponding to the amount of received light, and supplies the generated detection signal Sg2 to the control unit 7.
　ビームスプリッタ３は、送信部１から出射されるパルス状のレーザ光を透過する。また、ビームスプリッタ３は、スキャナ５によって反射された反射光を、受信部２に向けて反射する。 The beam splitter 3 transmits the pulsed laser light emitted from the transmitter 1. Furthermore, the beam splitter 3 reflects the reflected light reflected by the scanner 5 toward the receiving section 2 .
　スキャナ５は、例えば静電駆動方式のミラー（ＭＥＭＳミラー）であり、制御部７から供給される駆動信号「Ｓｇ３」に基づき、傾き（即ち光走査の角度）が所定の範囲内で変化する。そして、スキャナ５は、ビームスプリッタ３を透過したレーザ光をライダ１００の外部へ向けて反射すると共に、ライダ１００の外部から入射する反射光をビームスプリッタ３へ向けて反射する。 The scanner 5 is, for example, an electrostatically driven mirror (MEMS mirror), and the tilt (that is, the angle of optical scanning) changes within a predetermined range based on the drive signal "Sg3" supplied from the control unit 7. Then, the scanner 5 reflects the laser light that has passed through the beam splitter 3 toward the outside of the lidar 100 and reflects the reflected light that is incident from the outside of the lidar 100 toward the beam splitter 3.
　また、スキャナ５には、ピエゾセンサ６が設けられている。ピエゾセンサ６は、スキャナ５のミラー部を支持するトーションバーの応力により生じる歪みを検出する。ピエゾセンサ６は、生成した検出信号「Ｓｇ４」を、制御部７へ供給する。検出信号Ｓｇ４は、スキャナ５の向きの検出に用いられる。 Further, the scanner 5 is provided with a piezo sensor 6. The piezo sensor 6 detects distortion caused by stress in the torsion bar that supports the mirror portion of the scanner 5. The piezo sensor 6 supplies the generated detection signal “Sg4” to the control unit 7. The detection signal Sg4 is used to detect the orientation of the scanner 5.
　メモリ８は、ＲＡＭ（Ｒａｎｄｏｍ　Ａｃｃｅｓｓ　Ｍｅｍｏｒｙ）、ＲＯＭ（Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）、フラッシュメモリなどの各種の揮発性メモリ及び不揮発性メモリにより構成される。メモリ８は、制御部７が所定の処理を実行するために必要なプログラムを記憶する。また、メモリ８は、制御部７により参照される各種パラメータを記憶する。また、メモリ８には、制御部７により生成された点群データが記憶される。 The memory 8 is composed of various types of volatile memory and nonvolatile memory such as RAM (Random Access Memory), ROM (Read Only Memory), and flash memory. The memory 8 stores programs necessary for the control unit 7 to execute predetermined processing. Furthermore, the memory 8 stores various parameters referenced by the control unit 7. The memory 8 also stores point cloud data generated by the control unit 7.
　制御部７は、例えば、ＣＰＵ（Ｃｅｎｔｒａｌ　Ｐｒｏｃｅｓｓｉｎｇ　Ｕｎｉｔ）、ＧＰＵ（Ｇｒａｐｈｉｃｓ　Ｐｒｏｃｅｓｓｉｎｇ　Ｕｎｉｔ）などの各種プロセッサを含む。制御部７は、メモリ８に記憶されたプログラムを実行することで、所定の処理を実行する。制御部７は、プログラムを実行するコンピュータの一例である。なお、制御部７は、プログラムによるソフトウェアで実現することに限ることなく、ハードウェア、ファームウェア、及びソフトウェアのうちのいずれかの組合せ等により実現されてもよい。また、制御部７は、ＦＰＧＡ（Ｆｉｅｌｄ－Ｐｒｏｇｒａｍｍａｂｌｅ　ｇａｔｅ　ａｒｒａｙ）又はマイクロコントローラ等の、ユーザがプログラミング可能な集積回路であってもよく、ＡＳＳＰ（Ａｐｐｌｉｃａｔｉｏｎ　Ｓｐｅｃｉｆｉｃ　Ｓｔａｎｄａｒｄ　Ｐｒｏｄｕｃｅ）、ＡＳＩＣ（Ａｐｐｌｉｃａｔｉｏｎ　Ｓｐｅｃｉｆｉｃ　Ｉｎｔｅｇｒａｔｅｄ　Ｃｉｒｃｕｉｔ）等であってもよい。 The control unit 7 includes various processors such as a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit). The control unit 7 executes a predetermined process by executing a program stored in the memory 8. The control unit 7 is an example of a computer that executes a program. Note that the control unit 7 is not limited to being implemented as software based on a program, and may be implemented as a combination of hardware, firmware, and software. Further, the control unit 7 may be a user-programmable integrated circuit such as an FPGA (Field-Programmable Gate Array) or a microcontroller; Specific Integrated Circuit) etc. It may be.
　制御部７は、機能的には、送信駆動ブロック７０と、スキャナ駆動ブロック７１と、点群データ生成ブロック７２と、点群データ処理ブロック７３と、を有する。 Functionally, the control unit 7 includes a transmission drive block 70, a scanner drive block 71, a point cloud data generation block 72, and a point cloud data processing block 73.
　送信駆動ブロック７０は、送信部１を駆動する駆動信号Ｓｇ１を出力する。駆動信号Ｓｇ１は、送信部１に含まれるレーザ発光素子の発光時間と、当該レーザ発光素子の発光強度を制御するための情報を含む。送信駆動ブロック７０は、駆動信号Ｓｇ１に基づき、送信部１に含まれるレーザ発光素子の発光強度を制御する。 The transmission drive block 70 outputs a drive signal Sg1 that drives the transmission section 1. The drive signal Sg1 includes information for controlling the light emission time of the laser light emitting element included in the transmitter 1 and the light emission intensity of the laser light emitting element. The transmission drive block 70 controls the light emission intensity of the laser light emitting element included in the transmitter 1 based on the drive signal Sg1.
　スキャナ駆動ブロック７１は、スキャナ５を駆動するための駆動信号Ｓｇ３を出力する。この駆動信号Ｓｇ３は、スキャナ５の共振周波数に対応する水平駆動信号と、垂直走査するための垂直駆動信号と、を含む。また、スキャナ駆動ブロック７１は、ピエゾセンサ６から出力される検出信号Ｓｇ４を監視することで、スキャナ５の走査角度（すなわちレーザ光の出射方向）を検出する。 The scanner drive block 71 outputs a drive signal Sg3 for driving the scanner 5. This drive signal Sg3 includes a horizontal drive signal corresponding to the resonance frequency of the scanner 5 and a vertical drive signal for vertical scanning. Further, the scanner drive block 71 detects the scanning angle of the scanner 5 (that is, the direction in which the laser beam is emitted) by monitoring the detection signal Sg4 output from the piezo sensor 6.
　点群データ生成ブロック７２は、受信部２から供給される検出信号Ｓｇ２に基づき、ライダ１００を基準点として、レーザ光が照射された物体までの距離と方向とを出射されたパルス光毎に示した点群データを生成する。この場合、点群データ生成ブロック７２は、レーザ光を出射してから受信部２が反射光を検出するまでの時間を、光の飛行時間（Ｔｉｍｅ　ｏｆ　Ｆｌｉｇｈｔ）として算出する。そして、点群データ生成ブロック７２は、算出した飛行時間に応じた距離と、受信部２が受信した反射光に対応するレーザ光の出射方向との組を示す点群データを生成し、生成した点群データを点群データ処理ブロック７３に供給する。以後では、視野範囲内の１回分の走査により得られる点群データを、１フレーム分の点群データとする。ここで、点群データは、計測された点（被計測点）を画素とし、各被計測点の計測距離を画素値とする画像とみなすことができる。この場合、各画素は、縦方向の並びにおいて仰俯角におけるレーザ光の出射方向が異なり、横方向の並びにおいて水平角におけるレーザ光の出射方向が異なる。そして、画素ごとに、対応する出射方向及び計測距離の組に基づき、３次元座標系での座標値が求められる。なお、点群データ生成ブロック７２は、点群データにおいて物体を誤検知することで生成されたノイズデータを除去する処理を行い、ノイズデータを除去した点群データを生成してもよい。送信部１、受信部２、ビームスプリッタ３、スキャナ５、送信駆動ブロック７０、スキャナ駆動ブロック７１、及び点群データ生成ブロック７２は、「光計測装置」の一例である。 Based on the detection signal Sg2 supplied from the receiving unit 2, the point cloud data generation block 72 indicates, for each emitted pulsed light, the distance and direction to the object irradiated with the laser light, using the lidar 100 as a reference point. generate point cloud data. In this case, the point cloud data generation block 72 calculates the time from when the laser beam is emitted until the receiving unit 2 detects the reflected light as the time of flight of the light. Then, the point cloud data generation block 72 generates point cloud data indicating a set of the distance according to the calculated flight time and the emission direction of the laser beam corresponding to the reflected light received by the receiving unit 2. The point cloud data is supplied to a point cloud data processing block 73. Hereinafter, the point cloud data obtained by one scan within the field of view will be referred to as one frame of point cloud data. Here, the point cloud data can be regarded as an image whose pixels are measured points (points to be measured) and whose pixel values are the measured distances of each point to be measured. In this case, each pixel has a different emitting direction of laser light at an elevation/depression angle when arranged in the vertical direction, and a different emitting direction of laser light at a horizontal angle when arranged in a horizontal direction. Then, for each pixel, coordinate values in the three-dimensional coordinate system are determined based on the corresponding set of emission direction and measurement distance. Note that the point cloud data generation block 72 may perform a process of removing noise data generated by erroneously detecting an object in the point cloud data, and generate point cloud data from which the noise data has been removed. The transmitter 1, the receiver 2, the beam splitter 3, the scanner 5, the transmission drive block 70, the scanner drive block 71, and the point cloud data generation block 72 are an example of an "optical measurement device."
　点群データ処理ブロック７３は、点群データ生成ブロック７２から供給される点群データに基づき、水面距離を算出する。この場合、まず、点群データ処理ブロック７３は、反射部材２００（２００Ａ，２００Ｂ）の反射光を受光することで生成された点群データ（「反射部材計測データ」とも呼ぶ。）を、点群データ生成ブロック７２から供給される点群データから抽出する。ここで、反射部材計測データは、図１における仮想反射部材２００ａ、２００ｂを仮想的な被計測点として表したデータである。そして、点群データ処理ブロック７３は、反射部材計測データが示す計測距離の１／２を、水面距離として算出する。そして、点群データ処理ブロック７３は、算出した水面距離をメモリ８に記憶する。点群データ処理ブロック７３は、「取得手段」、「抽出手段」、「算出手段」、「推定手段」、「判定手段」及びプログラムを実行するコンピュータの一例である。 The point cloud data processing block 73 calculates the water surface distance based on the point cloud data supplied from the point cloud data generation block 72. In this case, first, the point cloud data processing block 73 converts point cloud data (also referred to as "reflecting member measurement data") generated by receiving reflected light from the reflecting members 200 (200A, 200B) into point cloud data. It is extracted from the point cloud data supplied from the data generation block 72. Here, the reflective member measurement data is data representing the virtual reflective members 200a and 200b in FIG. 1 as virtual measured points. Then, the point cloud data processing block 73 calculates 1/2 of the measured distance indicated by the reflective member measurement data as the water surface distance. Then, the point cloud data processing block 73 stores the calculated water surface distance in the memory 8. The point cloud data processing block 73 is an example of an "acquisition means", "extraction means", "calculation means", "estimation means", "determination means", and a computer that executes a program.
　ここで、反射部材計測データの抽出方法について補足説明する。点群データ処理ブロック７３は、反射部材２００の個数、サイズ、及び設置場所（反射部材２００同士の相対位置関係を含む）等に基づき、反射部材計測データを抽出してもよい。例えば、点群データ処理ブロック７３は、点群データが表す各被計測点の計測距離に基づき、各被計測点のクラスタリングを行う。この場合、点群データ処理ブロック７３は、任意のクラスタリング処理に基づき、被計測点のクラスタを決定してもよい。そして、点群データ処理ブロック７３は、決定したクラスタのうち、所定個数以上の要素数を有するクラスタを表すデータを、反射部材計測データとして抽出する。上述の所定個数は、反射部材２００のサイズに応じた個数となるように予め定められる。また、点群データ処理ブロック７３は、さらに、反射部材２００の個数及び相対位置に関する条件を満たすクラスタのデータを、反射部材計測データとして抽出してもよい。この場合、点群データ処理ブロック７３は、例えば、反射部材２００の個数と同数のクラスタの組であって、予め測定した反射部材２００の相対位置関係を満たすクラスタの組を、反射部材計測データとして抽出する。これにより、点群データ処理ブロック７３は、水面に浮いている物等を表すデータを誤って反射部材計測データとして抽出するのを防ぐことができる。 Here, a supplementary explanation will be given about the method for extracting the reflective member measurement data. The point cloud data processing block 73 may extract reflective member measurement data based on the number, size, and installation location of the reflective members 200 (including the relative positional relationship between the reflective members 200). For example, the point cloud data processing block 73 performs clustering of each measured point based on the measured distance of each measured point represented by the point cloud data. In this case, the point cloud data processing block 73 may determine clusters of measured points based on arbitrary clustering processing. Then, the point cloud data processing block 73 extracts data representing clusters having a predetermined number or more of elements from among the determined clusters as reflective member measurement data. The above-mentioned predetermined number is determined in advance so as to correspond to the size of the reflecting member 200. Further, the point cloud data processing block 73 may further extract cluster data that satisfies conditions regarding the number and relative positions of the reflective members 200 as the reflective member measurement data. In this case, the point cloud data processing block 73 uses, for example, a set of clusters of the same number as the number of reflective members 200, which satisfy the relative positional relationship of the reflective members 200 measured in advance, as the reflective member measurement data. Extract. Thereby, the point cloud data processing block 73 can prevent data representing objects floating on the water surface from being erroneously extracted as reflective member measurement data.
（３）処理フロー
　図３は、ライダ１００が実行するフローチャートの一例である。 (3) Processing Flow FIG. 3 is an example of a flowchart executed by the rider 100.
　まず、点群データ生成ブロック７２は、検出信号Ｓｇ２に基づき、点群データを生成する（ステップＳ０１）。この場合、点群データ生成ブロック７２は、ライダ１００の走査対象範囲における１回分の走査により生成された検出信号Ｓｇ２に基づき、現処理時刻に対応するフレーム（「現フレーム」とも呼ぶ。）の点群データを生成する。 First, the point cloud data generation block 72 generates point cloud data based on the detection signal Sg2 (step S01). In this case, the point cloud data generation block 72 generates the points of the frame corresponding to the current processing time (also referred to as the "current frame") based on the detection signal Sg2 generated by one scan in the scanning target range of the lidar 100. Generate group data.
　次に、点群データ生成ブロック７２は、ステップＳ０１で生成された点群データからノイズデータを除去する処理であるノイズ除去処理を実行する（ステップＳ０２）。この場合、点群データ生成ブロック７２は、任意のノイズ除去処理を実行してもよい。例えば、点群データ生成ブロック７２は、受信部２が受信した反射光の強度が所定の閾値未満となるデータを、ノイズデータとみなして点群データから除去する。 Next, the point cloud data generation block 72 executes a noise removal process, which is a process of removing noise data from the point group data generated in step S01 (step S02). In this case, the point cloud data generation block 72 may perform arbitrary noise removal processing. For example, the point cloud data generation block 72 regards data in which the intensity of reflected light received by the receiving unit 2 is less than a predetermined threshold as noise data and removes it from the point cloud data.
　次に、点群データ処理ブロック７３は、ノイズ除去処理後の点群データから反射部材２００に反射されたレーザ光に基づき生成された反射部材計測データを抽出する（ステップＳ０３）。そして、点群データ処理ブロック７３は、抽出した反射部材計測データが表す計測距離に基づき、水面距離を算出する（ステップＳ０４）。この場合、点群データ処理ブロック７３は、例えば、反射部材計測データが表す被計測点ごとの計測距離の平均値の１／２を、水面距離として算出する。なお、点群データ処理ブロック７３は、反射部材計測データが表す被計測点ごとの計測距離の平均値を用いる代わりに、反射部材計測データが表す被計測点ごとの計測距離の平均値以外の代表値（例えば中央値）を用いてもよい。 Next, the point cloud data processing block 73 extracts reflective member measurement data generated based on the laser beam reflected by the reflective member 200 from the point cloud data after the noise removal process (step S03). Then, the point cloud data processing block 73 calculates the water surface distance based on the measured distance represented by the extracted reflective member measurement data (step S04). In this case, the point cloud data processing block 73 calculates, for example, 1/2 of the average value of the measured distances for each measured point represented by the reflective member measurement data as the water surface distance. Note that, instead of using the average value of the measured distance for each measured point represented by the reflective member measurement data, the point cloud data processing block 73 uses a representative value other than the average value of the measured distance for each measured point represented by the reflective member measurement data. A value (eg median value) may also be used.
　（４）変形例
　以下に、上述の実施例に好適な各変形例について説明する。以下に説明する変形例は、任意に組み合わせて上述の実施例に適用してもよい。 (4) Modifications Below, modifications suitable for the above embodiment will be explained. The modifications described below may be applied to the above embodiments in any combination.
　（変形例１）
　点群データ処理ブロック７３は、点群データに基づき、計測対象となる河川等の状況を推定してもよい。 (Modification 1)
The point cloud data processing block 73 may estimate the situation of a river or the like to be measured based on the point cloud data.
　第１の例では、点群データ処理ブロック７３は、計測対象の状況として、水面の波立ちの度合いを推定する。この場合、点群データ処理ブロック７３は、所定期間に得られる点群データに基づき、フレーム周期ごとに得られる点群データに基づき水面距離を時系列において算出し、当該所定期間内における水面距離の最大値と最小値とを算出する。そして、点群データ処理ブロック７３は、上述の水面距離の最大値と最小値とに基づき、波立ちの度合いを推定する。この場合、点群データ処理ブロック７３は、例えば、上述の水面距離の最大値と最小値の差を、波立ちの度合いを表す指標として算出する。この例によれば、点群データ処理ブロック７３は、水面距離に加えて、河川等での波立ちの度合いを的確に推定することができる。 In the first example, the point cloud data processing block 73 estimates the degree of ripples on the water surface as the measurement target situation. In this case, the point cloud data processing block 73 calculates the water surface distance in time series based on the point cloud data obtained for each frame period based on the point cloud data obtained in a predetermined period, and calculates the water surface distance within the predetermined period. Calculate the maximum value and minimum value. Then, the point cloud data processing block 73 estimates the degree of ripples based on the maximum and minimum values of the water surface distance. In this case, the point cloud data processing block 73 calculates, for example, the difference between the maximum value and the minimum value of the water surface distance as an index representing the degree of ripples. According to this example, the point cloud data processing block 73 can accurately estimate the degree of undulation in a river or the like in addition to the water surface distance.
　なお、水面が波打っている場合、ライダ１００からの光が水面で散乱し、相対的に反射物から戻ってくる光量の割合が減少し、水面からの直接反射が増加することになる。また、水面が穏やかな時は反射部材２００の形状を忠実に表した点群データが取得できるのに対し、荒れている時は点群データが表す反射部材２００の形状が乱れ、かつ、計測した水面距離も短時間で変化する。以上を勘案し、点群データ処理ブロック７３は、点群データが示す反射部材２００の形状が乱れている場合（例えば実際の反射部材２００の形状との類似度が所定度合い以下の場合）、又は、水面距離も短時間で変化する場合（例えば所定期間における水面距離の分散値が所定値以上の場合）には、水面が波打っている状況であると推定してもよい。 Note that when the water surface is undulating, the light from the rider 100 is scattered on the water surface, the proportion of the amount of light returning from the reflecting object is relatively reduced, and the direct reflection from the water surface is increased. Furthermore, when the water surface is calm, point cloud data that faithfully represents the shape of the reflective member 200 can be obtained, whereas when the water surface is rough, the shape of the reflective member 200 represented by the point cloud data is distorted, and the measured The water surface distance also changes in a short time. Taking the above into consideration, the point cloud data processing block 73 performs processing when the shape of the reflective member 200 indicated by the point cloud data is disordered (for example, when the degree of similarity with the shape of the actual reflective member 200 is less than a predetermined degree), or If the water surface distance also changes in a short time (for example, if the variance value of the water surface distance in a predetermined period is greater than or equal to a predetermined value), it may be estimated that the water surface is undulating.
　第２の例では、点群データ処理ブロック７３は、計測対象の状況として、水面に浮いている物（水面浮遊物）に基づき、流速を算出する。この場合、点群データ処理ブロック７３は、落ち葉やビニール袋などの水面浮遊物を点群データに基づき検出した場合、当該水面浮遊物の検出位置をフレーム周期ごとに得られる点群データに基づきフレーム周期ごとに算出する。そして、点群データ処理ブロック７３は、フレーム周期ごとの水面浮遊物の検出位置の推移に基づき、流速を算出する。この場合、例えば、点群データ処理ブロック７３は、フレーム周期ごとに得られる点群データに対してクラスタリングを行い、水面距離から所定距離以内の計測距離であって要素数が所定数以上の被計測点のクラスタを、水面浮遊物を表すクラスタとして検出する。この例によれば、点群データ処理ブロック７３は、水面距離に加えて、河川等の流速を的確に推定することができる。 In the second example, the point cloud data processing block 73 calculates the flow velocity based on objects floating on the water surface (water surface floating objects) as the measurement target situation. In this case, when a floating object on the water surface such as fallen leaves or a plastic bag is detected based on the point cloud data, the point cloud data processing block 73 detects the detected position of the floating object on the water surface in a frame based on the point cloud data obtained every frame period. Calculate each cycle. Then, the point cloud data processing block 73 calculates the flow velocity based on the transition of the detected position of the water surface floating object for each frame period. In this case, for example, the point cloud data processing block 73 performs clustering on the point cloud data obtained for each frame period, and performs clustering on the point cloud data obtained for each frame period, and performs clustering on the point cloud data obtained at each frame period. Detect clusters of points as clusters representing objects floating on the water surface. According to this example, the point cloud data processing block 73 can accurately estimate the flow velocity of a river or the like in addition to the water surface distance.
　（変形例２）
　ライダ１００が水面と正対する代わりに、ライダ１００が水面に対して傾いて設置されてもよい。 (Modification 2)
Instead of the rider 100 directly facing the water surface, the rider 100 may be installed at an angle with respect to the water surface.
　図４は、本変形例に係る測距システムの第１構成例である。本変形例では、水面に対するライダ１００の向きが角度「θ０」（０＜θ＜９０）に設定されており、ライダ１００から出射され水面で反射したレーザ光が照射される位置に反射部材２００Ｃが設けられている。また、図４では、地表と高水位時の水面と低水位時の水面とを示しており、地表と反射部材２００Ｃとの距離を「ｈ１」、地表から低水位時の水面との距離を「Ｗ」、地表からライダ１００の距離を「ｈ０」としている。また、仮想反射部材２００ｃに対する計測距離を「ｄ」としている。また、仮想反射部材２００ｃは、高水位時に計測される反射部材２００Ｃの偽点を表し、仮想反射部材２０１ｃは、低水位時に計測される反射部材２００Ｃの偽点を表す。 FIG. 4 shows a first configuration example of a ranging system according to this modification. In this modification, the direction of the lidar 100 with respect to the water surface is set at an angle "θ0" (0<θ<90), and the reflective member 200C is placed at a position where the laser beam emitted from the lidar 100 and reflected on the water surface is irradiated. It is provided. Moreover, in FIG. 4, the ground surface, the water surface at high water level, and the water surface at low water level are shown, and the distance between the ground surface and the reflective member 200C is "h1", and the distance from the ground surface to the water surface at low water level is " W", and the distance of the rider 100 from the ground surface is "h0". Further, the measured distance to the virtual reflecting member 200c is set to "d". Further, the virtual reflective member 200c represents a false point of the reflective member 200C measured at a high water level, and the virtual reflective member 201c represents a false point of the reflective member 200C measured at a low water level.
　図４では、ライダ１００と反射部材２００Ｃとが離れた位置に設けられており、かつライダ１００が水面に対して傾いて設置されている。なお、反射部材２００Ｃには、反射部材２００Ｃに水面の反射光を集光させるための図１のフード３００が設けられてもよい。ライダ１００にも同様に、ライダ１００に水面の反射光を集光させるための図１のフード３００が設けられてもよい。 In FIG. 4, the rider 100 and the reflecting member 200C are provided at separate positions, and the rider 100 is installed at an angle with respect to the water surface. Note that the reflecting member 200C may be provided with a hood 300 shown in FIG. 1 for condensing light reflected from the water surface onto the reflecting member 200C. Similarly, the rider 100 may be provided with the hood 300 shown in FIG. 1 for condensing light reflected from the water surface onto the rider 100.
　そして、図４の例では、ライダ１００が出射したレーザ光は、水面、反射部材２００Ｃ、水面の順に反射し、その反射光がライダ１００により受光される。この場合、ライダ１００は、上述の反射光を受光することで生成した受光信号に基づき、水中に存在する仮想の仮想反射部材２００ｃを表す点群データを生成する。ここで、上述の反射光は、ライダ１００と水面との間を２往復しており、かつ、ライダ１００が水面に対して所定角度だけ傾いていることから、仮想反射部材２００ｃの計測距離に基づき水面距離を算出することが可能である。 In the example of FIG. 4, the laser beam emitted by the lidar 100 is reflected in the order of the water surface, the reflecting member 200C, and the water surface, and the reflected light is received by the lidar 100. In this case, the lidar 100 generates point cloud data representing the virtual virtual reflective member 200c existing in the water based on the light reception signal generated by receiving the above-mentioned reflected light. Here, the above-mentioned reflected light makes two round trips between the rider 100 and the water surface, and since the rider 100 is tilted at a predetermined angle with respect to the water surface, the reflected light is based on the measured distance of the virtual reflecting member 200c. It is possible to calculate the water surface distance.
　例えば、図４において低水位時を例にとると、幾何学的計算に基づき、以下の式が成立する。
　（ｈ０＋Ｗ＋Ｗ＋ｈ１）／ｄ＝ｓｉｎθ０ For example, taking the case of low water level in FIG. 4 as an example, the following equation holds true based on geometric calculations.
(h0+W+W+h1)/d=sinθ0
　従って、地表からの距離で表す水位Ｗは、
　Ｗ＝（ｄ＊ｓｉｎθ０－ｈ０－ｈ１）／２
となる。 Therefore, the water level W expressed as the distance from the ground surface is
W=(d*sinθ0-h0-h1)/2
becomes.
　ここで、反射部材２００Ｃが水平に設けられていない場合について考察する。図５は、本変形例に係る測距システムの第２構成例である。第２構成例では、反射部材２００Ｃが地表及び水面に対して角度「θ１」だけ傾けられている。また、「ｈ１」は、反射部材２００Ｃの最も低い位置から地表までの距離を表している。 Here, a case will be considered where the reflecting member 200C is not provided horizontally. FIG. 5 shows a second configuration example of the ranging system according to this modification. In the second configuration example, the reflecting member 200C is inclined by an angle "θ1" with respect to the ground surface and the water surface. Moreover, "h1" represents the distance from the lowest position of the reflecting member 200C to the ground surface.
　図５に示すように、反射部材２００Ｃが水平でない場合は、光の当たる位置によって測定される反射部材２００Ｃの計測距離（高さ）が変わってしまうので、そのままでは正確な水位が測れない。以上を勘案し、例えば設置位置及び角度の分かっている反射部材２００Ｃに、反射部材２００Ｃ内での位置（高さ）に応じた特徴を設けておく。そして、ライダ１００は、得られた点群データが示す反射部材２００Ｃのパターンと上記特徴とを照合することによって、計測した部分の位置（高さ）を認識する。 As shown in FIG. 5, if the reflective member 200C is not horizontal, the measured distance (height) of the reflective member 200C will change depending on the position of the light, so the water level cannot be measured accurately. Taking the above into consideration, for example, the reflecting member 200C, whose installation position and angle are known, is provided with features corresponding to the position (height) within the reflecting member 200C. Then, the lidar 100 recognizes the position (height) of the measured portion by comparing the pattern of the reflective member 200C indicated by the obtained point cloud data with the above characteristics.
　図６は、位置に応じた特徴が設けられた反射部材２００Ｃとライダ１００により検出される反射部材２００Ｃの点群データとの対応関係を示す図である。図６の例では、反射部材２００Ｃの表面に高反射矩形領域のパターンが設けられており、上記の矩形領域は反射部材２００Ｃの下端から上端にかけて徐々に小さくなっている。そして、図６の例では、ライダ１００は、破線部分の反射部材２００Ｃの領域２５０が検出されたことを点群データの被計測点のパターンから認識し、反射部材２００Ｃの下端から検出領域２５０までの距離「ｙ１」を算出する。ここで、ライダ１００は、反射部材２００Ｃの表面に設けられた高反射矩形領域の大きさと反射部材２００Ｃ内での位置との対応関係を示す情報を予め記憶しておくことで、上記距離ｙ１を算出する。 FIG. 6 is a diagram showing the correspondence between the reflective member 200C provided with features depending on the position and the point group data of the reflective member 200C detected by the lidar 100. In the example of FIG. 6, a pattern of highly reflective rectangular areas is provided on the surface of the reflective member 200C, and the rectangular area gradually becomes smaller from the lower end to the upper end of the reflective member 200C. In the example of FIG. 6, the lidar 100 recognizes from the pattern of the measured points of the point cloud data that the area 250 of the reflective member 200C indicated by the broken line has been detected, and extends from the lower end of the reflective member 200C to the detection area 250. The distance "y1" is calculated. Here, the rider 100 stores in advance information indicating the correspondence between the size of the highly reflective rectangular area provided on the surface of the reflective member 200C and the position within the reflective member 200C, thereby determining the distance y1. calculate.
　そして、ライダ１００は、距離ｙ１を用いることで水面距離を算出する。図５において、低水位時における例に取ると、幾何学的計算に基づき、以下の式が成立する。
　（ｈ０＋Ｗ＋Ｗ＋ｈ１＋ｙ１＊ｓｉｎθ１）／ｄ＝ｓｉｎθ０ Then, the rider 100 calculates the water surface distance by using the distance y1. In FIG. 5, taking the example of a low water level, the following equation holds true based on geometric calculations.
(h0+W+W+h1+y1*sinθ1)/d=sinθ0
　従って、地表からの距離で表す水位Ｗは、
　Ｗ＝（ｄ＊ｓｉｎθ０－ｈ０－ｈ１－ｙ１＊ｓｉｎθ１）
となる。 Therefore, the water level W expressed as the distance from the ground surface is
W=(d*sinθ0-h0-h1-y1*sinθ1)
becomes.
　このように、本変形例の態様においても、ライダ１００は、水面距離を好適に算出することができる。 In this way, also in the aspect of this modification, the rider 100 can suitably calculate the water surface distance.
　（変形例３）
　ライダ１００は、水面の波立ち状況に基づき、水面距離の計測方法を決定してもよい。 (Modification 3)
The rider 100 may determine the water surface distance measurement method based on the undulating state of the water surface.
　具体的には、ライダ１００は、水面に所定度合い以上の波立ちが発生している場合には、水面を計測した点群データ（「水面計測データ」とも呼ぶ。）を抽出し、当該水面計測データに基づき水面距離を算出する。一般に、水面が波立っている場合には、乱反射により水面からライダ１００に直接帰ってくる戻り光が相対的に多くなり、水面計測データを取得することが可能となる。従って、ライダ１００は、水面に所定度合い以上の波立ちが発生している場合、例えば、点群データのクラスタリングを行い、所定長以上の長さを有するクラスタのデータを、水面計測データとして抽出する。そして、ライダ１００は、水面計測データが表す計測距離の平均等を、水面距離として算出する。一方、ライダ１００は、水面に所定度合い以上の波立ちが発生していない場合には、反射部材計測データを抽出し、反射部材計測データに基づき水面距離を算出する。なお、実際には、波立ちのある場合と無い場合のいずれでも、水面からの直接反射、反射部材からの反射の両方が計測される。よって、ライダ１００は、上記の例に代えて、水面の波立ち状況にかかわらず、水面計測データに基づき水面距離を算出してもよく、水面計測データおよび反射部材計測データの双方に基づき水面距離を算出してもよい。この場合、例えば、ライダ１００は、水面計測データに基づき算出した水面距離と、反射部材計測データに基づき算出した水面距離とを平均又は加重平均することで、最終的な水面距離を算出する。この場合、例えば、泡立ち度合いが高い程、水面計測データに基づき算出した水面距離に対する重みを大きくするとよい。 Specifically, when ripples of a predetermined degree or more occur on the water surface, the rider 100 extracts point cloud data (also referred to as "water surface measurement data") obtained by measuring the water surface, and uses the water surface measurement data. Calculate the water surface distance based on Generally, when the water surface is rippled, the amount of return light that returns directly from the water surface to the lidar 100 increases relatively due to diffuse reflection, making it possible to acquire water surface measurement data. Therefore, when ripples of a predetermined degree or more occur on the water surface, the rider 100 performs clustering of point cloud data, for example, and extracts data of clusters having a length of a predetermined length or more as water surface measurement data. Then, the rider 100 calculates the average of the measured distances represented by the water surface measurement data as the water surface distance. On the other hand, when the ripples of a predetermined degree or more are not generated on the water surface, the rider 100 extracts the reflective member measurement data and calculates the water surface distance based on the reflective member measurement data. Note that, in reality, both direct reflection from the water surface and reflection from the reflective member are measured, both with and without ripples. Therefore, instead of the above example, the rider 100 may calculate the water surface distance based on the water surface measurement data regardless of the rippling condition of the water surface, and may calculate the water surface distance based on both the water surface measurement data and the reflective member measurement data. It may be calculated. In this case, for example, the rider 100 calculates the final water surface distance by averaging or weighted averaging the water surface distance calculated based on the water surface measurement data and the water surface distance calculated based on the reflective member measurement data. In this case, for example, the higher the degree of foaming, the greater the weight given to the water surface distance calculated based on the water surface measurement data.
　ここで、点群データ処理ブロック７３は、例えば、点群データに基づき、所定度合い以上の水面の波立ちが発生しているか否かを判定してもよい。この場合、点群データ処理ブロック７３は、例えば、点群データの反射パターンから所定度合い以上の水面の波立ちの有無を判定するように学習することで、上述の判定を行ってもよい。 Here, the point cloud data processing block 73 may determine, for example, based on the point cloud data, whether or not ripples on the water surface are occurring to a predetermined degree or more. In this case, the point cloud data processing block 73 may perform the above-mentioned determination by, for example, learning to determine the presence or absence of ripples on the water surface to a predetermined degree or more from the reflection pattern of the point cloud data.
　このようにすることで、ライダ１００は、水面の波立ち状況に応じた方法により水面距離を的確に計測することができる。 By doing so, the rider 100 can accurately measure the distance on the water surface using a method according to the ripples on the water surface.
　（変形例４）
　点群データ処理ブロック７３は、水面計測データおよび反射部材計測データの双方を用いて水面距離を算出する場合、双方の計測値の差から算出された水面距離の信頼度を判定してもよい。 (Modification 4)
When calculating the water surface distance using both the water surface measurement data and the reflecting member measurement data, the point cloud data processing block 73 may determine the reliability of the water surface distance calculated from the difference between the two measurement values.
　具体的には、水面計測データに基づき算出された水面距離にライダ１００と反射部材２００との位置関係を加味した距離と、反射物計測データに基づき算出された水面距離と、の差を評価することで、算出された水面距離の信頼度を判定する。例えば図１の様にライダ１００と、反射部材２００と、が配置されているとき、ライダ１００と反射部材２００との位置関係（特に、水面に垂直な方向におけるライダ１００と反射部材２００との距離）が既知であるとした場合、水面計測データに基づき算出された水面距離にライダ１００と反射部材２００との位置関係を加味した距離と、反射物計測データに基づき算出された水面距離と、は理論的には同一の距離となる。従って、同時刻における水面計測データに基づき算出された水面距離にライダ１００と反射部材２００との位置関係を加味した距離と、反射物計測データに基づき算出された水面距離と、の差が大きい場合には信頼度が低いと判定し、差が小さい場合には信頼度が高いと判定することができる。 Specifically, the difference between the water surface distance calculated based on the water surface measurement data, taking into account the positional relationship between the lidar 100 and the reflecting member 200, and the water surface distance calculated based on the reflective object measurement data is evaluated. This determines the reliability of the calculated water surface distance. For example, when the lidar 100 and the reflective member 200 are arranged as shown in FIG. ) is known, the distance calculated based on the water surface measurement data, taking into account the positional relationship between the lidar 100 and the reflective member 200, and the water surface distance calculated based on the reflective object measurement data are: Theoretically, the distances are the same. Therefore, if there is a large difference between the water surface distance calculated based on the water surface measurement data at the same time, taking into account the positional relationship between the lidar 100 and the reflective member 200, and the water surface distance calculated based on the reflective object measurement data, If the difference is small, it can be determined that the reliability is low, and if the difference is small, it can be determined that the reliability is high.
　この場合、点群データ処理ブロック７３は、信頼度が高いと判断された水面距離のみを計測結果として採用してもよいし、信頼度が低いと判断された水面距離を計測結果として採用しないようにしてもよい。 In this case, the point cloud data processing block 73 may adopt only the water surface distance determined to have high reliability as the measurement result, or may avoid adopting the water surface distance determined to have low reliability as the measurement result. You can also do this.
　このようにすることで、点群データ処理ブロック７３は、より高精度に水面距離を算出することができる。 By doing so, the point cloud data processing block 73 can calculate the water surface distance with higher accuracy.
　なお、上記の例では図１を例に説明したが、ライダ１００と、反射部材２００と、の配置はこれに限られない。図４や図５の配置としてもよいし、それ以外の種々の配置としてもよい。 Note that although the above example has been described using FIG. 1 as an example, the arrangement of the rider 100 and the reflecting member 200 is not limited to this. The arrangement shown in FIGS. 4 and 5 may be used, or various other arrangements may be used.
　なお、上記の例では信頼度を「高い」「低い」の２段階で判定したが、信頼度の判定方法はこれに限られない。「高」「中」「低」の３段階としてもよいし、４段階以上としてもよい。 Note that in the above example, the reliability was determined in two stages, "high" and "low," but the method for determining the reliability is not limited to this. There may be three levels of "high," "medium," and "low," or there may be four or more levels.
　（変形例５）
　ライダ１００の構成は、図２に示す構成に限定されない。例えば、制御部７の点群データ処理ブロック７３及び点群データ処理ブロック７３に相当する機能を、ライダ１００とは別の装置が有してもよい。 (Modification 5)
The configuration of rider 100 is not limited to the configuration shown in FIG. 2. For example, a device different from the lidar 100 may have functions corresponding to the point cloud data processing block 73 and the point cloud data processing block 73 of the control unit 7.
　図７は、変形例に係るライダシステムの構成図である。ライダシステムは、ライダ１００Ｘと、情報処理装置４００とを有する。この場合、ライダ１００Ｘは、点群データ生成ブロック７２が生成する点群データを情報処理装置４００へ供給する。 FIG. 7 is a configuration diagram of a rider system according to a modification. The lidar system includes a lidar 100X and an information processing device 400. In this case, the lidar 100X supplies the point cloud data generated by the point cloud data generation block 72 to the information processing device 400.
　情報処理装置４００は、制御部７Ａと、メモリ８とを有する。メモリ８には、制御部７Ａが処理を実行するために必要な情報が記憶されている。制御部７Ａは、機能的には、点群データ取得ブロック７２Ａと、点群データ処理ブロック７３とを有する。点群データ取得ブロック７２Ａは、ライダ１００Ｘの点群データ生成ブロック７２が生成する点群データを受信し、受信した点群データを点群データ処理ブロック７３に供給する。点群データ処理ブロック７３は、点群データ取得ブロック７２Ａから供給される点群データに対し、上述した実施例の点群データ処理ブロック７３と同一の処理を実行する。 The information processing device 400 includes a control unit 7A and a memory 8. The memory 8 stores information necessary for the control unit 7A to execute processing. Functionally, the control unit 7A includes a point cloud data acquisition block 72A and a point cloud data processing block 73. The point cloud data acquisition block 72A receives the point cloud data generated by the point cloud data generation block 72 of the lidar 100X, and supplies the received point cloud data to the point cloud data processing block 73. The point cloud data processing block 73 performs the same processing as the point cloud data processing block 73 of the above-described embodiment on the point cloud data supplied from the point cloud data acquisition block 72A.
　以上説明したように、実施例に係る点群データ取得ブロック７２は、水面に向けられ、出射した光の反射光を受光することで測距を行う光計測装置が生成した点群データを取得する。そして、点群データ処理ブロック７３は、点群データから、水面で反射された前記光が照射される位置に設けられた反射部材２００を計測した反射部材計測データを抽出する。そして、点群データ処理ブロック７３は、反射部材計測データに基づき、水面距離を算出する。この態様により、点群データ処理ブロック７３は、水面距離を正確に計測することが可能となる。 As described above, the point cloud data acquisition block 72 according to the embodiment acquires point cloud data generated by an optical measurement device that performs distance measurement by receiving reflected light of emitted light directed toward the water surface. . Then, the point cloud data processing block 73 extracts reflective member measurement data obtained by measuring the reflective member 200 provided at a position where the light reflected on the water surface is irradiated from the point cloud data. Then, the point cloud data processing block 73 calculates the water surface distance based on the reflective member measurement data. This aspect allows the point cloud data processing block 73 to accurately measure the water surface distance.
　なお、上述した実施例において、プログラムは、様々なタイプの非一時的なコンピュータ可読媒体（Ｎｏｎ－Ｔｒａｓｉｔｏｒｙ　Ｃｏｍｐｕｔｅｒ　Ｒｅａｄａｂｌｅ　Ｍｅｄｉｕｍ）を用いて格納され、コンピュータであるコントローラ等に供給することができる。非一時的なコンピュータ可読媒体は、様々なタイプの実体のある記憶媒体（Ｔａｎｇｉｂｌｅ　Ｓｔｏｒａｇｅ　Ｍｅｄｉｕｍ）を含む。非一時的なコンピュータ可読媒体の例は、磁気記憶媒体（例えばフレキシブルディスク、磁気テープ、ハードディスクドライブ）、光磁気記憶媒体（例えば光磁気ディスク）、ＣＤ－ＲＯＭ（Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）、ＣＤ－Ｒ、ＣＤ－Ｒ／Ｗ、半導体メモリ（例えば、マスクＲＯＭ、ＰＲＯＭ（Ｐｒｏｇｒａｍｍａｂｌｅ　ＲＯＭ）、ＥＰＲＯＭ（Ｅｒａｓａｂｌｅ　ＰＲＯＭ）、フラッシュＲＯＭ、ＲＡＭ（Ｒａｎｄｏｍ　Ａｃｃｅｓｓ　Ｍｅｍｏｒｙ）を含む。 Note that in the embodiments described above, the program can be stored using various types of non-transitory computer readable media and supplied to a controller or the like that is a computer. Non-transitory computer-readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic storage media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical storage media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, Includes CD-R/W, semiconductor memory (for example, mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, and RAM (Random Access Memory).
　以上、実施形態を参照して本願発明を説明したが、本願発明は上記実施形態に限定されるものではない。本願発明の構成や詳細には、本願発明のスコープ内で当業者が理解し得る様々な変更をすることができる。すなわち、本願発明は、請求の範囲を含む全開示、技術的思想にしたがって当業者であればなし得るであろう各種変形、修正を含むことは勿論である。また、引用した上記の特許文献等の各開示は、本書に引用をもって繰り込むものとする。 Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above embodiments. The configuration and details of the present invention can be modified in various ways that can be understood by those skilled in the art within the scope of the present invention. That is, it goes without saying that the present invention includes the entire disclosure including the claims and various modifications and modifications that a person skilled in the art would be able to make in accordance with the technical idea. In addition, the disclosures of the above cited patent documents, etc. are incorporated into this document by reference.
　１　送信部
　２　受信部
　３　ビームスプリッタ
　５　スキャナ
　６　ピエゾセンサ
　７、７Ａ　制御部
　８　メモリ
　１００、１００Ｘ　ライダ
　４００　情報処理装置 1 Transmitter 2 Receiver 3 Beam splitter 5 Scanner 6 Piezo sensor 7, 7A Control unit 8 Memory 100, 100X Lidar 400 Information processing device

Claims (11)

  1. 　液体面に向けられ、出射した光の反射光を受光することで測距を行う光計測装置が生成した計測データを取得する取得手段と、
    　前記計測データから、前記液体面で反射された前記光が照射される位置に設けられた反射部材を計測した反射部材計測データを抽出する抽出手段と、
    　前記反射部材計測データに基づき、前記液体面までの距離を算出する算出手段と、
    を有する情報処理装置。     an acquisition means for acquiring measurement data generated by an optical measurement device that performs distance measurement by receiving reflected light of the emitted light directed toward the liquid surface;
    Extracting means for extracting reflective member measurement data obtained by measuring a reflective member provided at a position where the light reflected on the liquid surface is irradiated from the measurement data;
    Calculation means for calculating a distance to the liquid surface based on the reflective member measurement data;
    An information processing device having:
  2. 　前記光計測装置は液体面と正対し、
    　前記反射部材は、前記光計測装置に近接して設けられる、請求項１に記載の情報処理装置。     The optical measurement device directly faces the liquid surface,
    The information processing device according to claim 1, wherein the reflecting member is provided close to the optical measurement device.
  3. 　前記反射部材は、前記液体面からの距離が前記光計測装置と同一となる位置に設けられ、
    　前記算出手段は、前記反射部材計測データが示す計測距離の１／２を前記液体面までの距離として算出する、請求項２に記載の情報処理装置。     The reflecting member is provided at a position where the distance from the liquid surface is the same as that of the optical measurement device,
    The information processing device according to claim 2, wherein the calculation means calculates 1/2 of the measured distance indicated by the reflective member measurement data as the distance to the liquid surface.
  4. 　前記計測データに基づき、前記液体面の状況を推定する推定手段をさらに有する、請求項１～３のいずれか一項に記載の情報処理装置。 The information processing device according to any one of claims 1 to 3, further comprising estimation means for estimating the state of the liquid level based on the measurement data.
  5. 　前記推定手段は、前記算出手段が時系列により算出した前記液体面までの距離に基づき、前記液体面の波立ちの度合いを推定する、請求項４に記載の情報処理装置。 The information processing apparatus according to claim 4, wherein the estimating means estimates the degree of ripples on the liquid surface based on the distance to the liquid surface calculated in time series by the calculating means.
  6. 　前記液体面の波立ちの度合いが所定度合い以上であるか否か判定する判定手段をさらに有し、
    　　前記液体面の波立ちの度合いが所定度合い以上であると前記判定手段が判定した場合、
    　前記抽出手段は、前記計測データから、前記液体面を計測した液体面計測データを抽出し、
    　前記算出手段は、前記液体面計測データに基づき、前記液体面までの距離を算出する、請求項１～５のいずれか一項に記載の情報処理装置。     further comprising determining means for determining whether the degree of ripples on the liquid surface is equal to or higher than a predetermined degree;
    When the determining means determines that the degree of ripples on the liquid surface is equal to or higher than a predetermined degree,
    The extraction means extracts liquid level measurement data obtained by measuring the liquid level from the measurement data,
    The information processing device according to any one of claims 1 to 5, wherein the calculation means calculates the distance to the liquid surface based on the liquid level measurement data.
  7. 　前記反射部材には位置に応じたパターンが形成され、
    　前記算出手段は、前記反射部材計測データに基づき、前記反射部材において計測された位置を認識し、当該位置に基づき前記液体面までの距離を算出する、
    請求項１～６のいずれか一項に記載の情報処理装置。     A pattern corresponding to a position is formed on the reflective member,
    The calculation means recognizes a position measured on the reflection member based on the reflection member measurement data, and calculates a distance to the liquid surface based on the position.
    The information processing device according to any one of claims 1 to 6.
  8. 　液体面に向けられ、出射した光の反射光を受光することで測距を行う光計測装置と、
    　請求項１～７のいずれか一項に記載の情報処理装置と、を有する測距システム。     an optical measurement device that measures distance by receiving the reflected light of the emitted light directed toward the liquid surface;
    A distance measuring system comprising: the information processing device according to claim 1;
  9. 　コンピュータが、
    　液体面に向けられ、出射した光の反射光を受光することで測距を行う光計測装置が生成した計測データを取得し、
    　前記計測データから、前記液体面で反射された前記光が照射される位置に設けられた反射部材を計測した反射部材計測データを抽出し、
    　前記反射部材計測データに基づき、前記液体面までの距離を算出する、
    制御方法。     The computer is
    Obtain measurement data generated by an optical measurement device that measures distance by receiving the reflected light emitted from the liquid surface.
    extracting reflective member measurement data obtained by measuring a reflective member provided at a position where the light reflected on the liquid surface is irradiated from the measurement data;
    calculating a distance to the liquid surface based on the reflective member measurement data;
    Control method.
  10. 　液体面に向けられ、出射した光の反射光を受光することで測距を行う光計測装置が生成した計測データを取得し、
    　前記計測データから、前記液体面で反射された前記光が照射される位置に設けられた反射部材を計測した反射部材計測データを抽出し、
    　前記反射部材計測データに基づき、前記液体面までの距離を算出する処理をコンピュータに実行させるプログラム。     Obtain measurement data generated by an optical measurement device that measures distance by receiving the reflected light emitted from the liquid surface.
    extracting reflective member measurement data obtained by measuring a reflective member provided at a position where the light reflected on the liquid surface is irradiated from the measurement data;
    A program that causes a computer to execute a process of calculating a distance to the liquid surface based on the reflective member measurement data.
  11. 　請求項１０に記載のプログラムを格納した記憶媒体。 A storage medium storing the program according to claim 10.
PCT/JP2023/008529 2022-03-07 2023-03-07 Information processing device, distance measuring system, control method, program, and storage medium WO2023171659A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2022034472 2022-03-07
JP2022-034472 2022-03-07

Publications (1)

Publication Number Publication Date
WO2023171659A1 true WO2023171659A1 (en) 2023-09-14

Family

ID=87935102

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2023/008529 WO2023171659A1 (en) 2022-03-07 2023-03-07 Information processing device, distance measuring system, control method, program, and storage medium

Country Status (1)

Country Link
WO (1) WO2023171659A1 (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS4959666A (en) * 1972-07-26 1974-06-10
JPS62145180A (en) * 1985-12-16 1987-06-29 アクメ・クリーヴランド・コーポレイション Target determining device
JP2000258227A (en) * 1999-03-12 2000-09-22 Shinkawa Denki Kk Device for measuring displacement of surface in object with specular and nonspecular surface
JP2011174713A (en) * 2010-02-23 2011-09-08 Photonic Science Technology Inc Method and device for detection of water surface

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS4959666A (en) * 1972-07-26 1974-06-10
JPS62145180A (en) * 1985-12-16 1987-06-29 アクメ・クリーヴランド・コーポレイション Target determining device
JP2000258227A (en) * 1999-03-12 2000-09-22 Shinkawa Denki Kk Device for measuring displacement of surface in object with specular and nonspecular surface
JP2011174713A (en) * 2010-02-23 2011-09-08 Photonic Science Technology Inc Method and device for detection of water surface

Similar Documents

Publication Publication Date Title
US9453941B2 (en) Road surface reflectivity detection by lidar sensor
JP5267592B2 (en) Object recognition device
US7760336B2 (en) Laser area sensor
JP5540217B2 (en) Laser scan sensor
CN111742241A (en) Optical distance measuring device
JP4940458B2 (en) Object detection device
US20230065210A1 (en) Optical distance measuring device
JP5246430B2 (en) Obstacle detection method and apparatus
JP6785421B2 (en) Wave measuring device and target detection device
US11454718B2 (en) Vertical road profile estimation
CN113099120B (en) Depth information acquisition method and device, readable storage medium and depth camera
WO2023171659A1 (en) Information processing device, distance measuring system, control method, program, and storage medium
US20210003676A1 (en) System and method
JP3690260B2 (en) Vehicle distance measurement method
US20240159872A1 (en) Information processing device, information processing method, program, and recording medium
WO2023176640A1 (en) Information processing device, control method, program, and storage medium
WO2023152871A1 (en) Information processing device, control method, program, and storage medium
JP2023064482A (en) Information processor, control method, program, and storage medium
WO2023181947A1 (en) Object detection device and object detection method
JP2023143755A (en) Object detection device and object detection method
WO2023176639A1 (en) Information processing apparatus, control method, program, and storage medium
US20240103177A1 (en) Using glancing angle distance to segment lidar imagery
WO2023120430A1 (en) Information processing device, control method, program, and storage medium
US20210262794A1 (en) Ranging method and range finder
JP2023094057A (en) Information processor, control method, program, and storage medium

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23766835

Country of ref document: EP

Kind code of ref document: A1