Measurement device, measurement method, and measurement program

Abstract

To provide a measurement device, a measurement method, and a measurement program capable of selecting measurement data of a transmission object by a simple method.SOLUTION: A measurement device includes: an acquisition section for acquiring a plurality of pieces of measurement data of a measurement object in a liquid, which is acquired by a measurement device at a predetermined time interval; a calculation section for calculating an occupation ratio of the measurement object occupying in a measurement range of the measurement device from a position and a direction of the measurement device concerning the plurality of pieces of measurement data acquired by the acquisition section; a selection section for selecting the measurement data from the plurality of pieces of measurement data based on the occupation ratio; and a communication device for transmitting the measurement data selected by the selection section.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present invention relates to a measurement device, a measurement method, and a measurement program. [Background technology]

[0002] There is a demand for a technique for accurately measuring the shape of a measurement object in liquid (see, for example, Patent Documents 1 to 4). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] U.S. Patent Publication No. 2003-0206489 [Patent Document 2] International Publication No. 2014-192805 [Patent Document 3] Japanese Patent Publication No. 2020-204502 [Patent Document 4] U.S. Patent Publication No. 2012 / 0099400 Summary of the Invention [Problem to be solved by the invention]

[0004] There is a demand for technology that can transmit measurement data obtained by measuring submerged objects to high-speed processing computers on land, etc. Therefore, it is possible to selectively transmit only the measurement data that contains the most necessary information. However, methods that directly analyze the measurement data are computationally expensive.

[0005] In one aspect, the present invention aims to provide a measurement device, a measurement method, and a measurement program that enable measurement data to be transmitted to be selected using a simple method. [Means for solving the problem]

[0006] In one embodiment, the measurement device includes an acquisition unit that acquires multiple measurement data of a measurement object in a liquid obtained by the measurement device at a predetermined time interval; a calculation unit that calculates the occupancy rate of the measurement object in the measurement range of the measurement device based on the position and orientation of the measurement device for the multiple measurement data acquired by the acquisition unit; a selection unit that selects measurement data from the multiple measurement data based on the occupancy rate; and a communication device that transmits the measurement data selected by the selection unit. [Effects of the Invention]

[0007] Measurement data to be transmitted can be selected using a simple method. [Brief explanation of the drawings]

[0008] [Figure 1] 1A is a diagram illustrating the configuration of a measurement device according to a first embodiment, FIG. 1B is a functional block diagram of a calculation unit, and FIG. 1C is a block diagram illustrating the hardware configuration of the calculation unit. [Figure 2] FIG. 10 is a diagram illustrating a flowchart. [Figure 3] FIG. 2 is a diagram illustrating a CG model of a measurement object. [Figure 4] FIG. 10 is a diagram illustrating a CG model of a measurement object viewed from directly above. [Figure 5] FIG. 10 is a diagram illustrating an example of a capture range obtained from the position and orientation of a camera. [Figure 6] FIG. 10 is a diagram illustrating an example of an occupancy rate. [Figure 7] 10A is a diagram illustrating an example of the evaluation value Pr, FIG. 10B is a diagram illustrating an example of the evaluation value Pd, and FIG. 10C is a diagram illustrating an example of the evaluation value Pc. [Figure 8] 10A is a diagram illustrating an example of an evaluation value Pac, FIG. 10B is a diagram illustrating an example of an evaluation value Poc, and FIG. 10C is a diagram illustrating an example of an evaluation value Psc. [Figure 9] FIG. 10 is a diagram illustrating the configuration of a measurement device according to a second embodiment. [Figure 10] FIG. 10 is a diagram illustrating a flowchart. DETAILED DESCRIPTION OF THE INVENTION

[0009] Technologies are being developed to use cameras mounted on underwater ROVs (Remotely Operated Vehicles) and underwater LiDAR (Light Detection and Ranging) to perform high-resolution three-dimensional measurements of the shapes of structures in seawater, freshwater, and other liquids, as well as the shapes of the seabed and lake bottom, and to create 3D models.

[0010] However, model creation is a heavy process, and it is difficult to equip underwater ROVs with high-speed processing computers, so models are created by processing the data brought back by the underwater ROV on a high-speed processing computer on land.However, this method makes it difficult to create models in real time.

[0011] In underwater communications and communications from water to land, the bandwidth of the transmission path is narrower and more unstable than the bandwidth required for creating high-resolution models. Therefore, for real-time provision, data volume must be reduced (by compression or thinning at regular intervals), which creates a trade-off with quality.

[0012] One possible solution is to select only data with a large amount of information required for model creation (for example, measurement data that can be acquired with high resolution) and send it from the underwater ROV to a high-speed processing computer on land. However, since directly analyzing the measurement data for evaluation during selection and calculation of evaluation values ​​is computationally expensive, it is necessary to install a processor for image processing, etc. on the underwater ROV. Running algorithms such as object recognition during measurement data analysis requires power consumption of tens of watts, which increases the cost of the underwater ROV and reduces its operating time.

[0013] Therefore, in the following embodiment, an example in which measurement data can be selected using a simple method will be described. [Example]

[0014] Fig. 1(a) is a diagram showing the configuration of a measuring device 100 according to a first embodiment. As illustrated in Fig. 1(a), the measuring device 100 includes a sensor unit 10, a calculation unit 20, a communication device 30, etc. The sensor unit 10 includes a camera 11, a navigation device 12, etc. In this embodiment, the camera 11 is used as the measuring device. The camera 11 acquires image data at predetermined time intervals (frames / second).

[0015] Fig. 1(b) is a functional block diagram of the calculation unit 20. As illustrated in Fig. 1(b), the calculation unit 20 functions as an acquisition unit 21, an evaluation value calculation unit 22, a data selection unit 23, a data storage unit 24, and the like.

[0016] Fig. 1(c) is a block diagram illustrating an example of the hardware configuration of the calculation unit 20. As illustrated in Fig. 1(c), the calculation unit 20 includes a CPU 101, a RAM 102, a storage device 103, and the like.

[0017] The CPU (Central Processing Unit) 101 is a central processing unit. The CPU 101 includes one or more cores. The RAM (Random Access Memory) 102 is a volatile memory that temporarily stores programs executed by the CPU 101, data processed by the CPU 101, etc. The storage device 103 is a non-volatile storage device. For example, a ROM (Read Only Memory), a solid state drive (SSD) such as a flash memory, or a hard disk driven by a hard disk drive can be used as the storage device 103. The storage device 103 stores a measurement program. Each part of the calculation unit 20 is realized by the CPU 101 executing the measurement program. Note that each part of the calculation unit 20 may be realized using hardware such as a dedicated circuit.

[0018] An example of the operation of the measurement device 100 will now be described with reference to the flowchart of FIG.

[0019] 2, the acquisition unit 21 acquires image data for each frame captured by the camera 11 (step S1). For example, the acquisition unit 21 acquires image data for each frame at a rate of 30 frames per second.

[0020] Next, the acquisition unit 21 acquires the position and orientation of the camera 11 from the navigation device 12 in synchronization with the frame of the camera 11 (step S2). For example, by acquiring the difference in mounting position between the camera 11 and the navigation device 12, it becomes possible to acquire the spatial position of the camera 11. Furthermore, by installing a GPS in the measurement device 100, it is possible to calibrate the INS (Inertial Navigation System) of the navigation device 12.

[0021] The acquisition unit 21 also acquires prior information from the data storage unit 24 (step S3). For example, the acquisition unit 21 acquires information about the measurement object. Specifically, the acquisition unit 21 acquires the surface position coordinates of the shape of the measurement object from the installation position of the measurement object, design information about the measurement object, past 3D measurement information about the measurement object, etc. For example, FIG. 3 is a diagram illustrating an example of a CG model of the measurement object. Also, FIG. 4 illustrates an example of a CG model of the measurement object when viewed from directly above. The installation position of the measurement object can be acquired from a GPS map, etc., and the absolute position of the detailed design shape of the measurement object can be obtained by combining latitude and longitude information and model shape information.

[0022] Furthermore, for example, the acquisition unit 21 acquires, as the prior information, information about the camera 11. Specifically, the acquisition unit 21 acquires the angle of view of the camera 11, the number of pixels (horizontal and vertical) of the camera 11, the imaging resolution range of the camera 11, and the like.

[0023] Next, the evaluation value calculation unit 22 calculates the evaluation value P of the occupancy rate using the information acquired by the acquisition unit 21. a (Step S4). The occupancy rate is the occupancy rate (%) of the measurement object relative to the shooting range (number of pixels) of the camera 11. The higher the occupancy rate, the more information about the measurement object is included, and therefore the higher the quality of the acquired image.

[0024] First, the evaluation value calculation unit 22 calculates the shooting range (vector of each pixel) from the position, direction, angle of view, and number of pixels of the camera 11. The vector of each pixel is obtained by adding directions equally divided from the horizontal and vertical angle of view and the number of horizontal and vertical pixels, with the starting vector being the position and direction of the camera 11. Next, the evaluation value calculation unit 22 calculates an evaluation value P a Specifically, the evaluation value calculation unit 22 calculates the value of each pixel in the shooting range by adding "1" if the surface of the measurement object is measured (the surface of the object exists on the extension line of each pixel vector) and "0" if it is not measured, and then divides the result by the total number of pixels. Whether or not the surface of the measurement object is measured can be determined from the positional relationship between the camera 11 and the measurement object acquired by the acquisition unit 21. FIG. 5 is a diagram illustrating an example of the shooting range obtained from the position and orientation of the camera 11.

[0025] As shown in Fig. 6, the higher the occupancy rate, the higher the evaluation value P a For example, the evaluation value P a If the measurement target is captured in the entire shooting range (entire image), it is set to "1", and if it is not captured at all, it is set to "0". a is stored in the data storage unit 24. The evaluation value P a is calculated for the image data of each frame. a are stored in a ring buffer of, for example, 150 frames, and when the buffer is filled with 150 frames, the next step S5 is executed.

[0026] Next, the data selection unit 23 selects the evaluation values ​​P a For example, the data selection unit 23 selects image data of frames that satisfy a predetermined condition from the evaluation values ​​P a Alternatively, the data selection unit 23 selects image data of any frame that is equal to or greater than the threshold value from the evaluation values ​​P aThe image data of the frame with the maximum value is selected from the above.

[0027] Next, the communication device 30 transmits the image data selected by the data selection unit 23 to a ship or land (step S6).

[0028] After step S6 is executed, the process is executed again from step S1. In this case, when image data of a new frame is acquired, the data storage unit 24 updates the evaluation value P a Alternatively, the data storage unit 24 may discard image data of all frames and update the ring buffer when new image data is acquired.

[0029] The above-mentioned occupancy rate can be obtained simply by calculating the occupancy ratio of the measurement object relative to the shooting range, thereby reducing calculation costs. Specifically, image data can be selected simply by analyzing the position and orientation of the camera 11, without directly analyzing the image data. Therefore, according to this embodiment, evaluation during data selection and calculation of evaluation values ​​can be performed using a simple method. For example, processing can be performed using a low-speed, low-power processor on the order of several hundred milliwatts. Furthermore, high-quality data can be transmitted in real time from the underwater ROV to a high-speed processing computer on land, making it possible to provide high-quality 3D models in real time.

[0030] For example, it is possible to provide 3D shape data of objects in the ocean in real time. It is also possible to improve the measurement accuracy of 3D shape data of objects in the ocean. Furthermore, it is possible to construct a digital twin using 3D data generated by high-speed processing computers on land. In other words, measurement data can be used as data for constructing a digital twin that virtually reproduces the state of the ocean in real space. For example, the generated 3D data can be placed on a digital twin that virtually reproduces the state of the ocean in real space. For example, 3D data of pipes and coral reefs, which are structures installed underwater, can be placed on the digital twin. This makes it possible to create a digital twin that reproduces the 3D shape data of objects in the ocean.

[0031] In the above example, one image data item is transmitted from among the multiple image data items stored in the data storage unit 24, but this is not limiting. For example, the number of image data items to be transmitted may be varied depending on the communication status of the communication device 30. As an example, the number of image data items to be transmitted may be increased when the communication bandwidth of the communication device 30 becomes wider.

[0032] Also, in the above example, the evaluation value P a In the above, image data is selected using only the evaluation value, but the present invention is not limited to this. In step S4 described above, image data may be selected by further reflecting other evaluation values. Specific examples will be described below.

[0033] For example, the occupancy evaluation value P a In addition, the distance evaluation value P r , the evaluation value of the movement speed P d , and the evaluation value P of the incident light amount c The overall evaluation value may be calculated by reflecting at least one of the above. Details will be described below.

[0034] First, the distance evaluation value P rThe distance here refers to the distance from the camera 11 to the object to be measured. If the distance from the camera 11 to the object to be measured is within the range that satisfies the range of the imaging resolution (image resolution on the surface of the object to be measured), the quality of the image data will be high.

[0035] First, the evaluation value calculation unit 22 calculates the angle θ per pixel from the number of horizontal pixels and the horizontal angle of view of the camera 11, and divides the upper and lower limits of the imaging resolution by tan(θ) to calculate the distance from the camera 11 to the surface of the object to be measured. Next, the evaluation value calculation unit 22 calculates the distance evaluation value P r Specifically, the evaluation value calculation unit 22 calculates the distance from the camera 11 to the surface of the object to be measured, and sets "1" when the calculated distance is equal to or less than the lower threshold, and sets "0" when the calculated distance is equal to or greater than the upper threshold. The evaluation value calculation unit 22 sets the evaluation value linearly between the lower threshold and the upper threshold. As shown in FIG. 7(a), the evaluation value P r is "1" when the calculated distance is below the lower threshold, decreases linearly between the lower threshold and the upper threshold, and is "0" when the distance is above the upper threshold. For example, when the distance is 10m, and the camera has 1920px horizontal pixels, a horizontal angle of view of 60°, and a shooting resolution of 0.2cm to 1.0cm, the threshold range is 3.7m to 18m, and the evaluation value is 0.56.

[0036] Next, the moving speed evaluation value P d The movement speed here refers to the movement speed of the camera 11 between frames. If the movement speed is sufficiently slow, high-definition image data can be acquired, resulting in high-quality image data. On the other hand, if the movement speed is too fast, it becomes difficult to acquire high-definition image data, resulting in low-quality image data.

[0037] First, the evaluation value calculation unit 22 calculates the moving speed. Specifically, the evaluation value calculation unit 22 acquires the position and orientation of the camera 11 in the current frame and the previous frame. The evaluation value calculation unit 22 calculates the position of the measurement target surface on the extension of the optical axis of the camera 11, finds the difference between the previous and next frames, and calculates the moving speed between frames. Next, the evaluation value calculation unit 22 calculates the moving speed evaluation value P d Specifically, the evaluation value calculation unit 22 sets the lower limit threshold to the length of the resolution per pixel per frame, and sets values ​​below that to "1", sets the upper limit threshold to the length of the lower limit of the shooting resolution per frame, and sets values ​​above that to "0", and sets a linear evaluation value between the lower limit threshold and the upper limit threshold. As shown in FIG. 7(b), the evaluation value P d is "1" when the movement speed is below the lower threshold, decreases linearly between the lower threshold and the upper threshold, and is "0" when the movement speed is above the upper threshold. For example, if the distance to the object is 10 m and the movement speed is 3 cm / frame, the lower threshold is 0.54 cm and the upper threshold is 1 cm, so the evaluation value is "0."

[0038] Next, the evaluation value P c When the amount of incident light is sufficiently large, it is possible to acquire high-definition image data, resulting in high-quality image data. On the other hand, when the amount of incident light is small, it is difficult to acquire high-definition image data, resulting in low-quality image data.

[0039] First, the evaluation value calculation unit 22 estimates the amount of incident light. Specifically, the evaluation value calculation unit 22 estimates the amount of incident light based on the position (water depth) of the camera 11 and the sunlight illuminance (W / cm 2 ) and the attenuation coefficient (-m) due to turbidity, the incident light amount at the time of measurement is estimated. Next, the evaluation value calculation unit 22 calculates the evaluation value P cSpecifically, the evaluation value calculation unit 22 reads a value from a map that corresponds the relationship between the amount of incident light and the image data quality, which has been created in advance based on measurement conditions such as the exposure time of the camera 11, and sets values ​​below the lower limit threshold to "0" and values ​​above the upper limit threshold to "1." Between the lower limit and the upper limit, the amount of incident light in the map corresponds to a value obtained by normalizing the image data quality. As shown in FIG. 7(c), the evaluation value P c is "0" when the amount of incident light is below the lower threshold, increases as the amount of incident light increases between the lower threshold and the upper threshold, and is "1" when the amount of incident light is above the upper threshold.

[0040] Each evaluation value P obtained i (Evaluation value of occupancy P a , distance evaluation value P r , the evaluation value of the movement speed P d , the evaluation value of the incident light amount P c ) is stored in the data storage unit 24. Note that each evaluation value P i is calculated for the image data of each frame. i are stored in a ring buffer of, for example, 150 frames, and when the buffer is filled with 150 frames, the next step S5 is executed.

[0041] Next, in step S5 described above, the evaluation value calculation unit 22 calculates each evaluation value P i Overall evaluation value P all The weight for each evaluation value Pi may be calculated as W i Then, the overall evaluation value P all can be calculated as follows: i For example, the overall evaluation value P may be an equal value, or a specific evaluation value may be weighted more heavily. all is calculated for each of the 150 frames of the buffer. P all =W a P a +W r P r +W d P d +W c Pc

[0042] Next, in step S5 described above, the data selection unit 23 selects the overall evaluation value P all For example, the data selection unit 23 may select image data of frames that satisfy a predetermined condition from the evaluation values ​​P all Alternatively, the data selection unit 23 selects image data of any frame that is equal to or greater than the threshold value from the evaluation values ​​P all The image data of the frame with the maximum value is selected from the above.

[0043] As mentioned above, the evaluation value of the occupancy rate P a In addition, the distance evaluation value P r , the evaluation value of the movement speed P d , and the evaluation value P of the incident light amount c By calculating an overall evaluation value that reflects at least one of the above, it becomes possible to transmit more appropriate image data for creating a 3D model, etc.

[0044] In addition, depending on the communication bandwidth, each evaluation value P i Weight W i For example, the overall evaluation value P all When calculating the weight W, a map is referenced in which the weights of each item are pre-set for each communication band range. i For example, if the communication band is so narrow that it takes more than five seconds to transmit one image data, the occupancy weight W a Increase to "2" and set the weight of the appropriate distance range W r By reducing this to "0.5," the evaluation value of image data captured over a wide range is increased and it becomes easier to transmit, making it possible to create a 3D model even if the number of images transmitted per unit time is small.

[0045] The number of evaluation values ​​is not limited to the above. For example, the evaluation value P ac , the evaluation value of the overlap rate of the shooting range P oc, the evaluation value of the difference in optical axis angle P sc At least one of the above is used as the overall evaluation value P all The details will be explained below.

[0046] The increase rate of the imaging area is the increase rate of the imaging area relative to the surface area of ​​the entire object to be measured when the current frame is selected. If the increase rate of the imaging area is high, it is possible to obtain information on more areas of the object, and therefore it is expected that the quality of the data will improve. The evaluation value calculation unit 22 calculates the evaluation value P of the increase rate of the imaging area. ac is set to a value that increases as the area of ​​the imaging region increases. For example, the evaluation value calculation unit 22 normalizes the rate of increase in the area of ​​the imaging region by the maximum area calculated from the allowable resolution, thereby obtaining the evaluation value P ac In this case, as shown in FIG. 8(a), the evaluation value P ac takes a value between "0" and "1".

[0047] The overlap rate of the imaging range is the overlap rate of the imaging range of the current frame with the image data most recently selected by the data selection unit 23. If the overlap rate is high, it is easier to obtain corresponding points of information on each image, and therefore a more accurate 3D model of the object can be obtained. The evaluation value calculation unit 22 calculates the evaluation value P of the overlap rate of the imaging range. oc is set to a value that increases as the overlap rate of the imaging range increases. On the other hand, as the overlap rate increases, the redundancy of the data also increases, so an upper limit value and a lower limit value are set. For example, the evaluation value calculation unit 22 sets the lower limit threshold to about 60%, and sets "0" below that, and the upper limit threshold to about 80%, and sets "1" above that, and sets a linear evaluation value between the lower limit threshold and the upper limit threshold. As shown in the example of FIG. 8(b), the evaluation value P oc is "0" when the overlap rate is below the lower threshold, increases linearly between the lower threshold and the upper threshold, and is "1" when the overlap rate is above the upper threshold.

[0048] The difference in optical axis angle is the difference in the optical axis angle of the data of the closest shooting range within a specified range from the normal direction of the measurement target surface (optical axis - measurement target surface). It is expected that the closer this angle difference is to 90°, the higher the resolution of the image data of the measurement target can be obtained. The evaluation value calculation unit 22 calculates the evaluation value P of the difference in optical axis angle. sc is set high within a predetermined range. For example, as shown in FIG. 8(c), the evaluation value calculation unit 22 sets the lower limit threshold to about 45°, and any angle below that is set to "0", sets the upper limit threshold to about 135°, and any angle above that is set to "0", and sets the upper limit between the lower limit threshold and the upper limit threshold so that an angle of about 90° is set to "1".

[0049] Next, in step S5 described above, the evaluation value calculation unit 22 calculates each evaluation value P i Overall evaluation value P all The weight for each evaluation value Pi may be calculated as W i Then, the overall evaluation value P all can be calculated as follows: i For example, the overall evaluation value P may be an equal value, or a specific evaluation value may be weighted more heavily. all is calculated for each of the 150 frames of the buffer. P all =W a P a +W r P r +W d P d +W c P c +W ac P ac +W oc P oc +W sc P sc [Example]

[0050] FIG. 9 is a diagram illustrating the configuration of a measuring device 100a according to a second embodiment. As illustrated in FIG. 9, the measuring device 100a differs from the measuring device 100 according to the first embodiment in that a LiDAR device 13 is provided as a measuring device instead of the camera 11. The LiDAR device 13 emits emitted light at predetermined time intervals, scans the emitted light over a predetermined range including the measurement object by changing the angle of a scanning mirror, receives the light-receiving element's light-receiving result of the reflected light from the measurement object, and measures the time, thereby measuring the distance to each sample point. Therefore, the LiDAR device 13 can acquire ranging point cloud data of the scanning area for each frame.

[0051] An example of the operation of the measurement device 100a will now be described with reference to the flowchart of Fig. 10. For simplicity of explanation, only differences from the flowchart of Fig. 2 will be described.

[0052] 10, the acquisition unit 21 acquires the ranging point cloud data acquired by the LiDAR device 13 for each frame (step S11). For example, the acquisition unit 21 acquires the ranging point cloud data for each frame at a rate of 30 frames per second.

[0053] Next, the acquisition unit 21 acquires the position and orientation of the LiDAR device 13 from the navigation device 12 in synchronization with the frame of the LiDAR device 13 (step S12).

[0054] The acquisition unit 21 also acquires prior information from the data storage unit 24 (step S13). For example, the acquisition unit 21 acquires information about the LiDAR device 13 as the prior information. Specifically, the acquisition unit 21 acquires the deflection angle of the LiDAR device 13, the sampling number (horizontal and vertical) of the LiDAR device 13, and the like.

[0055] Next, the evaluation value calculation unit 22 calculates the evaluation value P of the occupancy rate using the information acquired by the acquisition unit 21. a(Step S14). The occupancy here is the occupancy in the first embodiment, where the angle of view of the camera 11 is replaced with the deflection angle of the LiDAR device 13, the pixels of the camera 11 are replaced with the sampling points of the LiDAR device 13, and the vector of each pixel is replaced with the scan vector of the LiDAR device, and is acquired as the occupancy of the measurement object in the measurement range in the ranging point cloud data acquired by the LiDAR device 13.

[0056] Next, the data selection unit 23 selects the evaluation values ​​P a From the above, the ranging point cloud data of a frame that satisfies a predetermined condition is selected (step S15).

[0057] Next, the communication device 30 transmits the ranging point cloud data selected by the data selection unit 23 to a ship or land (step S16). After step S16 is executed, the process is executed again from step S11.

[0058] In this embodiment, the occupancy rate can be obtained simply by calculating the occupancy rate of the measurement object relative to the distance measurement range, which reduces the calculation cost. Therefore, in this embodiment, evaluation during data selection and calculation of evaluation values ​​can be performed using a simple method.

[0059] In each of the above embodiments, the acquisition unit 21 is an example of an acquisition unit that acquires multiple measurement data of a measurement object in liquid acquired by a measurement device at predetermined time intervals. The evaluation value calculation unit 22 is an example of a calculation unit that calculates the occupancy rate of the measurement object in the measurement range of the measurement device based on the position and orientation of the measurement device for the multiple measurement data acquired by the acquisition unit. The data selection unit 23 is an example of a selection unit that selects measurement data from the multiple measurement data based on the occupancy rate. The communication device 30 is an example of a communication device that transmits the measurement data selected by the selection unit.

[0060] Although the embodiments of the present invention have been described in detail above, the present invention is not limited to such specific embodiments, and various modifications and variations are possible within the scope of the gist of the present invention as defined in the claims. (Appendix 1) an acquisition unit that acquires a plurality of measurement data of a measurement object in liquid, the measurement data being acquired by a measurement device at predetermined time intervals; a calculation unit that calculates an occupancy rate of the measurement object in a measurement range of the measurement device from the position and orientation of the measurement device for the plurality of measurement data acquired by the acquisition unit; a selection unit that selects measurement data from the plurality of measurement data based on the occupancy rate; a communication device that transmits the measurement data selected by the selection unit. (Appendix 2) The measurement device described in Appendix 1, characterized in that the selection unit selects measurement data from the multiple measurement data based on an overall evaluation value obtained from the occupancy rate and an evaluation value representing the quality of the measurement data. (Appendix 3) The measurement device described in Appendix 2, characterized in that the evaluation value includes at least one of an evaluation value of the distance between the measurement device and the object to be measured, an evaluation value of the movement speed of the measurement device between frames measured by the measurement device, an evaluation value of the amount of light incident on the measurement device in liquid, an evaluation value of the rate of increase in the area of ​​the measurement region of the object to be measured between frames measured by the measurement device, an evaluation value of the overlap rate of the measurement range with the measurement data selected by the selection unit, and an evaluation value of the angle formed between the optical axis of the measurement device and the surface of the object to be measured. (Appendix 4) The measurement device according to claim 2, wherein the selection unit calculates the overall evaluation value by weighting the occupancy rate and the evaluation value, respectively. (Appendix 5) The measurement device according to claim 4, wherein the selection unit changes the weight depending on the communication state of the communication device. (Appendix 6) The measurement device according to claim 1, wherein the selection unit varies the number of pieces of measurement data selected from the plurality of pieces of measurement data depending on the communication state of the communication device. (Appendix 7) The measuring device described in Appendix 1, wherein the plurality of measurement data are image data captured by a camera or ranging point cloud data measured by a ranging device. (Appendix 8) The computer A process of acquiring a plurality of measurement data of the measurement object in the liquid, the measurement data being acquired by the measurement device at predetermined time intervals; a process of calculating an occupancy rate of the measurement object in the measurement range of the measurement device from the position and orientation of the measurement device for the plurality of acquired measurement data; a process of selecting measurement data from the plurality of measurement data based on the occupancy rate; and transmitting the selected measurement data by a communication device. (Appendix 9) The measurement method described in Appendix 8, characterized in that the computer executes a process of selecting measurement data from the plurality of measurement data based on an overall evaluation value obtained from the occupancy rate and an evaluation value representing the quality of the measurement data. (Appendix 10) The measurement method described in Appendix 9, characterized in that the evaluation value includes at least one of an evaluation value of the distance between the measurement device and the object to be measured, an evaluation value of the movement speed of the measurement device between frames measured by the measurement device, an evaluation value of the amount of light incident on the measurement device in liquid, an evaluation value of the rate of increase in the area of ​​the measurement region of the object to be measured between frames measured by the measurement device, an evaluation value of the overlap rate of the measurement range with the measurement data selected by the selection unit, and an evaluation value of the angle formed between the optical axis of the measurement device and the surface of the object to be measured. (Appendix 11) The measurement method according to claim 9, wherein the computer performs a process of calculating the overall evaluation value by weighting the occupancy rate and the evaluation value, respectively. (Appendix 12) 12. The measurement method according to claim 11, wherein the computer executes a process of changing the weight depending on the communication state of the communication device. (Appendix 13) The measurement method according to claim 8, wherein the computer executes a process of varying the number of pieces of measurement data selected from the plurality of pieces of measurement data depending on the communication state of the communication device. (Appendix 14) The measurement method described in Appendix 8, wherein the plurality of measurement data are image data captured by a camera or ranging point cloud data measured by a ranging device. (Appendix 15) On the computer, A process of acquiring a plurality of measurement data of the measurement object in the liquid, the measurement data being acquired by the measurement device at predetermined time intervals; a process of calculating an occupancy rate of the measurement object in the measurement range of the measurement device from the position and orientation of the measurement device for the plurality of acquired measurement data; a process of selecting measurement data from the plurality of measurement data based on the occupancy rate; and transmitting the selected measurement data by a communication device. (Appendix 16) 16. The measurement program described in Appendix 15, characterized in that the computer is caused to execute a process of selecting measurement data from the plurality of measurement data based on an overall evaluation value obtained from the occupancy rate and an evaluation value representing the quality of the measurement data. (Appendix 17) The measurement program according to claim 16, characterized in that the evaluation value includes at least one of an evaluation value of the distance between the measurement device and the object to be measured, an evaluation value of the movement speed of the measurement device between frames measured by the measurement device, an evaluation value of the amount of light incident on the measurement device in liquid, an evaluation value of the rate of increase in the area of ​​the measurement region of the object to be measured between frames measured by the measurement device, an evaluation value of the overlap rate of the measurement range with the measurement data selected by the selection unit, and an evaluation value of the angle formed between the optical axis of the measurement device and the surface of the object to be measured. (Appendix 18) 17. The measurement program according to claim 16, wherein the program causes the computer to perform a process of calculating the overall evaluation value by weighting the occupancy rate and the evaluation value, respectively. (Appendix 19) 19. The measurement program according to claim 18, wherein the program causes the computer to execute a process of changing the weight depending on the communication state of the communication device. (Appendix 20) 16. The measurement program according to claim 15, wherein the computer is caused to execute a process of varying the number of pieces of measurement data selected from the plurality of pieces of measurement data depending on the communication state of the communication device. (Appendix 21) The measurement program according to claim 15, wherein the plurality of measurement data are image data captured by a camera or ranging point cloud data measured by a ranging device. [Explanation of symbols]

[0061] 10 Sensor section 11 Camera 12 Navigation equipment 13 LiDAR device 20 Arithmetic section 21 Acquisition Department 22 Evaluation value calculation unit 23 Data selection section 24 Data Storage Department 30 Communication equipment 100 Measuring Device 101 CPU 102 RAM 103 Storage device

Claims

1. an acquisition unit that acquires a plurality of measurement data of a measurement object in liquid, the measurement data being acquired by a measurement device at predetermined time intervals; a calculation unit that calculates an occupancy rate of the measurement object in a measurement range of the measurement device from the position and orientation of the measurement device for the plurality of measurement data acquired by the acquisition unit; a selection unit that selects measurement data from the plurality of measurement data based on the occupancy rate; a communication device that transmits the measurement data selected by the selection unit.

2. 2. The measurement device according to claim 1, wherein the selection unit selects measurement data from the plurality of measurement data based on an overall evaluation value obtained from the occupancy rate and an evaluation value representing the quality of the measurement data.

3. 3. The measuring device according to claim 2, wherein the evaluation value includes at least one of an evaluation value of a distance between the measuring device and the object to be measured, an evaluation value of a moving speed of the measuring device between frames measured by the measuring device, an evaluation value of an amount of light incident on the measuring device in liquid, an evaluation value of an increase rate of a measurement region area of ​​the object to be measured between frames measured by the measuring device, an evaluation value of an overlap rate of a measurement range with the measurement data selected by the selection unit, and an evaluation value of an angle formed between an optical axis of the measuring device and a surface of the object to be measured.

4. The measuring device according to claim 2 , wherein the selection unit calculates the overall evaluation value by weighting the occupancy rate and the evaluation value.

5. The measuring device according to claim 4 , wherein the selector changes the weight in accordance with a communication state of the communication device.

6. The measurement device according to claim 1 , wherein the selection unit varies the number of pieces of measurement data selected from the plurality of pieces of measurement data depending on the communication state of the communication device.

7. 2. The measuring device according to claim 1, wherein the plurality of measurement data are image data captured by a camera or distance measurement point cloud data measured by a distance measuring device.

8. The computer A process of acquiring a plurality of measurement data of the measurement object in the liquid, the measurement data being acquired by the measurement device at predetermined time intervals; a process of calculating an occupancy rate of the measurement object in the measurement range of the measurement device from the position and orientation of the measurement device for the plurality of acquired measurement data; a process of selecting measurement data from the plurality of measurement data based on the occupancy rate; and transmitting the selected measurement data.

9. On the computer, A process of acquiring a plurality of measurement data of the measurement object in the liquid, the measurement data being acquired by the measurement device at predetermined time intervals; a process of calculating an occupancy rate of the measurement object in the measurement range of the measurement device from the position and orientation of the measurement device for the plurality of acquired measurement data; a process of selecting measurement data from the plurality of measurement data based on the occupancy rate; and transmitting the selected measurement data.

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