Obstacle detection device, obstacle detection method, and program

Abstract

An obstacle detection device (2000) acquires: a three-dimensional map (40) indicating the three-dimensional positions of a plurality of places of a facility (10); and position information (50) indicating the position of a user (20). The obstacle detection device (2000), by using the three-dimensional map (40) and the position information (50), determines, from a region around the position of the user (20) in the three-dimensional map (40), a detection range in which an obstacle region is to be detected. The obstacle detection device (2000) detects the obstacle region in the detection range.

Description

[Technical field]

[0001] The present disclosure relates to a technique for detecting an obstacle. [Background technology]

[0002] Technologies have been developed to detect obstacles using sensors. For example, Patent Document 1 discloses a technology that moves a self-propelled traveling device around a residence, identifies obstacles to walking within the residence, and allows the manager or the like to grasp each obstacle in the residence. This allows the manager or the like to improve the arrangement of furniture and other items in the residence. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2016-192040 A Summary of the Invention [Problem to be solved by the invention]

[0004] The invention of Patent Document 1 is premised on the premise that after identifying walking obstacles in a residence, the manager or the like will view the information. The present invention has been made in consideration of such problems, and one of its objectives is to provide a new technology for detecting obstacles. [Means for solving the problem]

[0005] The obstacle detection device of the present disclosure includes an acquisition unit that acquires a 3D map showing the 3D position of each of multiple locations in a facility and location information showing the user's location, a detection range determination unit that uses the 3D map and the location information to determine a detection range in which to detect an obstacle area from an area surrounding the user's location on the 3D map, and a detection unit that detects the obstacle area from the detection range.

[0006] The obstacle detection method disclosed herein is executed by a computer and includes an acquisition step of acquiring a 3D map showing the 3D positions of a plurality of points in a facility and position information showing a user's position, a detection range determination step of determining a detection range in which an obstacle area is to be detected from an area around the user's position on the 3D map using the 3D map and the position information, and a detection step of detecting the obstacle area from the detection range.

[0007] The computer-readable medium of the present disclosure stores a program that causes a computer to execute the obstacle detection method of the present disclosure. Effect of the Invention

[0008] According to the present disclosure, a new technique for detecting an obstacle is provided. [Brief description of the drawings]

[0009] [Figure 1] 3 is a diagram illustrating an example of an outline of the operation of the obstacle detection device of the first embodiment. FIG. [Diagram 2] 1 is a block diagram illustrating a functional configuration of an obstacle detection device according to a first embodiment. [Diagram 3] FIG. 2 is a block diagram illustrating a hardware configuration of a computer that realizes the obstacle detection device. [Figure 4] 4 is a flowchart illustrating a flow of processing executed by the obstacle detection device of the first embodiment. [Diagram 5] FIG. 1 is a first diagram illustrating a detection range. [Figure 6] FIG. 2 is a second diagram illustrating the detection range. [Figure 7] FIG. 3 is a third diagram illustrating the detection range. [Figure 8] 1 is a block diagram illustrating a functional configuration of an obstacle detection device having an output unit. [Figure 9] FIG. 11 is a first diagram illustrating output information. [Figure 10] FIG. 2 is a second diagram illustrating output information. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment of the present disclosure will be described in detail with reference to the drawings. In each drawing, the same or corresponding elements are given the same reference numerals, and duplicated explanations are omitted as necessary for clarity of explanation. Furthermore, unless otherwise specified, predetermined values ​​such as predetermined values ​​and threshold values ​​are stored in advance in a storage device accessible from a device that uses the values. Furthermore, unless otherwise specified, the storage unit is composed of one or more arbitrary number of storage devices.

[0011] [Embodiment 1] <Summary> Fig. 1 is a diagram illustrating an example of an outline of the operation of the obstacle detection device 2000 of the embodiment 1. Here, Fig. 1 is a diagram for facilitating understanding of the outline of the obstacle detection device 2000, and the operation of the obstacle detection device 2000 is not limited to that shown in Fig. 1.

[0012] The obstacle detection device 2000 detects an obstacle 30 that impedes the movement of the user 20 when the user 20 moves through the facility 10. The facility 10 may be an outdoor facility or an indoor facility. An outdoor facility 10 is, for example, a substation. An indoor facility 10 is, for example, the inside of a factory. The user 20 is a person who uses the obstacle detection device 2000 in the facility 10, for example, a worker performing work in the facility 10.

[0013] In order to detect the obstacle 30, the obstacle detection device 2000 uses a 3D map 40, which is data representing a 3D map of the facility 10. The 3D map 40 represents the 3D positions (3D coordinates) of each of a plurality of locations in the facility 10. The 3D map 40 is realized, for example, by point cloud data representing the 3D positions of the plurality of locations in the facility 10. The point cloud data has point data for each of the plurality of locations. The point data indicates the 3D coordinates of the corresponding location. The point cloud data is obtained, for example, by scanning the plurality of locations in the facility 10 using LiDAR (light detection and ranging) or a depth camera. However, as described later, the 3D map 40 is not limited to point cloud data.

[0014] Furthermore, the obstacle detection device 2000 acquires position information 50 indicating the position of the user 20. For example, the position information 50 indicates the position of a terminal carried by the user 20 (hereinafter, referred to as a user terminal). The user terminal is, for example, a smartphone, a tablet terminal, or a wearable terminal (such as a glasses-type terminal or a watch-type terminal). However, as will be described later, the position indicated by the position information 50 is not limited to the position of the user terminal.

[0015] The obstacle detection device 2000 detects an obstacle area, which is a three-dimensional area representing an obstacle 30, from a predetermined range (hereinafter, detection range) around the user 20 on the three-dimensional map 40. To this end, the obstacle detection device 2000 determines the detection range based on the position of the user 20 represented by the position information 50. Then, the obstacle detection device 2000 detects the obstacle area from within the determined detection range.

[0016] <Examples of effects> In facilities such as substations and factories, it is undesirable for workers to get injured by tripping over or bumping into obstacles. For example, the number of such injuries can be used as an evaluation index for a company regarding the safety of its work. In addition, if a worker is unable to work for a while due to injury, human resources will decrease. Therefore, it is important to prevent injuries caused by obstacles.

[0017] Therefore, the obstacle detection device 2000 detects obstacle areas existing within a detection range around the user 20 (such as a worker performing work in the facility 10) by using the 3D map 40 and the position information 50. In this way, the user 20 can easily recognize the obstacles 30 existing around him / her. Therefore, it is possible to prevent injuries caused by the obstacles 30 in the facility 10.

[0018] Here, one method for preventing injuries caused by the obstacles 30 is to identify the obstacles 30 that exist in the facility 10 in advance and provide that information to the user 20. However, it is a heavy burden for the user 20 to know the positions of all the obstacles 30 in advance and move around the facility 10 while being aware of their existence. In addition, there is a risk that the user 20 forgets the existence of the obstacles 30 and gets injured by the obstacles 30.

[0019] In this regard, according to the obstacle detection device 2000, the obstacles 30 around the user 20 are automatically detected, so the user 20 does not need to know the positions of the obstacles 30 in advance. Therefore, the burden on the user 20 is smaller than in a case where the user 20 must know the positions of all obstacles 30 in advance. In addition, it is possible to prevent a problem in which the user 20 forgets the existence of an obstacle 30 and gets injured by that obstacle 30.

[0020] The obstacle detection device 2000 of this embodiment will be described in more detail below.

[0021] <Example of functional configuration> 2 is a block diagram illustrating a functional configuration of the obstacle detection device 2000 of the first embodiment. The obstacle detection device 2000 includes an acquisition unit 2020, a detection range determination unit 2040, and a detection unit 2060. The acquisition unit 2020 acquires the 3D map 40 and position information 50. The detection range determination unit 2040 determines a detection range for detecting an obstacle area from an area around the position of the user 20 on the 3D map 40. The detection unit 2060 detects an obstacle area from within the detection range.

[0022] <Example of hardware configuration> Each functional component of the obstacle detection device 2000 may be realized by hardware that realizes each functional component (e.g., a hardwired electronic circuit, etc.), or may be realized by a combination of hardware and software (e.g., a combination of an electronic circuit and a program that controls it). A case in which each functional component of the obstacle detection device 2000 is realized by a combination of hardware and software will be further described below.

[0023] 3 is a block diagram illustrating an example of a hardware configuration of a computer 500 that realizes the obstacle detection device 2000. The computer 500 is any computer. For example, the computer 500 is a stationary computer such as a PC (Personal Computer) or a server machine. In addition, for example, the computer 500 is a portable computer such as a smartphone or a tablet terminal. The portable computer is, for example, a user terminal carried by the user 20. The computer 500 may be a dedicated computer designed to realize the obstacle detection device 2000, or may be a general-purpose computer.

[0024] For example, by installing a specific application in the computer 500, each function of the obstacle detection device 2000 is realized by the computer 500. The application is configured with a program for realizing each functional component of the obstacle detection device 2000. The method of acquiring the program is arbitrary. For example, the program can be acquired from a storage medium (such as a DVD disk or a USB memory) in which the program is stored. Alternatively, the program can be acquired by downloading the program from a server device that manages the storage device in which the program is stored.

[0025] The computer 500 has a bus 502, a processor 504, a memory 506, a storage device 508, an input / output interface 510, and a network interface 512. The bus 502 is a data transmission path for the processor 504, the memory 506, the storage device 508, the input / output interface 510, and the network interface 512 to transmit and receive data to and from each other. However, the method of connecting the processor 504 and other components to each other is not limited to a bus connection.

[0026] The processor 504 is a processor such as a central processing unit (CPU), a graphics processing unit (GPU), or a field-programmable gate array (FPGA). The memory 506 is a main storage device realized using a random access memory (RAM) or the like. The storage device 508 is an auxiliary storage device realized using a hard disk, a solid state drive (SSD), a memory card, or a read only memory (ROM) or the like.

[0027] The input / output interface 510 is an interface for connecting the computer 500 to an input / output device. For example, the input / output interface 510 is connected to an input device such as a keyboard and an output device such as a display device.

[0028] The network interface 512 is an interface for connecting the computer 500 to a network. This network may be a LAN (Local Area Network) or a WAN (Wide Area Network).

[0029] The storage device 508 stores a program (a program for realizing the above-mentioned application) for realizing each functional component of the obstacle detection device 2000. The processor 504 reads this program into the memory 506 and executes it to realize each functional component of the obstacle detection device 2000.

[0030] The obstacle detection device 2000 may be realized by one computer 500 or by multiple computers 500. In the latter case, the configurations of the computers 500 do not need to be the same, and may be different from each other. For example, it is possible to realize some of the functions of the obstacle detection device 2000 by a user terminal and the other functions by a server device.

[0031] <Processing flow> Fig. 4 is a flowchart illustrating a process flow executed by the obstacle detection device 2000 of the embodiment 1. Note that the process flow shown in Fig. 4 is an example, and the process flow executed by the obstacle detection device 2000 is not limited to that shown in Fig. 4.

[0032] The acquisition unit 2020 acquires the 3D map 40 (S102). Steps S104 to S112 constitute a loop process L1. The loop process L1 is repeatedly executed until a predetermined end condition is satisfied.

[0033] In S104, the obstacle detection device 2000 determines whether or not a termination condition is satisfied. If the termination condition is satisfied, the process in Fig. 4 ends. On the other hand, if the termination condition is not satisfied, S106 is executed next.

[0034] The acquisition unit 2020 acquires the position information 50 (S106). The detection range determination unit 2040 determines the detection range using the 3D map 40 and the position information 50 (S108). The detection unit 2060 detects an obstacle area from within the detection range (S110). Since S112 is the end of the loop process L1, S104 is executed next.

[0035] In this way, by repeatedly obtaining position information 50 and detecting obstacle areas, it is possible to determine whether or not there is an obstacle 30 around user 20 after the user 20 moves, depending on the user's movement.

[0036] Here, various conditions can be used as the termination condition for the loop process L1. For example, the termination condition is that "a specified input operation is performed." Another example of the termination condition is that "the user 20 goes outside the range shown on the 3D map 40." For example, if the 3D map 40 shows the entire facility 10, this termination condition is satisfied when the user 20 goes outside the facility 10.

[0037] <About 3D Map 40> The three-dimensional map 40 is generated by measuring the three-dimensional position of each of the multiple locations in the facility 10 using various sensors. The sensor used to generate the three-dimensional map 40 is, for example, a distance measuring device that can measure the distance between the sensor and a measurement target. The distance measuring device measures the three-dimensional position of each of the multiple locations in real space using, for example, electromagnetic waves such as laser light. Specifically, the distance measuring device emits electromagnetic waves in multiple different directions, and receives reflected waves of each electromagnetic wave that are the electromagnetic waves reflected by an object. Then, the distance measuring device generates point data representing the three-dimensional position of the location that reflected the electromagnetic wave from the relationship between the emitted electromagnetic wave and the reflected wave. Point cloud data is generated as a collection of point data obtained for each of the multiple locations in the facility 10. Examples of such distance measuring devices include LiDAR and depth cameras.

[0038] The sensor is not limited to a distance measuring device. For example, a camera that generates a two-dimensional captured image may be used as the sensor. In this case, for example, by applying SFM (Structure from Motion) to a plurality of captured images including the facility 10, the three-dimensional coordinates of each of a plurality of points in the facility 10 are identified, and a three-dimensional map 40 consisting of a collection of point data indicating the three-dimensional coordinates is generated. In this case, the plurality of captured images are obtained by capturing images of the facility 10 from different directions. Note that existing technology can be used for the technology of generating three-dimensional data of an imaged object using SFM.

[0039] The 3D map 40 is not limited to the point cloud data described above as long as it has data (point data) that indicates the 3D position of each of the multiple locations of the facility 10. Examples of 3D data other than point cloud data include mesh data and surface data.

[0040] The sensor may be used in a fixed state at a specific position, or may be used while moving from that position. In the former case, measurement is performed using a sensor in a fixed state using, for example, a tripod. Alternatively, for example, a worker may remain at a specific position while holding or wearing the sensor and perform measurement using the sensor. After that, if measurement is to be performed at another position, the same operation is performed at the other position. In this way, measurement can be performed at each of a plurality of positions within the facility 10.

[0041] When the sensor is used while moving, for example, a worker holding or wearing the sensor moves through the facility 10 to measure the facility 10. In this case, the sensor is provided on a smartphone held by the worker, a wearable device worn by the worker, or the like. Alternatively, for example, a moving body (e.g., a flying or running robot) to which a sensor is attached may move through the facility 10 to measure the facility 10. By having the sensor repeatedly perform measurements while moving in this manner, measurements can be taken at multiple locations within the facility 10.

[0042] It should be noted that multiple measurements may be taken not only by changing the position of the sensor, but also by changing the measurement direction at the same position.

[0043] When measurements are performed at multiple positions and in multiple measurement directions, the measurement results obtained from each measurement are integrated to generate the 3D map 40. Note that existing technology can be used as a technology for integrating the measurement results performed at multiple positions and in multiple measurement directions to generate one piece of 3D data.

[0044] Here, the 3D map 40 may be data directly representing the measurement results of the distance measuring device or the results of the SFM, or may be data that has been subjected to a predetermined processing process on these data. The predetermined processing process may be, for example, a process of applying coordinate conversion to each point data so that the position of a specific point in the real space becomes the origin of the coordinate space of the 3D map 40, or a downsampling process that reduces the amount of point data by thinning it out at a predetermined distance interval.

[0045] 4, the obstacle detection device 2000 continues to use the 3D map 40 acquired in S102 as is. However, after acquiring the 3D map 40, the obstacle detection device 2000 may repeatedly update the 3D map 40 based on the results of measurements performed in real time (at the time when the obstacle detection device 2000 is being used). In this case, for example, measurements are performed using a sensor provided in the user terminal.

[0046] The obstacle detection device 2000 detects three-dimensional data that is not included in the three-dimensional map 40 by comparing three-dimensional data obtained by measurements performed in real time with the three-dimensional map 40. For example, three-dimensional data representing objects temporarily placed in the facility 10 or objects newly installed in the facility 10 after the three-dimensional map 40 was generated is detected as three-dimensional data that is not included in the three-dimensional map 40. Therefore, the obstacle detection device 2000 updates the three-dimensional map 40 so as to include the three-dimensional data detected in this manner.

[0047] <Acquisition of 3D map 40: S102> The acquisition unit 2020 acquires the 3D map 40 (S102). There are various methods for the acquisition unit 2020 to acquire the 3D map 40. For example, the 3D map 40 is stored in advance in a storage unit accessible from the obstacle detection device 2000. The acquisition unit 2020 acquires the 3D map 40 by accessing this storage unit.

[0048] Alternatively, for example, the 3D map 40 may be input to the obstacle detection device 2000 in response to a user operation. For example, the user connects a portable storage unit (such as a memory card) in which the 3D map 40 is stored to the obstacle detection device 2000, and inputs the 3D map 40 from the storage unit to the obstacle detection device 2000.

[0049] Alternatively, for example, the acquisition unit 2020 may acquire the 3D map 40 by receiving the 3D map 40 transmitted from another device. For example, the other device is a device that generates the 3D map 40.

[0050] <About location information 50> The location information 50 is information that represents the location of the user 20. Here, various information can be adopted as the information that represents the location of the user 20. For example, the location information 50 represents the location of the user terminal of the user 20. Here, various existing technologies can be used as a technology for identifying the location of a specific terminal in a specific place (here, in the facility 10). For example, the location of the user terminal can be identified by detecting the location of a location sensor such as a GPS (Global Positioning System) sensor provided in the user terminal. In addition, for example, the location of the user terminal may be identified based on communication between the user terminal and a beacon or RFID sensor provided in the facility 10.

[0051] Alternatively, the position of the user terminal may be specified by using measurement data obtained from a sensor provided in the user terminal. The measurement data used to specify the position of the user terminal may be, for example, a two-dimensional captured image obtained from a two-dimensional camera, or three-dimensional data obtained from a depth camera or LiDAR. Note that existing technology may be used to specify the position where the measurement was performed from the measurement data.

[0052] When using captured images as measurement data, for example, captured images obtained by capturing images at various combinations of the position and imaging direction of the facility 10 are prepared in advance as reference images. Then, by identifying an image that matches the captured image obtained from the user terminal from among the reference images, the position corresponding to the reference image that matches the captured image can be identified as the position of the user 20.

[0053] When three-dimensional data is used as the measurement data, for example, three-dimensional data that matches the three-dimensional data obtained from the user terminal is detected from the three-dimensional map 40. The three-dimensional data detected in this manner represents the three-dimensional area measured by the sensor. In addition, the measurement direction can be identified based on the measurement data. Then, the position of the sensor (i.e., the position of the user terminal) can be identified based on the measured three-dimensional area and the measurement direction.

[0054] Alternatively, for example, the user terminal may be provided with a self-location estimation function such as that employed in autonomous mobile robots, and the position of the user terminal may be identified by this self-location estimation.

[0055] A user terminal does not have to be used to identify the location of the user 20. For example, the location of the user 20 may be identified by detecting the user 20 using a monitoring sensor (such as a camera) provided in the facility 10.

[0056] The generation of the position information 50 may be performed by the obstacle detection device 2000, or may be performed by a device (such as a user terminal or a server device) other than the obstacle detection device 2000. The device that generates the position information 50 acquires information (e.g., measurement data) necessary to identify the position of the user 20, and generates the position information 50 by identifying the position of the user 20 using the acquired information.

[0057] <Getting location information 50: S106> The acquiring unit 2020 acquires the location information 50 (S106). The method of acquiring the location information 50 is arbitrary. For example, the acquiring unit 2020 acquires the location information 50 by receiving the location information 50 transmitted from a device that generated the location information 50. Alternatively, for example, the acquiring unit 2020 may acquire the location information 50 by accessing a storage unit in which the location information 50 is stored.

[0058] <Detection range determination: S108> The detection range determination unit 2040 determines the detection range by using the 3D map 40 and the position information 50 (S108). To this end, the detection range determination unit 2040 first identifies the position of the user 20 on the 3D map 40 (the position on the 3D map 40 corresponding to the position of the user 20 represented by the position information 50). Here, it is assumed that the coordinate system of the 3D map 40 and the coordinate system of the coordinates of the user 20 represented by the position information 50 are different from each other. In this case, the detection range determination unit 2040 identifies the position of the user 20 on the 3D map 40 by converting the coordinates of the user 20 represented by the position information 50 into coordinates on the coordinate system of the 3D map 40. Note that the relationship between the coordinate system of the 3D map 40 and the coordinate system of the coordinates of the user 20 represented by the position information 50 is determined in advance.

[0059] Here, the coordinates in one coordinate system can be converted into coordinates in another coordinate system by using an existing method. For example, a conversion matrix for converting the coordinates of the user 20 represented by the position information 50 into the coordinate system of the 3D map 40 is determined in advance. In this case, the detection range determination unit 2040 calculates the coordinates representing the position of the user 20 on the 3D map 40 by applying this conversion matrix to the coordinates of the user 20 shown in the position information 50.

[0060] On the other hand, it is assumed that the coordinate system of the 3D map 40 and the coordinate system of the coordinates of the user 20 indicated by the position information 50 are the same. In this case, the position of the user 20 on the 3D map 40 is represented by the coordinates of the user 20 themselves represented by the position information 50. Therefore, the detection range determination unit 2040 can use the position of the user 20 indicated in the position information 50 as the position of the user 20 on the 3D map 40 as it is.

[0061] After the position of the user 20 on the 3D map 40 is identified, the detection range determination unit 2040 determines the detection range on the 3D map 40 based on the position of the user 20 on the 3D map 40. The detection range is a range around the user 20, which is a range within which an obstacle area is to be detected.

[0062] There are various methods for determining the detection range. For example, the detection range determination unit 2040 determines a range of a predetermined shape and a predetermined size centered on the position of the user 20 as the detection range. The detection range is determined, for example, as a range on a two-dimensional plane obtained by viewing the three-dimensional map 40 in a planar view. Note that the planar view referred to here may be a view of the three-dimensional map 40 looking vertically downward, or a view of the floor surface on which the user 20 is located in the opposite direction to the normal vector of the map. Note that the floor surface referred to here also includes the ground. In other words, when the facility 10 is an outdoor facility, the floor surface on which the user 20 is located means the ground surface on which the user 20 is located.

[0063] The shape of the detection range may be any shape, such as a circle or a rectangle. The shape of the detection range may be fixed in advance, or may be changeable by an input operation. Similarly, the size of the detection range may be fixed in advance, or may be changeable by an input operation.

[0064] 5 is a first diagram illustrating an example of a detection range. A detection range 60 is a range of a radius d centered on the position 25 of the user 20 in a plan view.

[0065] The detection range may be determined based on the direction of the user 20 (the direction of gaze or the direction of movement). For example, the detection range is determined as a range of a predetermined angle with the direction of the user 20 as the reference direction. FIG. 6 is a second diagram illustrating an example of the detection range. In FIG. 6, the direction of the user 20 is represented by an arrow 70. The detection range 60 is determined as a sector-shaped range with a radius of d in a plan view and a range of ±θ° with the direction of the user 20 as the reference.

[0066] In this way, by narrowing the range in which obstacles 30 are detected based on the direction of user 20, it is possible to detect objects that are likely to hinder the movement of user 20 while reducing the time and computer resources required for the detection.

[0067] Here, various techniques can be used to detect the gaze direction and movement direction of the user 20. For example, the measurement direction of a sensor provided in the user terminal used by the user 20 can be identified, and the measurement direction can be treated as the gaze direction and movement direction of the user. Here, existing techniques can be used to identify the measurement direction based on measurement data obtained from the sensor. In addition, for example, the movement direction of the user 20 may be identified from the time change in the position of the user 20 indicated by each of the multiple position information 50 acquired so far.

[0068] The detection range may be determined so that the area is wider in a direction closer to the direction of the user 20. Fig. 7 is a third diagram illustrating an example of the detection range. In the example of Fig. 7, the distance from the user 20 to the boundary of the detection range is longer in a direction closer to the direction of the user 20. In this way, it is possible to detect the obstacle 30 in a wider range in a direction where the user 20 is more likely to move.

[0069] The detection range may be determined not only in the horizontal direction but also in the vertical direction. As another example, the detection range determination unit 2040 may determine, as the detection range, a range above, below, or both above and below the position of the user 20, the distance from the position of the user 20 being equal to or less than a predetermined value. This predetermined value may be the same above and below, or may be different. As another example, when the facility 10 has multiple floors, it is preferable that the detection range determination unit 2040 specifies the floor on which the user 20 is located and limits the detection range to only that floor.

[0070] <Obstacle area detection: S110> The detection unit 2060 detects an obstacle area from the detection range 60 (S110). There are various methods for detecting an obstacle area from the detection range 60. For example, one or more obstacle areas are predefined for the 3D map 40. In this case, the detection unit 2060 determines whether or not there is an obstacle area included in the detection range 60 among the predefined obstacle areas. If there is an obstacle area included in the detection range 60, the detection unit 2060 detects the obstacle area as an obstacle area included in the detection range 60. If there is no obstacle area included in the detection range 60 among the obstacle areas, the obstacle area is not detected.

[0071] When the obstacle area is defined in advance, the acquisition unit 2020 acquires information defining the obstacle area (hereinafter, obstacle definition information) together with the 3D map 40. This obstacle definition information is stored in an arbitrary storage unit in a form that can be acquired from the obstacle detection device 2000. Note that the obstacle definition information may be included in the 3D map 40.

[0072] There are various methods for generating the obstacle definition information. For example, the obstacle definition information is manually generated by an administrator of the obstacle detection device 2000. Alternatively, for example, the obstacle definition information may be automatically generated by a computer. The computer that generates the obstacle definition information may be the obstacle detection device 2000 or may be other than the obstacle detection device 2000. Hereinafter, the computer that generates the obstacle definition information is referred to as an obstacle definition information generating device.

[0073] When automatically generating obstacle definition information, conditions for an area to be treated as an obstacle area (hereinafter, obstacle conditions) are defined in advance. The obstacle definition information generating device detects an area that satisfies the obstacle conditions from among areas included in the 3D map 40, and generates obstacle definition information that indicates the area as an obstacle area.

[0074] The detection unit 2060 does not need to use obstacle definition information to detect an obstacle area. In other words, the obstacle area included in the 3D map 40 does not need to be defined in advance. In this case, for example, the detection unit 2060 detects an area that satisfies an obstacle condition from within the detection range 60, and treats the area as an obstacle area. That is, an area that satisfies the obstacle condition from among the areas included in the detection range 60 is detected as an obstacle area. Note that information indicating the obstacle condition is stored in advance in an arbitrary storage unit in a form that can be acquired from the obstacle detection device 2000.

[0075] For ease of understanding, the following description will be given on the assumption that an obstacle area satisfying the obstacle condition is detected by the detection unit 2060. Even in the case where an obstacle area satisfying the obstacle condition is detected by an obstacle definition information generating device, the obstacle area can be detected by a similar method.

[0076] For example, the obstacle condition is determined as a condition for one surface. In this case, the detection unit 2060 determines whether or not each surface included in the detection range 60 of the 3D map 40 satisfies the obstacle condition. Then, the 3D data representing the surface that satisfies the obstacle condition, or the 3D data of the object that includes the surface, is detected as an obstacle area. Hereinafter, the surface for which it is determined whether or not the obstacle condition is satisfied is referred to as a target surface.

[0077] The obstacle condition is set so that a surface to be treated as an obstacle 30 satisfies the obstacle condition. For example, the obstacle condition is a condition that "the angle between the target surface and the horizontal plane is large (e.g., equal to or greater than a threshold)." This condition is satisfied when the target surface is not a surface on which the user 20 lies but a surface on which the user is standing. Surfaces that satisfy such a condition are treated as obstacles because the user 20 is less likely to stumble on a surface on which the user 20 lies, whereas the user is more likely to stumble on a surface on which the user is standing.

[0078] Whether the angle between the target surface and the horizontal plane is large can be verified, for example, by using the normal vector of the target surface. Specifically, the closer the direction of the normal vector of the target surface is to the horizontal direction, the closer the angle between the target surface and the horizontal plane is to 90°. Therefore, for example, as an obstacle condition that further embodies the obstacle condition "a surface that forms a large angle with the horizontal plane," the condition "the angle between the normal direction of the target surface and the horizontal direction is equal to or smaller than a threshold" may be used.

[0079] Alternatively, for example, an obstacle condition may be defined so that a surface that is relatively low in height from the floor surface is detected as an obstacle area. Objects that are low in height from the floor surface are unlikely to be in the field of view of the user 20, and are therefore likely to cause the user 20 to fall. Thus, for example, a condition that "the height of the target surface from the floor surface is equal to or less than a threshold value" is defined as an obstacle condition.

[0080] The floor surface here means the floor surface on which the object including the target surface is located. The facility 10 may have multiple floors and scaffolding in various locations. In such cases, the "height from the floor surface of the target surface" refers to the height from the floor surface of the floor or scaffolding on which the object including the target surface is placed.

[0081] Therefore, for example, the detection unit 2060 first identifies the floor surface on which the object including the target surface is located. The detection unit 2060 then calculates the height of the target surface from the floor surface by subtracting the z coordinate of the floor surface from the maximum z coordinate of the z coordinates of each point included in the target surface. However, the method of calculating the height of the target surface from the floor surface is not limited to this method.

[0082] Other obstacle conditions include, for example, "located at a height from the floor surface equal to or lower than a threshold value." For example, it is considered that the probability that the user will collide with an object installed at a position 2 m or higher from the floor surface is low. Therefore, in this way, the target surface that is located at a high position where the user 20 will not collide is not treated as an obstacle area.

[0083] The obstacle condition may include multiple conditions. For example, the following conditions may be used as the obstacle condition: "1) the angle between the normal direction of the target surface and the horizontal direction is equal to or less than a threshold, and 2) the height of the target surface from the floor surface is equal to or less than a threshold." In this case, a target surface on which a person stands and is low in height is identified as a target surface that satisfies the obstacle condition.

[0084] <<How to detect the target surface>> The detection unit 2060 detects one or more target surfaces from within the detection range 60 of the 3D map 40, and determines whether or not each target surface satisfies an obstacle condition. To this end, the detection unit 2060 detects the target surfaces. The method of detecting the target surfaces will be described below.

[0085] For example, the detection unit 2060 clusters each piece of point data included in the detection range 60 among the point data shown on the 3D map 40 for each piece of point data representing the same surface. Then, the detection unit 2060 treats the surface represented by each cluster as a target surface. In other words, multiple pieces of point data included in one cluster are treated as a group of point data constituting one target surface.

[0086] There are various methods for clustering point data for each same surface. For example, the detection unit 2060 calculates the normal vector of each point data included in the detection range 60. Here, the normal vector of the point data can be calculated as the normal vector of a triangular surface formed by the point data and two other point data adjacent to the point data. Note that, when the 3D map 40 is mesh data, the normal vector of the point data can be calculated as the normal vector of any one of the surfaces including the point data.

[0087] The detection unit 2060 clusters the point data based on the direction of the normal vector. Specifically, when the difference between the direction of the normal vector of a certain point data and the direction of the normal vector of other point data close to the point data (for example, the distance is less than a threshold value), the detection unit 2060 places these point data in the same cluster. This process makes it possible to capture multiple small adjacent surfaces with similar orientations as one large surface.

[0088] Here, the 3D map 40 may include objects with curved surfaces, such as pipes and tanks. For example, the detection unit 2060 may detect a cylindrical region by performing cylindrical fitting, and treat the side surface of the cylindrical region as the target surface. Here, the cylindrical fitting may be realized using an algorithm such as RANSAC. Here, the target surface may be a curved surface, such as when the side surface of a cylindrical region is the target surface. In this case, the normal line of the target surface that is closest to the horizontal direction is used to determine whether or not the obstacle condition of "the angle between the target surface and the horizontal plane is large" is satisfied. That is, for the normal line that is closest to the horizontal direction, if the angle between the normal line direction and the horizontal direction is equal to or smaller than a threshold value, it is determined that the above obstacle condition (in other words, the condition that "the angle between the normal line of the target surface and the horizontal plane is equal to or smaller than a threshold value") is satisfied.

[0089] When performing cylindrical fitting, the detection unit 2060 detects an area that is highly likely to have a cylindrical shape from within the 3D map 40, and performs cylindrical fitting on that area. This can be achieved, for example, by using semantic segmentation. Specifically, the detection unit 2060 performs semantic segmentation on the point data included in the detection range 60, thereby clustering the point data for each piece of point data representing the same object. This separates the point data into clusters for each piece of point data representing the same object. In addition, the type of object represented by each cluster (such as a wall, a tank, a pipe, or a steel structure) is identified.

[0090] Furthermore, for each cluster of a type (such as a tank or a pipe) that is the target of cylindrical fitting, the detection unit 2060 performs cylindrical fitting on point data included in the cluster to identify a target surface that corresponds to the cluster. Note that the type that is the target of cylindrical fitting is determined in advance.

[0091] The detection unit 2060 may use both a method of clustering point data based on normal vectors and a method of using cylindrical fitting. In this case, for example, the detection unit 2060 first identifies an area to be subjected to cylindrical fitting, and performs cylindrical fitting on the area to detect the side of the cylindrical area as a target surface. After that, the detection unit 2060 performs clustering using normal vectors on each piece of point data that is not included in the cylindrical area among the point data included in the detection range 60, thereby detecting the remaining target surfaces.

[0092] Alternatively, for example, the detection unit 2060 may divide the point data into clusters for the same object by semantic segmentation, and then detect the target surface for each object. In this case, the detection unit 2060 first applies semantic segmentation to the point data included in the detection range 60 to divide the point data into clusters for each point data representing the same object. Next, the detection unit 2060 performs target surface detection using cylindrical fitting for the cluster of the type of object that is the target of cylindrical fitting. Furthermore, the detection unit 2060 performs clustering using the normal vector described above for multiple point data included in the same cluster for each cluster of other types of objects. As a result, multiple target surfaces are detected from the cluster of the same object.

[0093] Alternatively, for example, when the 3D map 40 is composed of point cloud data, the detection unit 2060 may detect the target surface by converting the point cloud data into mesh data. As a result, data representing a plurality of surfaces is obtained from the point cloud data. Thus, for example, the detection unit 2060 treats each surface obtained by meshing as a target surface. Thus, for example, the detection unit 2060 treats each surface obtained by meshing as target data. However, in order to treat a surface of a certain size as a target surface, the detection unit 2060 may downsample the point cloud data and then convert it into mesh data.

[0094] <<Real-time obstacle detection>> In addition, when three-dimensional data of objects around the user 20 is obtained by measurements performed in real time, an obstacle area may be detected from the three-dimensional data thus obtained. For example, the detection unit 2060 identifies three-dimensional data representing objects not shown in the three-dimensional map 40 by taking a difference between the three-dimensional data obtained by measurements performed in real time and the three-dimensional map 40. In this way, among the three-dimensional data obtained by measurements performed in real time, the three-dimensional data representing objects not shown in the three-dimensional map 40 is called temporary three-dimensional data. The temporary three-dimensional data is, for example, three-dimensional data representing objects such as equipment and cables temporarily placed in the facility 10.

[0095] The detection unit 2060 detects, from the temporary three-dimensional data, a surface whose position on the three-dimensional map 40 is within the detection range 60 and which satisfies an obstacle condition. The surface thus detected, or an area representing an object including that surface, is also treated as an obstacle area. However, it is preferable that the obstacle area detected from the three-dimensional map 40 and the obstacle area detected from the temporary three-dimensional data are distinguishable from each other. For example, as described below, when outputting information about obstacle areas, it is made clear whether each obstacle area was detected from the three-dimensional map 40 or the temporary three-dimensional data.

[0096] <Detection result output> When an obstacle area is detected, the obstacle detection device 2000 preferably outputs information related to the detected obstacle area. The information output here is called output information. Hereinafter, the functional configuration unit that outputs the output information is called an output unit.

[0097] 8 is a block diagram illustrating a functional configuration of the obstacle detection device 2000 having an output unit. The output unit 2080 outputs output information 80. The output destination of the output information 80 is, for example, a user terminal of the user 20. By outputting the output information 80 to the user terminal, the user 20 can easily recognize the presence of an obstacle 30. This improves safety when the user 20 moves around the facility 10 and uses the facility 10. Note that the output destination of the output information 80 is arbitrary and is not limited to the user terminal.

[0098] Various information is indicated by the output information 80. For example, the output information 80 indicates the type (step, equipment, desk, cable, etc.), size, or position of the obstacle 30 represented by the obstacle area. Furthermore, in the case where an obstacle area is also detected from temporary three-dimensional data as described above, the output information 80 may indicate, for each obstacle area, whether it was detected from the three-dimensional map 40 or the temporary three-dimensional data.

[0099] The output information 80 represents, for example, a warning to inform the user 20 of the presence of an obstacle 30. In this case, it is preferable that the output information 80 includes a message representing the warning. FIG. 9 is a first diagram illustrating the output information 80. FIG. 9 shows a message 90 representing the contents of the output information 80. The message 90 indicates the position of the obstacle (diagonally forward to the left), the size of the obstacle (height: approximately 20 cm), and the type of the obstacle (step). The message 90 is displayed, for example, on a display device of a user terminal carried by the user 20.

[0100] The output information 80 may further include a three-dimensional map of the periphery of the obstacle area. FIG. 10 is a second diagram illustrating the output information 80. FIG. 10 shows a screen 100 showing the contents of the output information 80. The screen 100 displays a portion of the three-dimensional map 40 that is included in the field of view of the user 20. In addition, the obstacle area 130 is highlighted on the screen 100. Specifically, the obstacle area 130 is shaded with diagonal lines. In addition, the screen 100 displays a message 110 for notifying the user that a cable exists as the obstacle 30, and an arrow 120 indicating the direction in which the obstacle 30 is located. It is preferable that the range of the three-dimensional map 40 displayed on the screen 100 is changed in accordance with a change in the field of view of the user 20 (a change in the position or direction of the user 20).

[0101] There are various methods for determining the portion of the 3D map 40 included in the field of view of the user 20. For example, the output unit 2080 treats the portion of the 3D map 40 included in a predetermined range that represents the field of view when looking in the direction of movement of the user 20 from the position of the user 20 as the portion included in the field of view of the user 20. In addition, for example, when the user 20 uses a camera (hereinafter, a user camera) provided on a user terminal, the output unit 2080 may treat the portion of the 3D map 40 included in the imaging range of the user camera or a predetermined range therein as the portion of the 3D map 40 included in the field of view of the user 20.

[0102] Here, it is assumed that the scenery in the field of view of the user 20 is captured using the user camera. In this case, the image obtained from the user camera may be displayed on the screen 100 instead of the 3D map 40. In this case, the output unit 2080 superimposes the various displays shown in FIG. 10 on the image obtained from the user camera. Specifically, the output unit 2080 identifies an area corresponding to an obstacle area detected from the detection range from the image, and highlights the area. In addition, the output unit 2080 superimposes a message 110 and an arrow 120 indicating the direction of the area corresponding to the obstacle area on the image of the user camera.

[0103] Also, assume that the user 20 is wearing a glasses-type device, and the glasses-type device is configured to display any information on a transparent lens that functions as a display device, thereby superimposing the information on the actual scenery seen through the lens. In this case, the output unit 2080 superimposes information about the obstacle 30 on the surrounding scenery seen by the user 20 through the glasses-type device. Specifically, the output unit 2080 identifies an area of ​​the lens that corresponds to an obstacle area, and displays an image indicating that the area is an obstacle area (for example, an image of a specific color covering the obstacle area, etc.) on the area. Also, the output unit 2080 displays a message 110 and an arrow 120 on the lens.

[0104] The output information 80 is not limited to visual information. For example, the output information 80 may be auditory information. That is, the output unit 2080 may output a message indicating a warning or a message indicating the position of an obstacle as a voice message. In this case, the user 20 can recognize the obstacle by listening to the voice message output from a speaker provided in the user terminal or the facility 10.

[0105] There are various ways to output the output information 80. For example, the output unit 2080 transmits the output information 80 to a user terminal. Alternatively, for example, the output unit 2080 may output the output information 80 to a storage unit accessible from the user terminal. In this case, the user terminal acquires the output information 80 by accessing this storage unit. Alternatively, for example, when the output information 80 is a voice message, the output information 80 may be output to a speaker provided in the facility 10, so that the speaker outputs the voice message. Here, when a plurality of speakers are provided in the facility 10, it is preferable that, for example, the output unit 2080 identifies the speaker closest to the position of the user 20 and outputs the output information 80 to the speaker.

[0106] Although the present invention has been described above with reference to the embodiment, the present invention is not limited to the above embodiment. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

[0107] In the above example, the program includes a set of instructions (or software code) for making the computer perform one or more functions described in the embodiment when the program is loaded into the computer. The program may be stored in a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, the computer-readable medium or tangible storage medium includes random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technology, CD-ROM, digital versatile disc (DVD), Blu-ray (registered trademark) disk or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device. The program may be transmitted on a transitory computer-readable medium or communication medium. By way of example and not limitation, the transitory computer-readable medium or communication medium includes electrical, optical, acoustic, or other forms of propagating signals.

[0108] A part or all of the above-described embodiments can be described as, but is not limited to, the following supplementary notes. (Appendix 1) an acquisition unit that acquires a three-dimensional map that indicates a three-dimensional position of each of a plurality of points in the facility and location information that indicates a user's location; a detection range determination unit that uses the three-dimensional map and the position information to determine a detection range in which an obstacle area is to be detected from an area around the user's position on the three-dimensional map; a detection unit that detects the obstacle area from the detection range. (Appendix 2) The detection range determination unit is Identifying a location of the user on the three-dimensional map; 2. The obstacle detection device according to claim 1, wherein a range of a predetermined size including the identified position is determined as the detection range. (Appendix 3) The detection range determination unit is Identifying the user's position on the three-dimensional map and the user's movement direction or line of sight direction; 2. The obstacle detection device according to claim 1, wherein a range of a predetermined angle including the user's movement direction or line of sight is determined as the detection range. (Appendix 4) The detection range determination unit is Identifying the user's position on the three-dimensional map and the user's movement direction or line of sight direction; 2. An obstacle detection device as described in claim 1, wherein the detection range is determined so that a size in the user's movement direction or line of sight direction is larger than a size in other directions. (Appendix 5) The detection unit is 5. The obstacle detection device according to claim 1, wherein, among target surfaces that are surfaces included in the detection range in the 3D map, a target surface that satisfies an obstacle condition, which is a condition for treating it as an obstacle, or a region that represents an object that includes the target surface is detected as the obstacle region. (Appendix 6) 6. The obstacle detection device according to claim 5, wherein the obstacle condition includes a condition that an angle between the target surface and a horizontal direction is equal to or greater than a first threshold value. (Appendix 7) 7. The obstacle detection device according to claim 5, wherein the obstacle condition includes a condition that a height of the target surface from a floor surface is equal to or less than a second threshold value. (Appendix 8) 8. An obstacle detection device according to any one of claims 1 to 7, comprising an output unit that outputs output information indicating information related to the detected obstacle area. (Appendix 9) The obstacle detection device described in Appendix 8, wherein the output information includes a display highlighting the obstacle area, a display showing the position or direction of the obstacle area, a display showing the type of obstacle represented by the obstacle area, or a screen including two or more of these. (Appendix 10) 1. A computer implemented method for obstacle detection, comprising: An acquisition step of acquiring a three-dimensional map representing a three-dimensional position of each of a plurality of points of the facility and location information representing a user's location; a detection range determination step of determining, using the three-dimensional map and the position information, a detection range in which an obstacle area is to be detected from an area around the user's position on the three-dimensional map; a detection step of detecting the obstacle area from the detection range. (Appendix 11) In the detection range determination step, Identifying a location of the user on the three-dimensional map; 11. The obstacle detection method according to claim 10, further comprising determining a range of a predetermined size including the identified position as the detection range. (Appendix 12) In the detection range determination step, Identifying the user's position on the three-dimensional map and the user's movement direction or line of sight direction; The obstacle detection method according to claim 10, wherein a range of a predetermined angle including the user's movement direction or line of sight is determined as the detection range. (Appendix 13) In the detection range determination step, Identifying the user's position on the three-dimensional map and the user's movement direction or line of sight direction; The obstacle detection method according to claim 10, wherein the detection range is determined so that a size in the user's movement direction or line of sight direction is larger than a size in other directions. (Appendix 14) In the detection step, 14. The obstacle detection method according to claim 10, further comprising detecting, as the obstacle area, a target surface that satisfies an obstacle condition, which is a condition for treating the target surface as an obstacle, or an area that represents an object that includes the target surface, among target surfaces that are included in the detection range in the 3D map. (Appendix 15) 15. The obstacle detection method according to claim 14, wherein the obstacle condition includes a condition that the magnitude of an angle between the target surface and a horizontal direction is equal to or greater than a first threshold value. (Appendix 16) 16. The obstacle detection method according to claim 14, wherein the obstacle condition includes a condition that a height of the target surface from a floor surface is equal to or less than a second threshold value. (Appendix 17) 17. The obstacle detection method according to any one of claims 10 to 16, comprising an output step of outputting output information indicative of information regarding the detected obstacle region. (Appendix 18) The obstacle detection method described in Appendix 17, wherein the output information includes a display highlighting the obstacle area, a display showing the position or direction of the obstacle area, a display showing the type of obstacle represented by the obstacle area, or a screen including two or more of these. (Appendix 19) An acquisition step of acquiring a three-dimensional map representing a three-dimensional position of each of a plurality of points of the facility and location information representing a user's location; a detection range determination step of determining, using the three-dimensional map and the position information, a detection range in which an obstacle area is to be detected from an area around the user's position on the three-dimensional map; A non-transitory computer-readable medium storing a program for causing a computer to execute the steps of: detecting the obstacle region from the detection range. (Appendix 20) In the detection range determination step, Identifying a location of the user on the three-dimensional map; The computer-readable medium of claim 19, further comprising determining a range of a predetermined size including the identified position as the detection range. (Appendix 21) In the detection range determination step, Identifying the user's position on the three-dimensional map and the user's movement direction or line of sight direction; The computer-readable medium of claim 19, wherein a predetermined angle range including the user's movement direction or gaze direction is determined as the detection range. (Appendix 22) In the detection range determination step, Identifying the user's position on the three-dimensional map and the user's movement direction or line of sight direction; 20. The computer-readable medium of claim 19, wherein the detection range is determined such that a magnitude in a direction of movement or a direction of gaze of the user is greater than a magnitude in other directions. (Appendix 23) In the detection step, 23. The computer-readable medium of claim 19, wherein, among target surfaces that are surfaces included in the detection range in the 3D map, a target surface that satisfies an obstacle condition, which is a condition for treating the target surface as an obstacle, or an area representing an object that includes the target surface is detected as the obstacle area. (Appendix 24) 24. The computer-readable medium of claim 23, wherein the obstacle condition includes a condition that an angle between the target plane and a horizontal direction is greater than or equal to a first threshold. (Appendix 25) 25. The computer-readable medium of claim 23 or 24, wherein the obstacle condition includes a condition that a height of the target surface from a floor surface is less than or equal to a second threshold. (Appendix 26) 26. The computer-readable medium of any one of claims 19 to 25, further comprising an output step of outputting output information indicative of information regarding the detected obstacle region. (Appendix 27) 27. The computer-readable medium of claim 26, wherein the output information includes a display highlighting the obstacle region, a display indicating the position or direction of the obstacle region, a display indicating the type of obstacle represented by the obstacle region, or a screen including two or more of these. [Explanation of symbols]

[0109] 10 Facilities 20 users 25 positions 30 Obstacles 40 3D Map 50 Location information 60 Detection Range 70 Arrow 80 Output Information 90 Messages 100 screens 110 Messages 120 Arrow 130 Obstacle Area 500 Computers 502 Bus 504 Processor 506 Memory 508 Storage Devices 510 Input / Output Interface 512 Network Interface 2000 Obstacle detection device 2020 Acquisition Department 2040 Detection range determination unit 2060 Detector 2080 Output section

Claims

1. an acquisition unit that acquires a three-dimensional map representing a three-dimensional position of each of a plurality of points in the facility and location information representing a user's location; a detection range determination unit that determines a detection range in which an obstacle area is to be detected from an area around the user's position on the three-dimensional map, using the three-dimensional map and the position information; a detection unit that detects the obstacle area from the detection range, the detection unit detects, as the obstacle area, an object surface that satisfies an obstacle condition, which is a condition for treating the object surface as an obstacle, or an area that represents an object including the object surface, among object surfaces that are surfaces included in the detection range on the three-dimensional map; The obstacle detection device, wherein the obstacle condition includes a condition that the magnitude of an angle between the target surface and a horizontal direction is equal to or greater than a threshold value.

2. An acquisition unit that acquires a three-dimensional map representing the three-dimensional position of each of a plurality of points in a facility and location information representing a user's location; a detection range determination unit that determines a detection range in which an obstacle area is to be detected from an area around the user's position on the three-dimensional map, using the three-dimensional map and the position information; a detection unit that detects the obstacle area from the detection range, the detection unit detects, as the obstacle area, an object surface that satisfies an obstacle condition, which is a condition for treating the object surface as an obstacle, or an area that represents an object including the object surface, among object surfaces that are surfaces included in the detection range on the three-dimensional map; The obstacle detection device, wherein the obstacle condition includes a condition that the height of the target surface from a floor surface is equal to or less than a threshold value.

3. The detection range determination unit is Identifying a location of the user on the three-dimensional map; 3. The obstacle detection device according to claim 1, wherein a range of a predetermined size including the identified position is determined as the detection range.

4. The detection range determination unit is Identifying the position of the user on the three-dimensional map and the direction of movement or line of sight of the user; The obstacle detection device according to claim 1 , wherein the detection range is determined to be a range of a predetermined angle including a moving direction or a line of sight direction of the user.

5. The detection range determination unit is Identifying the position of the user on the three-dimensional map and the direction of movement or line of sight of the user; The obstacle detection device according to claim 1 , wherein the detection range is determined so that a size in the user's moving direction or line of sight direction is larger than sizes in other directions.

6. The obstacle detection device according to claim 1 , further comprising an output unit that outputs output information indicating information related to the detected obstacle area.

7. 1. A computer implemented method for obstacle detection, comprising: an acquisition step of acquiring a three-dimensional map representing a three-dimensional position of each of a plurality of points of the facility and location information representing a user's location; a detection range determination step of determining, using the three-dimensional map and the position information, a detection range in which an obstacle area is to be detected from an area around the user's position on the three-dimensional map; a detection step of detecting the obstacle area from the detection range, In the detection step, a target surface that satisfies an obstacle condition, which is a condition for treating the target surface as an obstacle, or a region that represents an object including the target surface is detected as the obstacle region, among target surfaces that are surfaces included in the detection range in the three-dimensional map; The obstacle detection method, wherein the obstacle condition includes a condition that the magnitude of an angle between the target surface and a horizontal direction is equal to or greater than a threshold value.

8. A computer-implemented method for obstacle detection, comprising: an acquisition step of acquiring a three-dimensional map representing a three-dimensional position of each of a plurality of points of the facility and location information representing a user's location; a detection range determination step of determining, using the three-dimensional map and the position information, a detection range in which an obstacle area is to be detected from an area around the user's position on the three-dimensional map; a detection step of detecting the obstacle area from the detection range, In the detection step, a target surface that satisfies an obstacle condition, which is a condition for treating the target surface as an obstacle, or a region that represents an object including the target surface is detected as the obstacle region, among target surfaces that are surfaces included in the detection range in the three-dimensional map; The obstacle detection method, wherein the obstacle condition includes a condition that a height of the target surface from a floor surface is equal to or less than a threshold value.

9. an acquisition step of acquiring a three-dimensional map representing a three-dimensional position of each of a plurality of points of the facility and location information representing a user's location; a detection range determination step of determining, using the three-dimensional map and the position information, a detection range in which an obstacle area is to be detected from an area around the user's position on the three-dimensional map; a detection step of detecting the obstacle region from the detection range; In the detection step, a target surface that satisfies an obstacle condition, which is a condition for treating the target surface as an obstacle, or a region that represents an object including the target surface is detected as the obstacle region, among target surfaces that are surfaces included in the detection range in the three-dimensional map; The obstacle condition includes a condition that an angle between the target surface and a horizontal direction is equal to or greater than a threshold value.

10. An acquisition step of acquiring a three-dimensional map representing the three-dimensional position of each of a plurality of points of the facility and location information representing the user's location; a detection range determination step of determining, using the three-dimensional map and the position information, a detection range in which an obstacle area is to be detected from an area around the user's position on the three-dimensional map; a detection step of detecting the obstacle region from the detection range; In the detection step, a target surface that satisfies an obstacle condition, which is a condition for treating the target surface as an obstacle, or a region that represents an object including the target surface is detected as the obstacle region, among target surfaces that are surfaces included in the detection range in the three-dimensional map; The obstacle condition includes a condition that a height of the target surface from a floor surface is equal to or less than a threshold value.

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