Measurement system, measuring device, measurement method, and program

Abstract

The objective of the present disclosure is to provide a measurement system enabling detailed analysis using three-dimensional map data. A measurement system (10) according to the present disclosure comprises: a data generation unit (11) that generates three-dimensional map data combining point cloud data indicating the shape of a predetermined area and identification information for objects present in the predetermined area; an identification unit (12) that identifies the shape of each object included in the three-dimensional map data; a measurement unit (13) that measures the dimensions of an object of measurement designated for measurement from among the objects of which the shape has been identified; and an output unit (14) that outputs a measurement result.

Description

Technical Field

[0005] , , , ,

[0001] The present disclosure relates to a measurement system, a measuring device, a measuring method, and a non-transitory computer-readable medium.

Background Art

[0002] As a system for quickly and accurately acquiring information regarding a three-dimensional space, a MMS (Mobile Mapping System) is used. The MMS mounts a laser measuring instrument, a camera device, and an IMU (Inertial Measurement Unit) on a vehicle, and during the running of the vehicle, combines the point cloud data acquired by the laser measuring instrument with the image data acquired by the camera device and the data measured by the IMU to generate three-dimensional map data.

[0003] Further, Patent Document 1 discloses a configuration of a road object recognition device that recognizes a road object having a shape formed by laser point clouds using road object image data. Furthermore, Patent Document 2 discloses a configuration of a tree number calculation system that identifies tree species and measures distances between respective tree top points using a three-dimensional image including tree top points and tree crowns of trees.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0005] When 3D map data generated using MMS or the apparatus disclosed in Patent Document 1 is applied to the system disclosed in Patent Document 2, it is possible to identify tree species included in the 3D map data. On the other hand, there is a desire to perform more detailed analysis using the 3D map data and to smoothly carry out survey work in a predetermined area.

[0006] The purpose of this disclosure is to provide a measurement system, measuring device, measurement method, and non-temporary computer-readable medium that enable detailed analysis using three-dimensional map data. [Means for solving the problem]

[0007] A measurement system according to a first aspect of this disclosure includes: data generation means for generating three-dimensional map data by combining point cloud data indicating the shape of a predetermined region and identification information of objects present in the predetermined region; identification means for identifying the shape of each of the objects included in the three-dimensional map data; measurement means for measuring the dimensions of a measurement target designated as a measurement target among the objects whose shapes have been identified; and output means for outputting the measurement result.

[0008] A measuring device according to a second aspect of the present disclosure includes: identification means for identifying the shape of each object included in a three-dimensional map data which is a combination of point cloud data indicating the shape of a predetermined region and identification information of objects present in the predetermined region; measuring means for measuring the dimensions of an object to be measured from among the objects whose shapes have been identified and which has been designated as a measurement target; and output means for outputting the measurement result.

[0009] A measurement method according to a third aspect of this disclosure identifies the shape of each object included in a 3D map data that combines point cloud data indicating the shape of a predetermined region with identification information of objects present in the predetermined region, measures the dimensions of an object designated as a measurement target from among the objects whose shapes have been identified, and outputs the measurement result.

[0010] A program according to a fourth aspect of this disclosure causes a computer to identify the shape of each object included in a three-dimensional map data which is a combination of point cloud data indicating the shape of a predetermined region and identification information of objects present in the predetermined region, measure the dimensions of an object designated as a measurement target among the objects whose shapes have been identified, and output the measurement results. [Effects of the Invention]

[0011] This disclosure provides a measurement system, measuring device, measurement method, and non-temporary computer-readable medium that enable detailed analysis using 3D map data. [Brief explanation of the drawing]

[0012] [Figure 1] This is a diagram showing the configuration of the measurement system according to Embodiment 1. [Figure 2] This figure shows the flow of the measurement process performed in the measurement system according to Embodiment 1. [Figure 3] This is a diagram showing the configuration of the measuring device according to Embodiment 2. [Figure 4] This figure shows the processing flow for image data according to Embodiment 2. [Figure 5] This is a diagram illustrating the flow of the position estimation process according to Embodiment 2. [Figure 6] This figure shows the flow of the 3D map data generation process according to Embodiment 2. [Figure 7] This diagram shows the flow of the dimensional measurement process according to Embodiment 2. [Figure 8] This diagram shows the flow of the measurement process according to Embodiment 3. [Figure 9] These are configuration diagrams of the measuring devices according to each embodiment. [Modes for carrying out the invention]

[0013] (Embodiment 1) Embodiments of the present invention will be described below with reference to the drawings. An example of the configuration of a measurement system 10 according to Embodiment 1 will be described using Figure 1. The measurement system 10 includes a data generation unit 11, a identification unit 12, a measurement unit 13, and an output unit 14. The data generation unit 11, the identification unit 12, the measurement unit 13, and the output unit 14 may be located in a single physical computer device, or they may be distributed across two or more computer devices. When the data generation unit 11, the identification unit 12, the measurement unit 13, and the output unit 14 are distributed across two or more computer devices, the data generation unit 11, the identification unit 12, the measurement unit 13, and the output unit 14 may transfer data via a network. The data generation unit 11, the identification unit 12, the measurement unit 13, and the output unit 14 may be used as means for generating data, means for identifying data, means for measuring data, and means for outputting data, respectively.

[0014] The computer device may be a device that operates by a processor executing a program stored in memory. The data generation unit 11, the identification unit 12, the measurement unit 13, and the output unit 14 may be software or modules whose processing is performed by a processor executing a program stored in memory. Alternatively, the data generation unit 11, the identification unit 12, the measurement unit 13, and the output unit 14 may be hardware such as a circuit or chip.

[0015] The data generation unit 11 generates 3D map data by combining point cloud data indicating the shape of a predetermined area with identification information of objects present in the predetermined area. The predetermined area may be, for example, an area from which point cloud data can be acquired using a sensor or the like. Specifically, the predetermined area may be a closed space such as the interior of a building, or an open space such as the exterior of a building. In describing the embodiments of this disclosure, point cloud data is used as an example to describe the data indicating the shape of a predetermined area, but this is not intended to limit the embodiments to point cloud data, and other data representing shapes, such as mesh data generated from point cloud data, may also be used.

[0016] The shape of the predetermined area may be the shape of an object existing within the predetermined area. The object existing within the predetermined area may be, for example, an object having a shape such as a building, a vehicle, a person, a traffic signal, a plant, etc. Also, the object may be a moving object or a stationary object.

[0017] The point cloud data is a set of points having three-dimensional information. For example, each point may be represented using coordinates indicating a position in a three-dimensional space such as the surface or boundary (edge) of an object. The three-dimensional information may be acquired or detected using a three-dimensional sensor. Specifically, the three-dimensional sensor may be a LiDAR (Light Detection and Ranging or Laser Imaging Detection and Ranging), a laser scanner, etc. The coordinates indicating a position in the three-dimensional space may be represented using a coordinate system based on the position of the three-dimensional sensor. Or, the coordinates indicating a position in the three-dimensional space may be a coordinate system for indicating the position of an object in a space such as a world coordinate system or a local coordinate system. Or, the coordinates indicating a position in the three-dimensional space may be coordinates after being converted to a world coordinate system or the like using a predetermined conversion formula from a coordinate system based on the position of the three-dimensional sensor.

[0018] The identification information of the object may be, for example, information indicating the name of the object, or may be color information used to distinguish each object. The name of the object may be determined, for example, based on the shape of the point cloud data. The name of the object may be determined using a learning model that learns the shape of the point cloud data and outputs the name of the object indicated by the shape of the point cloud data, or the name recognized by an operator or the like who visually recognized the shape of the point cloud data may be input to the data generation unit 11.

[0019] 3D map data may be represented as image data. Alternatively, 3D map data may be image data in which different colors are applied to each object, making each object identifiable. Or, 3D map data may be distance image data or depth image data, etc., which uses the same color to indicate areas of the same distance. The same distance only needs to be substantially the same, and may be considered the same distance as long as it is within a predetermined margin of error. Furthermore, the image data representing the 3D map data may display the names of each object.

[0020] The identification unit 12 identifies the shape of each object included in the 3D map data. The identification unit 12 may, for example, divide or distinguish objects included in the 3D map data by determining that a group of adjacent points constitutes a point cloud representing the same object. The group of adjacent points may, for example, be a set of points located at positions where the distance between them is shorter than a predetermined distance. Alternatively, the identification unit 12 may identify a region within the 3D map data where a set of points assigned the same identification information exists as the shape of the same object.

[0021] The measuring unit 13 measures the dimensions of an object designated as a measurement target from among objects whose shape has been specified. The measurement target may be an object designated by, for example, a worker, and the measuring unit 13 may accept input of information identifying the object from the worker. For example, the worker may input information indicating the name of the object, and further input information indicating the measurement location within the measurement target object.

[0022] The dimensions of the object being measured may, for example, be information indicating the size of the object. Specifically, the dimensions of the object may be the height, width, major axis of the object's cross-section, minor axis of the object's cross-section, perimeter of the object's cross-section, etc.

[0023] The measuring unit 13 may, for example, measure dimensions using the positions of points included in the object to be measured. Alternatively, the measuring unit 13 may measure dimensions using the positions of points included in the object to be measured and a reference position. Specifically, the measuring unit 13 may specify multiple points to be used for measurement and measure the dimensions of the object to be measured using the coordinates of each point. Alternatively, the measuring unit 13 may use the ground as the reference position to measure the height of the object from the ground.

[0024] The output unit 14 outputs the measurement results. For example, the output unit 14 may be a display means such as a display that shows image data, or it may be a speaker that outputs the measurement results by sound or the like.

[0025] Next, the flow of the measurement process performed in the measurement system 10 will be explained using Figure 2. First, the data generation unit 11 generates 3D map data by combining point cloud data showing the shape of a predetermined area and identification information of objects present in the predetermined area (S11). Next, the identification unit 12 identifies the shape of each object included in the 3D map data (S12). Next, the measurement unit 13 measures the dimensions of the object designated as the measurement target from among the objects whose shapes have been identified (S13). Next, the output unit 14 outputs the measurement results (S14).

[0026] If the data generation unit 11 and the identification unit 12 are located on different computer devices, the data generation unit 11 transmits 3D map data to the identification unit 12 via the network. Also, if the identification unit 12 and the measurement unit 13 are located on different computer devices, the identification unit 12 transmits information about the identified object to the measurement unit 13 via the network. Furthermore, if the measurement unit 13 and the output unit 14 are located on different computer devices, the measurement unit 13 transmits information indicating the measurement results to the output unit 14 via the network.

[0027] As described above, the measurement system 10 according to Embodiment 1 measures the dimensions of objects included in 3D map data as an analysis using 3D data. Furthermore, workers investigating a predetermined area can recognize the measurement results indicating the dimensions. This makes it possible for workers to easily recognize the arrangement or size of objects within the predetermined area.

[0028] (Embodiment 2) Next, an example configuration of the measuring device 20 will be described using Figure 3. The measuring device 20 may be a computer device that operates by a processor executing a program stored in memory. The components of the measuring device 20 may be software or modules whose processing is performed by a processor executing a program stored in memory. Alternatively, the components of the measuring device 20 may be hardware such as circuits or chips.

[0029] The measuring device 20 has a color data measuring unit 21, a shape data measuring unit 23, and a movement amount data measuring unit 25. The measuring device 20 generates various types of data using the color data measuring unit 21, the shape data measuring unit 23, and the movement amount data measuring unit 25 while moving. Alternatively, the measuring device 20 may collect or acquire the generated data from the color data measuring unit 21, the shape data measuring unit 23, and the movement amount data measuring unit 25, which generate data while moving.

[0030] The color data measurement unit 21 may be an image sensor that generates image data. Specifically, the image sensor may be a camera device that generates RGB (Red Green Blue) colors. The color data measurement unit 21 may be located within the measuring device 20, or it may be attached as an external device to the measuring device 20. Alternatively, the color data measurement unit 21 may be installed at a location separate from the measuring device 20 and transmit image data to the measuring device 20 via a network.

[0031] The shape data measurement unit 23 may be a three-dimensional sensor that measures the shape of an object and the distance from the shape data measurement unit 23 to the object. The sensor that measures shape and distance may be, for example, a LiDAR. The shape data measurement unit 23 generates point cloud data that shows the shape of the object. The shape data measurement unit 23 may also associate time information with each point included in the point cloud data. Specifically, the shape data measurement unit 23 may associate the time at which each point was detected with each point. The shape data measurement unit 23 may be provided within the measuring device 20, or it may be attached as an external device to the measuring device 20. Alternatively, the shape data measurement unit 23 may be installed at a location separate from the measuring device 20 and transmit point cloud data to the measuring device 20 via a network.

[0032] The movement data measurement unit 25 may be a movement sensor that records how the movement data measurement unit 25 has moved. The movement sensor may be, for example, an inertial measurement unit (IMU). The inertial measurement unit may include, for example, an angular velocity sensor, an acceleration sensor, a magnetic sensor, etc. For example, the movement of the measuring device 20 equipped with the movement data measurement unit 25 may be measured using values ​​detected using the angular velocity sensor and the acceleration sensor. The movement may be the distance from a specific reference position, or the distance moved within a predetermined period. The shape data measurement unit 23 may associate time information with the data indicating the movement. Specifically, the shape data measurement unit 23 may associate the time at which the movement was measured with the data indicating the movement.

[0033] The measuring device 20 includes an object identification unit 22, a shape data synthesis unit 24, a position and direction measuring unit 26, a shape data identification unit 27, and a splitting unit 28 for processing various data generated in the color data measuring unit 21, the object identification unit 22, and the shape data measuring unit 23. Processing the data may include processes such as converting the data and correcting the data.

[0034] The object identification unit 22 uses the image data generated by the color data measurement unit 21 to identify objects contained within the image data. For example, the object identification unit 22 may perform semantic segmentation to identify objects contained within the image data. In semantic segmentation, for example, image recognition using deep learning may be performed. A convolutional neural network may be used as the deep learning method. Specifically, the object identification unit 22 may assign an identifier that identifies an object, or a category of the object, to all pixels contained in the image data. The identifier may also be called a label. The object identification unit 22 may assign the same color to pixels that represent the same object or the same category.

[0035] The position and direction measuring unit 26 uses data indicating the amount of movement of the measuring device 20 to estimate the direction of movement of the measuring device 20, the orientation of the measuring device 20, and the position at the time the amount of movement was measured. The position and direction measuring unit 26 uses multiple sensor data held by the amount of movement data measuring unit 25 to estimate the direction of movement, orientation, position, etc.

[0036] The shape data synthesis unit 24 synthesizes point cloud data generated in the shape data measurement unit 23 using time information associated with the direction of movement, orientation, position, etc., estimated in the position and orientation measurement unit 26. Specifically, the shape data synthesis unit 24 identifies points associated with time information that is substantially the same as the time information associated with the direction of movement, orientation, position, etc., estimated in the position and orientation measurement unit 26. Points associated with time information are points included in the point cloud data. The points identified using the time information are rotated and translated using the direction of movement, orientation, position, etc., estimated in the position and orientation measurement unit 26 to generate 3D data showing the three-dimensional shape.

[0037] The shape data identification unit 27 associates the coordinate values ​​in three-dimensional space between the pixels assigned identifiers, generated by the object identification unit 22, and the three-dimensional data generated by the shape data synthesis unit 24. For example, the physical mounting positions of the shape data measurement unit 23 and the displacement data measurement unit 25 are predetermined. As a result, the shape data identification unit 27 may recognize or maintain in advance the correspondence between the measurement locations measured by the shape data measurement unit 23 and which pixels included in the image data generated by the color data measurement unit 21 correspond to. The measurement locations measured by the shape data measurement unit 23 may also be individual points included in the point cloud data.

[0038] Alternatively, the shape data identification unit 27 may match the feature points extracted from the point cloud data generated by the shape data measurement unit 23 and the image data generated by the color data measurement unit 21, and associate the pixels and measurement locations that have the matching feature points.

[0039] The shape data identification unit 27 generates 3D map data with color data by associating the coordinate values ​​in 3D space between the pixels to which identifiers have been assigned and the 3D data generated in the shape data synthesis unit 24.

[0040] The division unit 28 divides the objects contained in the 3D map data by grouping together pixels with the same identifier within a predetermined area in the 3D map data. In other words, the division unit 28 recognizes pixels with the same identifier that are in the vicinity as a single object. As a result, the division unit 28 can distinguish and recognize, for example, people, plants, buildings, etc., and furthermore, it can distinguish individual people, plants, buildings, etc. of the same type.

[0041] The dimension measurement target designation unit 29 designates the object to be measured. The dimension measurement target designation unit 29 may accept input of information specifying the object from, for example, the administrator of the measuring device 20. For example, the dimension measurement target designation unit 29 may specify an identifier assigned by the object identification unit 22. Specifically, the dimension measurement target designation unit 29 may specify the type of wood to be measured, a building, a road, etc. Alternatively, the dimension measurement target designation unit 29 may designate individual objects identified in the division unit 28 as objects to be measured.

[0042] The measurement location designation unit 30 designates the measurement location on the object specified in the dimension measurement target designation unit 29. The measurement location designation unit 30 may also accept input of information specifying the measurement location from, for example, the administrator of the measuring device 20. The measurement location may be, for example, the diameter, major axis, or minor axis of the cross-section at a specific location on the object. Alternatively, the measurement location may be the perimeter of the cross-section at a specific location on the object. Alternatively, the measurement location may be the height, length, or width of the object.

[0043] The dimension measuring unit 31 measures the measurement points specified in the measurement point specification unit 30 of the object specified in the dimension measuring target specification unit 29. The dimension measuring unit 31 may measure the dimensions using, for example, the coordinates indicated by the point cloud data at the location specified as the measurement point. Alternatively, if the dimension measuring unit 31 is specified to measure the height of the object, it may measure the dimension from the surface to which the lower end of the object is in contact with the upper end of the object. In other words, the dimension measuring unit 31 may measure the dimensions using the coordinates indicating the surface and the coordinates of the point cloud data at the upper end of the object. The surface to which the lower end of the object is in contact may be, for example, the ground, the surface of another object, etc.

[0044] Alternatively, if the dimension measuring unit 31 is specified as a measurement point for a dimension relating to the cross-section of a predetermined position of the object, it may use point cloud data of the cross-section to fit a circular shape to the cross-section and measure the diameter, circumference, area, etc. of the fitted circular shape.

[0045] The output unit 32 outputs the measurement results measured by the dimension measuring unit 31. Specifically, the output unit 32 may display the measurement results. Alternatively, the output unit 32 may transmit the measurement results to a terminal or other device held by a user who wishes to confirm the measurement results.

[0046] Next, Figure 4 will be used to explain the processing flow for image data according to Embodiment 2. First, the color data measurement unit 21 acquires image data (S21). For example, the color data measurement unit 21 acquires image data using a camera capable of acquiring RGB data. Next, the object identification unit 22 identifies the object contained in the image data (S22). For example, the object identification unit 22 may perform semantic segmentation and assign an identifier to identify the object to all pixels contained in the image data. The color data measurement unit 21 may be mounted on a moving measuring device 20 and acquire image data periodically. Therefore, steps S21 and S22 may be repeated over a predetermined period. In addition, information regarding the time the image data was acquired may be associated with the image data acquired by the color data measurement unit 21.

[0047] Next, the flow of the position estimation process of the measuring device 20 according to Embodiment 2 will be explained using Figure 5. First, the movement amount data measurement unit 25 acquires movement amount data (S31). For example, the movement amount data measurement unit 25 may use an inertial measurement unit to acquire data indicating the amount of movement that the measuring device 20 on which the movement amount data measurement unit 25 is mounted has made.

[0048] Next, the position and direction measuring unit 26 uses data indicating the amount of movement of the measuring device 20 to estimate the direction of movement of the measuring device 20, the orientation of the measuring device 20, and the position at the time the amount of movement was measured (S32). The amount of movement data measuring unit 25 may be mounted on the moving measuring device 20 and periodically acquire the amount of movement data. Therefore, steps S31 and S32 may be repeated over a predetermined period. In addition, the data indicating the amount of movement acquired by the amount of movement data measuring unit 25 may be associated with information about the time the data indicating the amount of movement was acquired.

[0049] Next, the flow of the 3D map data generation process according to Embodiment 2 will be explained using Figure 6. First, the shape data measurement unit 23 acquires point cloud data indicating the shape of an object (S41). The shape data measurement unit 23 may acquire point cloud data using a LiDAR, which is a three-dimensional sensor. The shape data measurement unit 23 acquires point cloud data periodically over a predetermined period. The shape data measurement unit 23 may associate measured time information with each point cloud data. The shape data measurement unit 23 may acquire point cloud data periodically. More specifically, the shape data measurement unit 23 may acquire point cloud data periodically while the measuring device 20 is moving.

[0050] Next, the shape data synthesis unit 24 generates three-dimensional data that shows the object in three dimensions within a predetermined area, using the direction of movement of the measuring device 20, the orientation of the measuring device 20, and the position at which the amount of movement was measured, as estimated by the movement amount data measurement unit 25 (S42).

[0051] Next, the shape data identification unit 27 generates 3D map data using the 3D data generated in the shape data synthesis unit 24 and image data including pixels to which identifiers have been assigned (S43). The shape data identification unit 27 may also generate colored 3D map data by combining the color data included in the image data with the 3D data.

[0052] Next, the division unit 28 identifies the shape of each object included in the 3D map data (S44). The division unit 28 groups pixels with the same identifier that are in the vicinity and recognizes them as a single object. This makes it possible for the division unit 28 to distinguish and recognize, for example, people, plants, buildings, etc., and furthermore, to distinguish individual plants, etc., even if they are of the same species.

[0053] Next, the flow of the dimensional measurement process according to Embodiment 2 will be explained using Figure 7. First, the dimensional measurement target designation unit 29 receives information specifying the object to be measured (S51). For example, the dimensional measurement target designation unit 29 receives input of information specifying the object from the administrator of the measuring device 20. The administrator may input the information specifying the object using an input device such as a touch panel or keyboard.

[0054] Next, the measurement location designation unit 30 receives information specifying the measurement locations of the object to be measured (S52). For example, the measurement location designation unit 30 receives information specifying the measurement locations of the object from the administrator of the measuring device 20 or the like.

[0055] Next, the dimension measuring unit 31 measures the dimensions of the object designated as the object to be measured, specifically the measurement points designated as the object to be measured (S53). Then, the output unit 32 outputs the measurement results of the dimensions measured by the dimension measuring unit 31 (S54).

[0056] As described above, the measuring device 20 according to Embodiment 2 measures the dimensions of any location specified by the administrator or other person for an object included in 3D map data. This enables detailed analysis using the dimensions of each object in a predetermined area.

[0057] For example, by using the measuring device 20 when conducting a forest tree survey, workers do not need to measure the diameter at breast height of each individual tree on-site, and the diameter at breast height of each tree can be easily measured. The tree survey may also be called a tree-by-tree survey.

[0058] (Embodiment 3) Next, the measurement process flow according to Embodiment 3 will be explained using Figure 8. In Figure 8, the process flow for verifying the accuracy of the various data acquired by the color data measurement unit 21, the shape data measurement unit 23, and the displacement data measurement unit 25 will be explained.

[0059] In this embodiment, the color data measurement unit 21 acquires the time at which the color data was acquired (a time synchronized with the shape data measurement unit 23 and the movement amount data measurement unit 25) along with the color data. Using this time information and position information estimated by the position and direction measurement unit 26, which can be considered to be the same time, the shape data identification unit 27 identifies the distance from the color data measurement unit 21 to the object at the time the color data of the object included in the 3D map data was acquired (S61). The 3D map data includes point cloud data and has distance information to each object. The shape data identification unit 27 may, for example, identify the distance information of any point among the point cloud data included in the 3D map data. Any point may, for example, be a point included in each of the regions when the 3D map data is divided into multiple regions.

[0060] Next, the shape data identification unit 27 identifies the measurement error using the distance to the object and the number of pixels in the image data (S62). For example, the shape data identification unit 27 uses the sensor size and focal length of the camera that generates the image data and the distance to any point included in the point cloud data to calculate how much of the actual length of a predetermined area the width and height of the image data corresponds to. Furthermore, the shape data identification unit 27 uses the number of pixels in the image data to calculate how much of the actual length of a predetermined area each pixel constituting the image data corresponds to. In other words, the shape data identification unit 27 calculates how many meters in reality each pixel corresponds to. In this case, the longer the distance to the object, the longer the actual length represented by one pixel.

[0061] The shape data identification unit 27 considers the actual length represented by one pixel as the measurement error and compares the measurement error with a threshold (S63). For example, if the actual length represented by one pixel is longer than the threshold, the shape data identification unit 27 designates the area near the object as an incomplete measurement area (S64). Also, if the actual length represented by one pixel is shorter than the threshold, the shape data identification unit 27 designates the area near the object as a completed measurement area (S65). If the actual length represented by one pixel is longer than the threshold, it means that the object was photographed from a greater distance. In this case, if a measurement point is specified to measure the dimensions, the measurement error will be larger. Therefore, if the actual length represented by one pixel is longer than the threshold, the area near the object may be designated as an incomplete measurement area, prompting the user to remeasure the area near the object. For example, the shape data identification unit 27 may output map data showing the completed measurement area and the incomplete measurement area from the output unit 32.

[0062] Alternatively, the shape data identification unit 27 may determine the incomplete measurement area and the completed measurement area using the measurement errors provided as specifications for the image sensor used as the color data measurement unit 21 and the three-dimensional sensor used as the shape data measurement unit 23. In this case, the measurement error represents, for example, the error relating to the size of the object at a predetermined distance from the sensor. If the measurement errors of the specifications at a predetermined distance differ for the image sensor and the three-dimensional sensor, the completed measurement area and the incomplete measurement area may be determined based on the distance at which the measurement error of the less accurate sensor becomes smaller than a predetermined value. Alternatively, if the specifications of the measurement error at a predetermined distance differ for the image sensor and the three-dimensional sensor, data may be acquired using each sensor, and measurement may be determined to be complete when all areas become completed measurement areas for both sensors.

[0063] The dimension measuring unit 31 uses 3D map data relating to the measurement completion area to measure the dimensions of a specified measurement point on a specified object.

[0064] As described above, by performing the measurement process according to Embodiment 3, it is possible to generate data with smaller measurement errors so that errors are reduced when measuring dimensions. Here, a configuration in which measurement errors are kept below a threshold using 3D map data has been described, but this may also be done during measurement. For example, the measurement range may be set in advance using a GIS (Geographic Information System), and the measurement device 20 may acquire positional information in a coordinate system that can be linked with the GIS using GNSS, etc., so that it can understand its own position. Then, each time the color data measurement unit 21 acquires color data, the shape data measurement unit 23 uses the shape data (a collection of distance information to the object) acquired to acquire distance information to the object indicated by each pixel of the color data. From this distance information, the sensor size and focal length of the color data measurement unit 21, and the number of pixels, the actual number of meters that one pixel corresponds to may be calculated, and the measurement may be determined to be complete or incomplete based on a comparison with a threshold. Alternatively, the measuring device 20 may determine its own position using SLAM (Simultaneous Localization And Mapping) technology and reference point information that can be linked with GIS.

[0065] Figure 9 is a block diagram showing an example configuration of the measuring device 20 described in the above-described embodiment. Referring to Figure 9, the measuring device 20 includes a network interface 1201, a processor 1202, and memory 1203. The network interface 1201 may be used to communicate with a network node. The network interface 1201 may include, for example, a network interface card (NIC) compliant with the IEEE 802.3 series. IEEE stands for Institute of Electrical and Electronics Engineers.

[0066] The processor 1202 reads and executes software (computer programs) from the memory 1203 to perform the processing of the measuring device 20 as described using a flowchart in the above embodiment. The processor 1202 may be, for example, a microprocessor, an MPU, or a CPU. The processor 1202 may include multiple processors.

[0067] Memory 1203 is composed of a combination of volatile and non-volatile memory. Memory 1203 may also include storage located away from the processor 1202. In this case, the processor 1202 may access memory 1203 via an I / O (Input / Output) interface, which is not shown.

[0068] In the example shown in Figure 9, memory 1203 is used to store a group of software modules. The processor 1202 can read these software modules from memory 1203 and execute them, thereby enabling the measurement device 20 to perform the processing described in the above embodiment.

[0069] As explained with reference to Figure 9, each of the processors in the measuring device 20 in the above-described embodiment executes one or more programs that include a set of instructions for causing a computer to perform the algorithm described with reference to the drawings.

[0070] In the examples described above, the program includes a set of instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more of the functions described in the embodiments. The program may be stored on a non-temporary computer-readable medium or a physical storage medium. Examples, but not limited to, include random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technologies, CD-ROM, digital versatile disc (DVD), Blu-ray® disc or other optical disc storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices. The program may be transmitted over a temporary computer-readable medium or a communication medium. Examples, but not limited to, include temporary computer-readable medium or a communication medium that includes electrically, optically, acoustically or otherwise propagating signals.

[0071] Furthermore, the technical concepts in this disclosure are not limited to the embodiments described above, and may be modified as appropriate without departing from the spirit of the invention.

[0072] Some or all of the above embodiments may also be described as follows, but are not limited to the following:

[0073] (Note 1) A data generation means for generating 3D map data by combining point cloud data showing the shape of a predetermined region and identification information of objects present in the predetermined region, A means for identifying the shape of each of the objects included in the three-dimensional map data, A measuring means for measuring the dimensions of an object designated as a measurement target from among the objects whose shape has been identified, A measurement system comprising an output means for outputting measurement results. (Note 2) The aforementioned measuring means is The measurement system described in Appendix 1, which measures the dimensions of a measurement target area of ​​the measurement target, which is specified together with the measurement target object. (Note 3) The aforementioned measuring means is The measurement system described in Appendix 1, which measures the height of the object to be measured or the width of the object at a predetermined height. (Note 4) The aforementioned specifying means is, A measurement system according to any one of the appendices 1 to 3, which associates the aforementioned object with label information indicating the aforementioned object. (Note 5) The aforementioned measuring means is A measurement system as described in Appendix 4, for measuring the dimensions of an object associated with the specified label information. (Note 6) The system further includes an image sensor that generates image data of the aforementioned object, The data generation means is A measurement system according to any one of the appendices 1 to 5, which generates the three-dimensional map data using the image data in which the measurement error of the size of the object falls within a predetermined range. (Note 7) The system further comprises a three-dimensional sensor that generates point cloud data including distance information from the image sensor to the object, The data generation means is The measurement system according to Appendix 6, which uses the distance to the object to select the point cloud data and the image data to be used to generate the three-dimensional map data. (Note 8) The data generation means is The measurement system according to Appendix 7, which calculates the length corresponding to one pixel using the distance information and the number of pixels in the image data generated by the image sensor, and generates the three-dimensional map data using the image data and point cloud data generated at a distance where the length corresponding to one pixel is shorter than a predetermined length. (Note 9) A means for identifying the shape of each object included in a 3D map data that combines point cloud data showing the shape of a predetermined region and identification information of objects present in the predetermined region, A measuring means for measuring the dimensions of an object designated as a measurement target from among the objects whose shape has been identified, A measuring device comprising an output means for outputting measurement results. (Note 10) The aforementioned measuring means is A measuring device as described in Appendix 9, which measures the dimensions of a portion of the object to be measured, as specified together with the object to be measured. (Note 11) The shape of each object included in the 3D map data, which is a combination of point cloud data showing the shape of a predetermined region and identification information of objects present in the predetermined region, is identified. Of the objects whose shape has been identified, the dimensions of the object designated as the object to be measured are measured. A measurement method that outputs measurement results. (Note 12) The shape of each object included in the 3D map data, which is a combination of point cloud data showing the shape of a predetermined region and identification information of objects present in the predetermined region, is identified. Of the objects whose shape has been identified, the dimensions of the object designated as the object to be measured are measured. A non-temporary, computer-readable medium containing a program that causes a computer to output measurement results. [Explanation of Symbols]

[0074] 10 Measurement Systems 11 Data Generation Unit 12 Specific section 13 Measuring part 14 Output section 20 Measuring devices 21 Color data measurement unit 22 Object Identification Unit 23 Shape data measurement unit 24 Shape Data Synthesis Unit 25 Movement Data Measurement Unit 26 Position and direction measuring unit 27 Shape data identification unit 28 Division 29. Designated section for dimension measurement. 30. Designated measurement points 31 Dimensional measuring section 32 Output section

Claims

1. A data generation means for generating three-dimensional map data by combining point cloud data showing the shape of a predetermined region and identification information of objects present in the predetermined region, A means for identifying the shape of each of the objects included in the three-dimensional map data, A measuring means for measuring the dimensions of an object designated as a measurement target from among the objects whose shape has been identified, It includes an output means for outputting measurement results, The aforementioned specifying means is, The object and the label information indicating the object are associated. Measurement system.

2. The aforementioned measuring means is The measurement system according to claim 1, which measures the dimensions of an object associated with the specified label information.

3. The system further includes an image sensor that generates image data of the aforementioned object, The data generation means is The measurement system according to claim 1 or 2, wherein the three-dimensional map data is generated using the image data such that the measurement error of the size of the object falls within a predetermined range.

4. The system further comprises a three-dimensional sensor that generates point cloud data including distance information from the image sensor to the object, The data generation means is The measurement system according to claim 3, wherein the distance to the object is used to select the point cloud data and the image data used to generate the three-dimensional map data.

5. The data generation means is The measurement system according to claim 4, comprising: calculating the length corresponding to one pixel using the distance information and the number of pixels in the image data generated by the image sensor; and generating the three-dimensional map data using the image data and point cloud data generated at a distance where the length corresponding to one pixel is shorter than a predetermined length.

6. A means for identifying the shape of each object included in a 3D map data that combines point cloud data showing the shape of a predetermined region and identification information of objects present in the predetermined region, A measuring means for measuring the dimensions of an object designated as a measurement target from among the objects whose shape has been identified, An output means for outputting measurement results, Equipped with, The aforementioned specifying means is, The object and the label information indicating the object are associated. Measuring device.

7. The aforementioned measuring means is The measuring device according to claim 6, which measures the dimensions of a portion of the object to be measured that is specified together with the object to be measured.

8. The shape of each object included in the 3D map data, which is a combination of point cloud data showing the shape of a predetermined region and identification information of objects present in the predetermined region, is identified. Of the objects whose shape has been identified, the dimensions of the object designated as the object to be measured are measured. Output the measurement results, The aforementioned object is associated with label information that identifies the object. Measurement method.

9. The shape of each object included in the 3D map data, which is a combination of point cloud data showing the shape of a predetermined region and identification information of objects present in the predetermined region, is identified. Of the objects whose shape has been identified, the dimensions of the object designated as the object to be measured are measured. Output the measurement results, The aforementioned object is associated with label information that identifies the object. A program that causes a computer to perform a task.

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