Information processing device and signal strength map completion method

Abstract

To generate a signal strength map with high accuracy and high convenience.SOLUTION: An information processing device includes: an acquisition section that acquires a first signal strength map of a target space, which is obtained from a result of signal strength measurement in the target space; and an output section that outputs a second signal strength map of the target space, which is obtained by completing the first signal strength map on the basis of information generated from the other data source different from a data source of the signal strength measurement. The other data source is a simulation of signal strength in the target space based on map information and ray tracing of the target space.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing device and a radio wave intensity map complementing method. [Background technology]

[0002] There are known techniques for plotting the radio wave strength of wireless equipment as a map. For example, Patent Document 1 discloses an invention related to generating a radio wave strength heat map that takes into account the radio wave strength in a real environment. The invention described in Patent Document 1 updates the map by using a spatial propagation coefficient that represents changes in radio waves due to the shape of the space or obstacles. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 7399387 Summary of the Invention [Problem to be solved by the invention]

[0004] The technology described in Patent Document 1 has a problem in that the accuracy of the radio wave intensity map is poor in places where radio wave intensity measurements are not performed.

[0005] In response to this, the present invention provides a technique for creating a more accurate radio wave intensity map even in places that cannot be covered by radio wave intensity measurements. [Means for solving the problem]

[0006] One aspect of the present disclosure provides an information processing device having an acquisition unit that acquires a first radio wave intensity map of a target space obtained from the results of radio wave intensity measurements in the target space, and an output unit that outputs a second radio wave intensity map of the target space that is supplemented with the first radio wave intensity map based on information generated from a data source other than the radio wave intensity measurements.

[0007] Another aspect of the present disclosure provides a radio wave intensity map completion method including the steps of: acquiring a first radio wave intensity map of a target space obtained from the results of radio wave intensity measurements in the target space; and outputting a second radio wave intensity map of the target space by completing the first radio wave intensity map based on information generated from a data source other than the radio wave intensity measurements. [Effects of the Invention]

[0008] According to the present invention, it is possible to create a highly accurate and convenient radio wave intensity map. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating an example of the system configuration of an information processing system 1. [Figure 2] FIG. 1 is a diagram illustrating an example of the functional configuration of an information processing system 1. [Figure 3] FIG. 1 is a diagram illustrating an example of a hardware configuration of an information processing device 10. [Figure 4] 4 is a sequence chart illustrating a method for measuring radio wave intensity in the information processing system 1. [Figure 5] FIG. 10 is a diagram illustrating an example of an actual measurement database 1001. [Figure 6] FIG. 10 is a diagram illustrating self-position estimation using SLAM processing. [Figure 7] 4 is a flowchart illustrating a method for generating a radio wave intensity map in the information processing system 1. [Figure 8] FIG. 4 is a diagram illustrating a first radio wave intensity map. [Figure 9] FIG. 10 is a diagram illustrating a radio wave propagation simulation in a three-dimensional space V1. [Figure 10] FIG. 10 is a diagram illustrating a second radio wave intensity map. [Figure 11] 4 is a sequence chart illustrating a method for estimating the position of a mobile terminal in the information processing system 1. DETAILED DESCRIPTION OF THE INVENTION

[0010] 1. Configuration FIG. 1 is a diagram illustrating the system configuration of an information processing system 1. In this example, the information processing system 1 (or simply referred to as the "system") is a system for creating a geographical map (hereinafter referred to as a "radio signal strength map") reflecting the radio wave strength of radio waves output from access points (an example of wireless communication transmission equipment) that perform wireless communication in a target space (hereinafter referred to as a "target space"), such as an indoor facility. Furthermore, the information processing system 1 is a system for estimating the position (so-called "indoor positioning") of a mobile terminal (or simply referred to as a "terminal") in the target space using the created radio wave strength map. Positioning methods using radio waves are a useful means suitable for terminal positioning indoors, where satellite positioning using GPS (Global Positioning System) or the like does not function adequately. Therefore, there is a demand for improved technology that can perform accurate position estimation even indoors. It is particularly important to create a highly accurate radio wave strength map that indicates the radio wave environment in the target space.

[0011] In related art, radio wave intensity maps have traditionally been created by installing or moving sensors in a grid pattern (e.g., a lattice or square) in a target space to collect radio wave information. This requires personnel to install sensors or equipment to perform fixed-point measurements. This process requires significant manpower and time. In contrast, a method for collecting radio wave information using a mobile object (e.g., a robot) that moves through the target space instead of fixed sensors is conceivable. In this case, the results of radio wave information collection depend on the robot's path. In other words, if there are areas in the target space where the robot does not measure radio wave intensity, even if radio wave information is acquired, only a sparse radio wave intensity map can be constructed. This can result in many locations that cannot be fully covered by data collection. Conversely, if radio wave intensity measurements are to be performed throughout the entire target space, the robot's travel distance will naturally be extended. Ultimately, this method is not an essential solution due to the increased labor and time required. To address these issues, the information processing system 1 provides a technology for creating highly accurate and convenient radio wave intensity maps.

[0012] The information processing system 1 obtains a highly accurate radio wave intensity map (an example of a second radio wave intensity map) by supplementing a radio wave intensity map (an example of a first radio wave intensity map) of a target space obtained from the results of radio wave intensity measurement with information generated from a data source other than the radio wave intensity measurement (hereinafter simply referred to as "another data source"). Radio wave intensity measurement refers to, for example, conducting a test, experiment, or survey to measure the radio wave intensity of radio waves from a wireless communication transmission facility, i.e., a received signal strength indicator (RSSI) value. The wireless communication transmission facility is wireless equipment such as a Wi-Fi access point that is pre-installed in the target space. Note that a mobile terminal such as a smartphone can access a network or perform positioning of the terminal in the target space by wirelessly connecting to the wireless communication transmission facility.

[0013] Here, the term "another data source" refers to an element for generating a radio wave strength map that does not depend solely on the results of radio wave strength measurements in order to solve the above-mentioned problem. As an example, the other data source is a simulation (or its results) of radio wave strength in a target space based on map information and ray tracing of the target space. A simulation of radio wave strength in a target space refers to a method for simulating the propagation characteristics of radio waves transmitted from an access point, such as straight propagation, reflection, transmission, or diffraction, in a three-dimensional space constructed on a computer, and predicting or evaluating the propagation characteristics. Unlike evaluations based on actual measurements of radio wave strength measured at an actual site (by radio wave strength measurements), when performing a simulation, it is necessary to set conditions or parameters for constructing a spatial model on a computer that is identical to the real space. Alternatively, it is necessary to specify a calculation method or computation method for reproducing the physical behavior of radio waves in the spatial model. Therefore, in the present invention, the system performs a simulation based on (A) map information of the target space and (B) ray tracing.

[0014] First, (A) map information of the target space (or simply "map information") is a 3D environmental map generated using image data representing photographed images of the target space. A 3D environmental map is obtained by analyzing and integrating image data captured from multiple viewpoints to create a three-dimensional 3D model. Typical creation techniques include GS (Gaussian Splatting), photogrammetry, and NeRF (Neural Radiance Fields).

[0015] Next, (B) ray tracing is, in a broad sense, a technology used in fields such as computer graphics and optics. For example, it is a video rendering technique (also known as "ray tracing") that can generate realistic shadows or light reflections by tracing the behavior of rays. In this system, ray tracing is used in physical calculations to reproduce the propagation characteristics of radio waves in a target space. Specifically, the system models radio waves emitted from an access point as rays of light and calculates the physical behavior of the radio waves, such as their reflection and refraction by surrounding objects, buildings, or terrain. This makes it possible to simulate the propagation path, reach, signal strength, and obstruction by obstacles of the radio waves.

[0016] The information processing system 1 includes an information processing device 10, a robot 20, and a user terminal 30. The information processing device 10 is an information processing device or a server device in the information processing system 1. In this example, the information processing device 10 acquires a result of radio wave intensity measurement in a target space from the robot 20. The result of the radio wave intensity measurement includes numerical data of radio wave intensity (an example of RSSI data) for each measurement point. A specific method for performing the radio wave intensity measurement will be described later. The information processing device 10 generates a first radio wave intensity map of the target space that reflects the result of the radio wave intensity measurement as a map. Furthermore, the information processing device 10 generates a second radio wave intensity map of the target space by supplementing the first radio wave intensity map based on information generated from a data source other than the radio wave intensity measurement. The information processing device 10 acquires measured values ​​of radio wave intensity from mobile terminals in the target space and estimates the position of the mobile terminals using the second radio wave intensity map. The correspondence between the components and functions of the information processing device 10 will be described later.

[0017] The robot 20 is a machine or device in the information processing system 1 that measures radio wave intensity in a target space. The target space S1 represents a space where radio wave intensity measurement is to be performed. At the time of measurement, an access point 800 (also abbreviated as "AP") is installed in the target space S1 as a facility for wireless communication. The access point 800 (an example of wireless communication transmission equipment 80) is a facility for wireless communication. For example, the access point 800 includes a wireless access point, a wireless router (modem), a wireless LAN (Local Area Network) device, a repeater, or various wireless terminals. The radio wave intensity measurement is usually an investigation to measure radio wave intensity at each measurement point on the trajectory traveled by the robot 20 in the target space S1 where the access points 800 are installed.

[0018] In this example, the robot 20 moves (draws a trajectory) along a predetermined path in the target space S1 and measures the radio wave intensity at each measurement point. At the same time, the robot 20 captures images of the target space S1 using a camera mounted on the robot itself. The captured images are used to generate a 3D environmental map showing the target space S1 (details will be described later). The robot 20 transmits various data to the information processing device 10 via the network 9. The robot 20 is composed of various functional elements, and in particular has a moving means, a measuring means, a photographing means, and a communication means for performing the radio wave intensity measurement. The photographing means may include, for example, a monocular camera (wide-angle camera, fisheye camera, omnidirectional camera), a compound eye camera (stereo camera, multi-camera), or an RGB-D camera (depth camera, ToF camera). The robot 20 may also be equipped with at least one of various sensors, in particular, a LiDAR (Light Detection and Ranging) or laser device, an autonomous control function using AI or the like, a microphone, and a speaker (audio device).

[0019] The user terminal 30 (an example of a mobile terminal) is a terminal device (or simply referred to as a "terminal") in the information processing system 1 that estimates the position of the terminal itself within the target space S1. The user terminal 30 includes, for example, a smartphone, a tablet, or a personal computer. In this example, the user terminal 30 is a terminal used by a user in the target space S1. The user accesses the network and estimates (locates) the position of the terminal in the target space by wirelessly communicating with an access point 800 that is connected to the user terminal 30.

[0020] 2 is a diagram illustrating an example of the functional configuration of the information processing system 1. In this embodiment, the information processing device 10 has functional blocks (components) including a first acquisition unit 11, a second acquisition unit 12, a complementation unit 13, an output unit 14, a first generation unit 151, a second generation unit 152, a simulation unit 16, a 3D map generation unit 17, a first position estimation unit 181, a second position estimation unit 182, a storage unit 191, and a control unit 192. In this example, the storage unit 191 stores various data and programs including a database, for example. In this example, the control unit 192 performs various controls.

[0021] The first acquisition unit 11 (an example of an acquisition unit) acquires a first radio wave intensity map of the target space obtained from the result of radio wave intensity measurement in the target space. The first radio wave intensity map is generated by the first generation unit 151 based on the result of the radio wave intensity measurement, i.e., the radio wave intensity measured by the robot 20. The first acquisition unit 11 acquires RSSI data from the robot 20 via the network as a result of the radio wave intensity measurement. The RSSI data is numerical data indicating the radio wave intensity of radio waves.

[0022] The second acquisition unit 12 acquires information from another data source. The second acquisition unit 12 acquires image data captured from the robot 20 simultaneously with radio wave intensity measurement. The second acquisition unit 12 provides the simulation unit 16 with a 3D environment map generated using the image data and various parameters used in the simulation. The second acquisition unit 12 acquires the results of the ray tracing simulation from the simulation unit 16.

[0023] The complementing unit 13 complements the first radio wave intensity map based on information generated from a data source separate from radio wave intensity measurements. The complementing unit 13 acquires the first radio wave intensity map from the first generating unit 151. The complementing unit 13 acquires the results of a ray tracing simulation from the second acquiring unit 12 or the simulation unit 16. Since the first radio wave intensity map contains many locations showing sparse radio wave intensity, the complementing unit 13 complements (expands) the distribution range of radio wave intensity using the results of the simulation.

[0024] The output unit 14 outputs a second radio wave intensity map of the target space, which is obtained by complementing the first radio wave intensity map based on information generated from a data source other than radio wave intensity measurements. The output unit 14 outputs the second radio wave intensity map complemented by the complementing unit 13. The other data source is a simulation of radio wave intensity in the target space based on map information of the target space and ray tracing. In this example, the map information represents environmental conditions or parameters included in the simulation performed by the information processing device 10, and the ray tracing represents a calculation method or an operation method.

[0025] The map information is a 3D environmental map generated using image data representing captured images of the target space. Alternatively, it may be a 3D virtual space (metaverse) that is reproduced on a computer to simulate the target space. The image data is acquired simultaneously with the measurement of radio wave intensity. In this example, the robot 20, for example, uses a photographing means to capture images of each measurement point and transmits the image data to the information processing device 10. The 3D environmental map is generated by the 3D map generation unit 17 based on the acquired image data.

[0026] Ray tracing is performed using the positions of wireless communication transmitting equipment estimated from the results of radio wave intensity measurements. Simulations using these ray tracings are performed by the simulation unit 16.

[0027] The first generation unit 151 generates a first radio wave intensity map of the target space based on the result of the radio wave intensity measurement performed by the robot 20.

[0028] The second generation unit 152 complements the first radio wave intensity map based on information generated from a data source other than the radio wave intensity measurement, and generates a second radio wave intensity map of the target space. The second generation unit 152 can generate the second radio wave intensity map in cooperation with, for example, the complementing unit 13, the simulation unit 16, or the 3D map generation unit 17.

[0029] The simulation unit 16 executes a simulation of radio wave intensity in the target space based on map information and ray tracing of the target space.

[0030] The 3D map generator 17 generates a 3D environmental map using image data representing captured images of the target space. Specifically, the 3D map generator 17 estimates measurement points (the position or movement trajectory of the robot 20) using SLAM (Simultaneous Localization and Mapping) processing, for example, based on the image data captured by the robot 20, and constructs a three-dimensional space using GS (Gaussian Splatting) processing. Based on these technologies, the 3D map generator 17 can reproduce the target space on a computer based on image data captured from multiple viewpoints.

[0031] The first position estimation unit 181 estimates the position of the wireless communication transmission equipment from the result of the radio wave intensity measurement. As an example, the first position estimation unit 181 uses machine learning to estimate the position of the wireless communication transmission equipment. Furthermore, the first position estimation unit 181 performs machine learning using a machine learning model trained using training data including the second radio wave intensity map.

[0032] The second position estimation unit 182 estimates the position of a mobile terminal (for example, user terminal 30) from the measured value of the radio wave intensity at the mobile terminal.

[0033] FIG. 3 is a diagram illustrating an example of the hardware configuration of an information processing device 10. The information processing device 10 is physically configured as a computer including a processor 101, a memory 102, a storage 103, a communication device 104, an input device (optional), a display device (optional), and a bus connecting these. Each of these devices operates using power supplied from a battery (not shown). In the following description, the term "device" can be interpreted as a circuit, a device, a unit, etc. The hardware configuration of the information processing device 10 may be configured to include one or more of the devices shown in FIG. 3, or may be configured without including some of the devices. Furthermore, the information processing device 10 may be configured by communicating with multiple devices each having a different housing.

[0034] Each function of the information processing device 10 is realized by loading predetermined software (programs) onto hardware such as the processor 101, memory 102, etc., so that the processor 101 performs calculations, controls communication via the communication device 104, and controls at least one of reading and writing of data in the memory 102 and storage 103.

[0035] The processor 101 controls the entire computer by running, for example, an operating system. The processor 101 may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. Furthermore, for example, a baseband signal processing unit, a call processing unit, etc. may be realized by the processor 101.

[0036] The processor 101 reads programs (program codes), software modules, data, etc. from at least one of the storage 103 and the communication device 104 into the memory 102, and executes various processes in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described below. The functional blocks of the information processing device 10 may be implemented by a control program stored in the memory 102 and running on the processor 101. Various processes may be executed by one processor 101, or may be executed simultaneously or sequentially by two or more processors 101. The processor 101 may be implemented by one or more chips. The programs may be transmitted to the information processing device 10 via a telecommunications line.

[0037] The memory 102 is a computer-readable recording medium and may be configured by, for example, at least one of a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), etc. The memory 102 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 102 can store executable programs (program codes), software modules, etc. for implementing the method according to this embodiment.

[0038] Storage 103 is a computer-readable recording medium, and may be composed of at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray® disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy disk, a magnetic strip, etc. Storage 103 may also be referred to as an auxiliary storage device.

[0039] The communication device 104 is hardware (transmission / reception device) for performing communication between computers via at least one of a wired network and a wireless network, and is also called, for example, a network device, a network controller, a network card, or a communication module.

[0040] Each device, such as the processor 101 and the memory 102, is connected by a bus for communicating information. The bus may be configured using a single bus, or different buses may be used between each device.

[0041] The information processing device 10 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 101 may be implemented using at least one of these pieces of hardware.

[0042] In this example, the programs stored in the storage 103 include a program for causing a computer to function as a server of the information processing system 1 (hereinafter referred to as a "server program").

[0043] When the processor 101 is executing a server program, the processor 101, the memory 102, the storage 103, and the communication device 104 are examples of functional blocks for operating the information processing device 10. The processor 101 is an example of a first acquisition unit 11, a second acquisition unit 12, a complementation unit 13, a first generation unit 151, a second generation unit 152, a simulation unit 16, a 3D map generation unit 17, a first position estimation unit 181, a second position estimation unit 182, and a control unit 192. At least one of the memory 102 and the storage 103 is an example of a storage unit 191. The communication device 104 is an example of the first acquisition unit 11, the second acquisition unit 12, and the output unit 14.

[0044] Although detailed description will be omitted, the user terminal 30 is a computer having a processor, memory, storage, a communication device, an input device, and an output device, specifically, for example, a smartphone, a tablet terminal, or a personal computer. In this example, the programs stored in the storage of the user terminal 30 include a program (hereinafter referred to as a "client program") for causing the computer to function as a client in the information processing system 1. Up to this point, the configuration of the information processing system 1 has been described. Next, the operation of the information processing system 1 will be described.

[0045] 2.Operation 2-1.Radio wave intensity measurement Fig. 4 is a sequence chart illustrating a method for measuring radio wave intensity in the information processing system 1. The process in Fig. 4 is started when, for example, a user instructs the robot 20 to start measuring radio wave intensity.

[0046] In step S101, the robot 20 starts moving from an arbitrary position in the target space S1. The robot 20 is autonomously controllable and can move freely within the target space S1. At this time, the robot 20 wanders randomly through the target space S1 while avoiding obstacles using various sensors mounted thereon.

[0047] In step S102, the robot 20 receives radio waves from each access point 800 at the measurement point and measures the radio wave intensity. For example, the robot 20 performs the measurement at any frequency (periodically) while moving in the target space S1. The location of the robot 20 at the time of measurement is the measurement point. The location where the measurement is performed is defined as the measurement point in this application. Simultaneously with the radio wave intensity measurement, the robot 20 photographs the target space S1 from various viewpoints using a photographing means such as a camera. The photographed images are recorded as image data in a form corresponding to the measurement results of the radio wave intensity.

[0048] The robot 20 also acquires information about the specifications of the access point 800 corresponding to the received radio waves. The specifications of the access point 800 are, for example, information about the power used in the transmitted radio waves (an example of power information). This information is, for example, included as a signal in the radio waves transmitted from the access point 800. The robot 20 acquires the radio wave power information from the access point 800. Alternatively, the robot 20 may acquire the power information from an external database or the like based on identification information indicating the model or type of the access point 800. In this case, the robot 20 acquires the identification information from the access point 800. The power information is used as a parameter in a simulation using ray tracing (described later). The robot 20 records the various data acquired during the radio wave intensity measurement in association with the power information.

[0049] In step S103, the information processing device 10 acquires various data from the robot 20. The various data include at least RSSI data and image data. In this example, the information processing device 10 acquires the data from the robot 20 via a network or the like. Data communication between the robot 20 and the information processing device 10 may be performed via an access point 800 or the like within the target space S1. Note that the robot 20 may transmit data to the information processing device 10 immediately after measurement or photographing.

[0050] In step S104, the information processing device 10 records various data in a database. Here, the database for managing various data will be described.

[0051] FIG. 5 is a diagram illustrating an example of the measurement database 1001. In this example, the measurement database 1001 includes multiple records. Each record corresponds to a record of each radio wave intensity measurement. Each record includes a measurement ID, a timestamp, an AP (access point) ID, radio wave intensity, image data, and a traveled distance (cumulative). The measurement ID is identification information unique to each radio wave intensity measurement performed by the robot 20 in the target space S1. The timestamp is time information indicating the time when the measurement was performed. The AP (access point) ID is identification information unique to each access point 800 that is the source of the radio waves measured in each measurement. For example, the APID includes a BSSID (Basic Service Set Identifier). The robot 20 uniquely identifies radio waves emitted from the access points 800 installed in the target space S1. This allows the robot 20 to measure the radio wave intensity for each access point 800. The radio wave intensity is the RSSI value (in dBm) of each access point 800. The image data is an image taken at the same time as the measurement. The movement distance (an example of movement information) is a numerical value of the distance accumulated since the robot 20 started moving. Note that the actual measurement database 1001 may also record information about the specifications of each access point 800 (for example, power information).

[0052] Returning to FIG. 4, in step S105, the information processing device 10 performs SLAM processing using image data. In this example, SLAM is, for example, Visual SLAM. According to Visual SLAM, self-location estimation of the robot 20 and creation of an environmental map of the target space S1 are simultaneously performed based on image data captured by a camera. This allows the information processing device 10 to acquire the movement history of the robot 20 in the target space S1, that is, trajectory data that depicts the trajectory on the map. The trajectory includes at least the position coordinates (an example of position data) of measurement points. Note that the information processing device 10 may only perform self-location estimation of the robot 20 in the SLAM processing. Here, self-location estimation by SLAM processing will be described.

[0053] FIG. 6 is a diagram illustrating self-position estimation using SLAM processing. Trajectory data 2001 is data on an environmental map including the position of the robot 20 during measurement and the trajectory of the movement result. FIG. 6 shows a viewpoint viewed perpendicularly from the ceiling to the floor surface in the target space S1. The robot 20 is an icon of the robot body during movement (measurement). Trajectory OB1 represents the trajectory of movement from when the robot 20 starts moving until a certain time. Point X1 is the measurement point where the robot 20 performed measurement. Note that point X1 is drawn as a dot in the figure. Note that the information processing device 10 may generate the trajectory data 2001 based on movement information such as the movement speed, movement distance, or movement direction of the robot 20.

[0054] Returning to FIG. 4, in step S106, the information processing device 10 transmits the position data to the robot 20. This allows the robot 20 to know its own position. Note that the estimation of its own position in step S105 may be performed by the robot 20 itself. Furthermore, the information processing device 10 may output data on the position of the robot 20 and the environmental map to an external device as necessary. This allows a user using the system to know the movement state of the robot 20 in real time.

[0055] Finally, the robot 20 utilizes the position data to return to the position from which it started moving (initial position). The robot 20 ends the measurement when it returns to the initial position and notifies the information processing device 10 of this.

[0056] As described above, the information processing system 1 can collect various data without spending time and effort by using the robot 20. Next, a method for generating a radio wave intensity map by the information processing device 10 will be described.

[0057] 2-2. How to generate a radio wave intensity map FIG. 7 is a flowchart illustrating a method for generating a radio wave intensity map in the information processing system 1. The following flow represents processing executed mainly by the information processing device 10. The information processing device 10 starts processing when triggered by the completion of radio wave intensity measurement by the robot 20 in section 2-1. In step S1, the information processing device 10 generates a first radio wave intensity map of the target space S1 based on the results of the radio wave intensity measurement by the robot 20. For example, the information processing device 10 generates a heat map in which the color intensity is adjusted according to the numerical value of the radio wave intensity. The information processing device 10 generates a first radio wave intensity map for each access point. Here, the first radio wave intensity map will be described.

[0058] FIG. 8 is a diagram illustrating a first radio wave intensity map. In this example, maps M11 and M12 are examples of the first radio wave intensity maps of access point 801 and access point 802, respectively. Maps M11 and M12 represent a viewpoint of the target space S1 looking down from the ceiling to the floor. The environmental map base of the first radio wave intensity map is, for example, map data acquired by SLAM processing. In this example, trajectory OB1 represents the trajectory of movement of the robot 20. Point X1 represents one of the measurement points on the trajectory. The radio wave intensity at a point on the map is calculated from the result of radio wave intensity measurement. For example, the radio wave intensity in the area between each measurement point is calculated by interpolation. In the diagram of map M11, only the radio wave intensity at the measurement point (e.g., point X1) is indicated by a marker, and the radio wave intensity calculated by interpolation between the measurement value points is omitted. The marker is an object that represents the strength of the radio wave intensity with a shade of color. In this example, if the radio wave strength value at point X1 is -40 dBm, a marker of a color corresponding to this value is reflected on the map. The range of the marker (for example, a range with a radius of 0.5 m to 1 m from point X1) is predefined as a uniform radio wave strength that extends to the vicinity of the measurement point. Therefore, the range of the marker may be set in any way (it may be small or large). In this way, the information processing device 10 generates a map for each access point 800 that reflects the radio wave strength of each measurement point. The data of these first radio wave strength maps is recorded in a database in a state where the correspondence with the access point 800 can be identified. Note that it is possible to estimate the radio wave strength by interpolation even at locations far from the measurement point. In other words, even if the measurement points are sparse, it is mathematically possible to interpolate the radio wave strength in all areas on the map. However, for simplicity's sake, FIG. 8 shows the radio wave strength only around some measurement points.

[0059] Returning to FIG. 7 , in step S2, the information processing device 10 identifies or estimates the position of the access point 800. In one example, the information processing device 10 estimates the position of the access point 800 using a first signal strength map. For example, the information processing device 10 performs position estimation using a machine learning model ML1 that has learned the correspondence between signal strength and distance from past training data. The machine learning model ML1 has a function of accepting, as an explanatory variable, a first signal strength map including actual measured values ​​of signal strength at each measurement point. The machine learning model ML1 also has a function of outputting, as a target variable, a first signal strength map that reflects the position of the access point 800. The machine learning model ML1 is, for example, a model that has been trained in advance using past signal strength maps as training data. As an example, the machine learning model ML1 includes machine learning models such as K-nearest neighbors, neural networks (NN), support vector machines (SVM), or random forest. Note that the machine learning model ML1 may be configured using, for example, a relatively simple algorithm based on triangulation.

[0060] Referring again to Figure 8, in this example, maps M11 and M12 reflect the position of access point 801 or access point 802, respectively. The position of access point 800 on the map is displayed as an identifiable object (a "star-shaped plot" in the figure). Note that location information of access point 800, etc., may be included in the first radio wave intensity map as metadata.

[0061] Returning to FIG. 7, in step S3, the information processing device 10 constructs a three-dimensional space model by GS processing using the image data. The GS processing is performed based on the image data and position coordinate data. The position coordinate data may be the same data as the position information estimated by SLAM processing. The information processing device 10 records the three-dimensional space model constructed on the computer in a database as a 3D environmental map.

[0062] In step S4, the information processing device 10 performs a ray tracing simulation using the 3D environmental map. The information processing device 10 divides the process into the following two stages. In the first stage, the information processing device 10 makes the access point 800 appear at a corresponding position on the 3D environmental map using the correspondence between the first radio wave intensity map and the 3D environmental map. This process may be performed when constructing a three-dimensional space model. In the second stage, the information processing device 10 simulates the physical behavior (propagation prediction) of radio waves emitted from the access point 800. In the ray tracing simulation, radio waves emitted by the access point 800 are tracked in three-dimensional space. Here, the ray tracing simulation will be described.

[0063] FIG. 9 is a diagram illustrating a radio wave propagation simulation in a three-dimensional space V1. FIG. 9 schematically illustrates a three-dimensional space model on a computer. In this example, the three-dimensional space V1 is a three-dimensional space model (an example of a 3D environmental map) showing a target space S1 constructed on a computer. In the three-dimensional space V1, the floor, walls, ceiling, and objects in the target space S1 are reproduced as a three-dimensional model. In the figure, the objects in the space are depicted in a simplified form. The information processing device 10 can reproduce objects in detail based on image data. Alternatively, not only the shape of each object but also its material, color, and texture (elasticity) may be reproduced. In other words, the information processing device 10 may set any parameters in the three-dimensional space model as long as the parameters are information for simulating the characteristics of an object with respect to radio waves (such as reflection, absorption, or diffraction). An access point 801 is a source of radio waves. A radio wave R1 is one of the radio waves emitted from the access point 801. The propagation behavior of the radio wave R1 is depicted as, for example, a ray. The information processing device 10 uses ray tracing to simulate the behavior of each radio wave emitted in various directions from the access point 801. This allows the information processing device 10 to calculate the radio wave intensity of the radio waves emitted from the access point 801 in the three-dimensional space V1. In the figure, the radio wave intensity in the space is visualized as a heat map.

[0064] Returning to Fig. 7, in step S5, the information processing device 10 generates a second radio wave intensity map based on the simulation results. The second radio wave intensity map is a radio wave intensity map supplemented by a simulation of radio wave intensity in the target space S1. Here, the second radio wave intensity map will be described.

[0065] FIG. 10 is a diagram illustrating an example of a second radio wave intensity map. In this example, maps M21 and M22 are examples of the second radio wave intensity map for each access point 800. Maps M21 and M22 share the same environmental map as the first radio wave intensity map described above. Therefore, like the first radio wave intensity map, maps M21 and M22 represent the target space S1 from a perspective looking down from the ceiling to the floor. For example, map M21 is a map showing the radio wave intensity of the access point 801 in the target space S1. In the figure, the strength of the radio wave intensity is expressed as a heat map. The heat map is complemented based on the results of a simulation using ray tracing by the information processing device 10. Here, a method for complementing the first radio wave intensity map will be described.

[0066] When the first radio wave intensity map is complemented, the actual measured values ​​obtained by measurement are used for the radio wave intensity around the measurement point. For example, in the first radio wave intensity map, it is expected that the radio wave intensity is somewhat accurate within a predetermined range including the path of the robot 20. Therefore, the information processing device 10 excludes from the complementation the area swept by a circle centered on the measurement point (e.g., a range with a radius of 0.5 m to 1 m from the measurement point X1) when the robot 20 moves along the path of the robot 20. In other words, since the second radio wave intensity map complements the area outside the periphery of the path of the robot 20, a heat map obtained by the simulation results is pasted as shown in FIG. 10. The information processing device 10 performs the above-mentioned process, so-called "pasting" on the first radio wave intensity map to obtain the complemented second radio wave intensity map. Therefore, the visual image of the second radio wave intensity map is as shown in FIG. 10. In order to simplify the drawing, FIG. 10 does not show the area swept along the path of the robot 20 by a circle centered on the measurement point, but only shows the circle around the measurement point.

[0067] Referring again to FIG. 8, the first radio wave intensity map obtained solely from the results of radio wave intensity measurements appears to have a certain degree of accuracy in the radio wave intensity around the measurement point, but the radio wave intensity calculated by interpolation in areas away from the measurement point may contain significant errors. In contrast, the second radio wave intensity map supplemented by a simulation using ray tracing generates a heat map that covers the entire range of the target space S1. After generating the second radio wave intensity map, the information processing device 10 associates it with each target space or each access point and records it in a database. The supplemented second radio wave intensity map is used, for example, to estimate the position of the user terminal 30.

[0068] As a result, the information processing device 10 can create a highly accurate and convenient radio wave intensity map. Unlike the first radio wave intensity map, the second radio wave intensity map can cover, by ray tracing, areas that cannot be covered by a map created by simply measuring radio wave intensity alone. Since the second radio wave intensity map comprehensively covers radio wave intensity in a space, it is used as a highly accurate map for indoor positioning, etc. Next, a method for estimating the position of a mobile terminal using the second radio wave intensity map will be described.

[0069] 2-3. Mobile terminal location estimation method FIG. 11 is a sequence chart illustrating a method for estimating the position of a mobile terminal in the information processing system 1. Here, a process for estimating the position of a user terminal 30 in a target space S1 using a second radio wave intensity map (indoor positioning) will be described. In this system, the information processing device 10 has a dedicated application (hereinafter abbreviated as "dedicated app") for estimating the position of a mobile terminal. The dedicated app is, for example, a map app, a weather app, or a location information app. The same application is pre-installed in the user terminal 30, and the user can know the position of the terminal via the dedicated app. Hereinafter, the processes of the information processing device 10 and the user terminal 30 are executed via this dedicated app.

[0070] In step S201, the user terminal 30 measures the radio wave strength of the access point 800. For example, if the user terminal 30 is communicating wirelessly with an access point installed in the target space S1, the user terminal 30 measures the radio wave strength of the access point with which it is communicating. Alternatively, the user terminal 30 selectively measures the radio wave strength of the access point received by its own antenna. In this case, the user terminal 30 uniquely identifies the received radio wave. The user terminal 30 periodically measures the radio wave strength of each access point 800 and records each measurement in a database.

[0071] In step S202, the information processing device 10 acquires RSSI data from the user terminal 30. This RSSI data includes the radio wave intensity value of the radio wave received from each access point 800. The user terminal 30 transmits to the information processing device 10 data in which the identification information of the access point 800, the measurement time, and the radio wave intensity value are associated with each other.

[0072] In step S203, the information processing device 10 reads out the second radio wave intensity map from the database. The information processing device 10 identifies the second radio wave intensity map of the target space S1 from the identification information included in the RSSI data of the user terminal 30, etc.

[0073] In step S204, the information processing device 10 estimates the position of the user terminal 30 using the second radio wave intensity map. The information processing device 10 estimates the position of the user terminal 30 using, for example, a machine learning model (hereinafter referred to as "machine learning model ML2"), triangulation, or various programs. The machine learning model ML2 receives input of the RSSI data of the user terminal 30 and the second radio wave intensity map as explanatory variables. The machine learning model ML2 outputs position data indicating the position of the user terminal 30 as a response variable. The position data includes, for example, a position on a 3D environmental map, a position on the second radio wave intensity map, or coordinate information.

[0074] In step S205, the information processing device 10 outputs the position data to the user terminal 30. The information processing device 10 reflects the position of the user terminal 30 on a map using, for example, a dedicated app. The user terminal 30 acquires its own position data via the dedicated app. The user terminal 30 presents the position of the terminal on the map to the user according to various UIs (User Interfaces). This allows the user to confirm the position of the terminal in the target space S1.

[0075] As a result, the information processing device 10 can estimate the location of the terminal with high accuracy using the second radio wave intensity map. This allows the user to know the exact location of the terminal even if the terminal is located in an indoor facility where GPS or the like does not function well.

[0076] 3. Variations The present invention is not limited to the above-described embodiment, and various modifications are possible. Some modifications will be described below. Two or more of the following features may be combined and applied.

[0077] (1) Information Processing System 1 The hardware configuration and network configuration of the information processing system 1 are not limited to those exemplified in the embodiment. The information processing system 1 may have any hardware configuration and network configuration as long as the required functions can be realized. For example, multiple physical devices may cooperate to function as the information processing system 1. For example, the information processing system 1 may include a receiver installed in the target space. The receiver measures the radio wave intensity of the radio waves emitted by the wireless communication transmission equipment 80. The information processing device 10 acquires location data and RSSI data from the receiver. The information processing device 10 may generate a first radio wave intensity map based on the acquired various data.

[0078] (2) Information processing device 10 The correspondence between the functional elements of the information processing device 10 and the hardware is not limited to that exemplified in the embodiment. For example, in the embodiment, at least some of the functions described as being implemented in the information processing device 10 may be implemented in another device or system, and conversely, at least some of the functions described as being implemented in another device or system may be implemented in the information processing device 10. For example, the robot 20 may perform processing performed by the information processing device 10. The robot 20 may perform SLAM processing using image data. The robot 20 may generate a first radio wave intensity map using map data obtained from the SLAM processing and the results of radio wave intensity measurement.

[0079] (3) Robot 20 The robot 20 is not limited to the example shown in the embodiment. The robot 20 may have any hardware configuration as long as it can realize the required functions and operations. The robot 20 may have any measurement means. For example, the robot 20 may measure the radio wave intensity of a smart device (e.g., a smart home appliance or a smart speaker) in addition to the access point 800. In this case, the robot 20 may perform measurements according to wireless communication standards other than Wi-Fi, such as Bluetooth, Zigbee, or Matter. Any communication standard may be used for the measurement. Note that the smart device to be measured is preferably a device fixed to the target space (e.g., a smart light bulb or a smart lock). The robot 20 may have any imaging means. The imaging means may be, for example, an analog camera, a stereo camera, a depth camera (sensor), a thermography camera, a motion camera (sensor), a network (IP) camera, a cloud camera, or an infrared camera. Furthermore, the data used for estimating the position of the robot 20 and creating an environmental map does not have to be image data, and may be, for example, point cloud data obtained from a LiDAR. Furthermore, the information processing system 1 may be one in which a human being uses a measuring device to obtain the required measurement results or data, without using the robot 20. In this case, the measuring device is a device that measures radio wave intensity and a device that obtains data for creating a 3D environmental map. These may be separate devices or a single device.

[0080] (4) User terminal 30 The user terminal 30 is not limited to the example shown in the embodiment. The user terminal 30 may have any hardware configuration as long as it can realize the required functions and operations. The user terminal 30 may be a measuring device that measures the radio wave intensity of the access point 800. In this example, the user terminal 30 has a function of transmitting the measured RSSI data to the information processing device 10. The user terminal 30 has a function of acquiring the terminal's location data from the information processing device 10 and presenting it to the user.

[0081] (5) Wireless communication transmission equipment 80 (access point 800) The wireless communication transmission equipment 80 is not limited to the example shown in the embodiment. The wireless communication transmission equipment 80 may have any hardware configuration as long as it can realize the required functions and operations. The wireless communication transmission equipment 80 may be a wireless communication base station or the like. The wireless communication transmission equipment 80 may be a small cell, microcell, picocell (nanocell), femtocell, or the like installed indoors. The wireless communication transmission equipment 80 may have a function to connect with the robot 20 and the user terminal 30 via wireless communication.

[0082] (6) Radio wave intensity measurement method The sequence chart shown in FIG. 4 merely illustrates one example of the operations, and the radio wave intensity measurement method in the information processing system 1 is not limited to this. Some of the illustrated operations may be changed or omitted, the order may be changed, or new operations may be added. In step S105, the robot 20 may acquire any data. In addition to image data, the robot 20 may acquire point cloud data from LiDAR or distance data from a depth camera. In step S105, the information processing device 10 may perform Visual SLAM, LiDAR SLAM, Depth SLAM, or the like. In addition, in step S105, the information processing device 10 may generate a 3D environmental map to be used in simulations. The information processing device 10 may modify or update the 3D environmental map based on image data acquired for each measurement.

[0083] (7) How to generate a radio wave intensity map The flowchart shown in FIG. 7 merely illustrates one example of the operation, and the method for generating a radio wave intensity map in the information processing system 1 is not limited to this. Some of the illustrated operations may be changed or omitted, the order may be changed, or new operations may be added. The processes in steps S1 to S2 may be performed simultaneously. In step S1, the information processing device 10 may generate a first radio wave intensity map including position information of the target space or the robot 20. The information processing device 10 may generate a first radio wave intensity map integrated for multiple access points 800, rather than for each access point 800. In step S2, the information processing device 10 may estimate the position of the access point 800 in any way. In step S3, the information processing device 10 may create a 3D environment map at any timing. In step S4, the information processing device 10 may set any parameters for the ray tracing simulation. For example, the layout of the target space S1, the physical characteristics of objects in the space, or radio wave interference from multiple wireless communication transmission facilities 80 may be arbitrarily set as parameters.

[0084] Furthermore, the method by which the information processing device 10 acquires information about the specifications of the access point 800 and the method by which the information processing device 10 identifies the position of the access point 800 are not limited to those exemplified in the embodiment. Information about the position and specifications of the access point 800 may be prepared in advance by, for example, a facility manager of the symmetric space, and the information processing device 10 may acquire this information from the facility manager. In other words, the information processing device 10 may acquire at least one of the information about the specifications of the access point 800 and the information about the position of the access point 800 without relying on the access point 800 and the robot 20.

[0085] Furthermore, the specific method by which the information processing device 10 complements the first radio wave intensity map with the simulation results is not limited to the example given in the embodiment. For example, the information processing device 10 compares the radio wave intensity at a predetermined point (e.g., a predetermined grid point) on the first radio wave intensity map with the simulated radio wave intensity at that point. The information processing device 10 may complement the first radio wave intensity map by overwriting the first radio wave intensity map with the simulation results for an area where the difference exceeds a predetermined threshold.

[0086] (8) Method for estimating the location of a mobile terminal The sequence chart shown in FIG. 11 merely shows one example of the operations, and the method for estimating the position of a mobile terminal in the information processing system 1 is not limited to this. Some of the operations shown in the figure may be changed or omitted, the order may be changed, or new operations may be added. In step S205, the information processing device 10 may transmit coordinate information, height information, building floor information, or the like to the user terminal 30 as position data. The user terminal 30 may present information to the user according to data acquired from the information processing device 10 regarding the position of the terminal. This allows the user to know the detailed position of the user terminal 30 (for example, the basement, area, or building floor), etc.

[0087] (9) Database The databases or data of the information processing system 1 shown in Figures 5, 6, 8, and 10 are not limited to those exemplified in the embodiments. In this example, any data may be recorded in the database. For example, the actual measurement database 1001 may include movement information of the robot 20. The trajectory data 2001 may include coordinate information and time information of measurement points. The first radio wave intensity map and the second radio wave intensity map may be 3D environmental maps or 2D environmental maps.

[0088] (10) Alternative data sources The other data sources are not limited to those exemplified in the embodiments. The other data sources may be the results of various simulations. Simulations include simulations using a physics engine, virtual simulations using a virtual model, and emulation. Ray tracing may be adjusted to a method suitable for predicting radio wave propagation. For example, physical characteristics according to the transmission power or frequency of radio waves from the access point 800 may be set as parameters. The behavior of radio waves obtained by ray tracing may or may not be visualized on a computer.

[0089] (11) Other The various programs executed by processor 101 may be provided by downloading via a network such as the Internet, or may be provided in a state recorded on a computer-readable non-transitory recording medium such as a DVD-ROM. Each processor may be, for example, a CPU, an MPU (Micro Processing Unit), or a GPU (Graphics Processing Unit).

[0090] The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wires, wirelessly, etc.) and these multiple devices. The functional block may also be realized by combining the single device or multiple devices with software.

[0091] Functions include, but are not limited to, judgment, determination, assessment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, election, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.

[0092] For example, the information processing device 10 according to an embodiment of the present disclosure may function as a computer that performs the processing of the present disclosure.

[0093] Each aspect / embodiment described in the present disclosure may be applied to at least one of systems using LTE (Long Term Evolution), LTE-Advanced (LTE-A), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), FRA (Future Radio Access), NR (New Radio), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark), IEEE 802.20, UWB (Ultra-Wideband), Bluetooth (registered trademark), or other appropriate systems, and next-generation systems extended based on these. Furthermore, a combination of multiple systems (e.g., a combination of at least one of LTE and LTE-A with 5G) may also be applied.

[0094] The order of the procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless it is consistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0095] Input and output information may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be sent to another device.

[0096] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0097] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0098] Software, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise, shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc. Additionally, software, instructions, information, etc. may be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then such wired and / or wireless technologies are included within the definition of a transmission medium.

[0099] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof. Note that terms described in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings.

[0100] Furthermore, the information, parameters, etc. described in this disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information.

[0101] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0102] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0103] The "unit" in the configuration of each of the above devices may be replaced with "means," "circuit," "device," or the like.

[0104] When used in this disclosure, the terms "include," "including," and variations thereof are intended to be inclusive, similar to the term "comprising." Furthermore, when used in this disclosure, the term "or" is not intended to be an exclusive or.

[0105] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0106] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different." [Explanation of symbols]

[0107] 1...information processing system, 10...information processing device, 20...robot, 30...user terminal, 80...wireless communication transmission equipment, 800, 801, 802...access points, 9...network, 11...first acquisition unit, 12...second acquisition unit, 13...completion unit, 14...output unit, 151...first generation unit, 152...second generation unit, 16...simulation unit, 17...3D map generation unit, 181...first position estimation unit, 182...second position estimation unit, 191...storage unit, 192...control unit, 101...processor, 102...memory, 103...storage, 104...communication device, 1001...actual measurement database, 2001...trajectory data

Claims

1. an acquisition unit that acquires a first radio wave intensity map of the target space obtained from a result of measuring radio wave intensity in the target space; an output unit that outputs a second radio wave intensity map of the target space obtained by complementing the first radio wave intensity map based on information generated from a data source other than the radio wave intensity measurement; An information processing device having the above.

2. The other data source is a simulation of radio wave intensity in the target space based on map information and ray tracing of the target space. The information processing device according to claim 1 .

3. The map information is a 3D environmental map generated using image data representing an image obtained by photographing the target space. The information processing device according to claim 2 .

4. The image data is acquired simultaneously with the radio wave intensity measurement. The information processing device according to claim 3 .

5. The ray tracing is performed using the location of the wireless communication transmitting equipment estimated from the results of the radio wave intensity measurements. The information processing device according to claim 2 .

6. a first position estimation unit that estimates the position of the wireless communication transmitting equipment from the result of the radio wave intensity measurement; The information processing device according to claim 5 .

7. The first location estimation unit uses machine learning to estimate the location of the wireless communication transmission equipment. The information processing device according to claim 6 .

8. The first position estimation unit performs the machine learning using a machine learning model trained using teacher data including the second radio wave intensity map. The information processing device according to claim 7 .

9. A second position estimation unit is provided to estimate the position of the mobile terminal from the measured value of the radio wave intensity at the mobile terminal. The information processing device according to claim 1 .

10. acquiring a first radio wave intensity map of the target space obtained from a result of measuring radio wave intensity in the target space; a step of outputting a second radio wave intensity map of the target space, the second radio wave intensity map being obtained by supplementing the first radio wave intensity map based on information generated from a data source other than the radio wave intensity measurement; A radio wave intensity map interpolation method having the following.

Images