JP2013257238A - Information processing system, and information processing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide technology for estimating an indoor movement locus with high accuracy when environmental positioning is combined with autonomous positioning.SOLUTION: Autonomous positioning processing is executed by using pieces of data of an acceleration sensor and a gyro sensor to generate a plurality of locus candidates, and the plurality of locus candidates are evaluated to generate one optimal locus as target locus information. Here, a processor executes comparison processing for comparing signal intensity data at a position on locus candidates within predetermined distance from a base station with a predetermined threshold for each of the plurality of locus candidates, and locus determination processing for determining the target locus information from the plurality of locus candidates on the basis of a result of the comparison processing when the optimal locus is determined.

Description

  The present invention relates to an information processing system and an information processing method, and, for example, relates to a technique for generating trajectory information of a target moving in a building.

  As represented by car navigation, positioning methods using GPS satellites are widely used, and highly accurate position detection is realized. However, since the GPS satellite signal cannot be received indoors or in the shade, position detection using the GPS satellite cannot be performed. In the case of a car, most of the routes are outdoors, so the travel time in the shade is short and the position is temporarily disabled. On the other hand, pedestrians often move indoors and underground, and it is difficult to detect the position of pedestrians continuously with GPS alone. Therefore, it is necessary to detect the position of a pedestrian indoors (indoor positioning) using a positioning technique other than GPS indoors or underground.

  There are two types of indoor positioning technology: environmental positioning in which positioning devices are installed on the environment side, and autonomous positioning in which no positioning devices are installed on the environment side. In environmental positioning, the distance from a positioning device (base station) is measured by using the difference in radio signal reception intensity and reception time, and absolute coordinates are measured indoors. To obtain sufficient accuracy, A huge equipment cost is required. On the other hand, in autonomous positioning, the user's walking speed and walking direction are estimated at low cost using an acceleration sensor, a magnetic bearing sensor, a gyro sensor, etc. Need to be estimated.

  As for autonomous positioning, for example, as shown in Patent Documents 1 and 2, the data of various sensors such as an acceleration sensor and a gyro sensor are used to analyze how fast and in what direction a pedestrian is moving. There is a technique for estimating the position of a pedestrian. Thereby, the position of a pedestrian can be detected even indoors.

Japanese Patent Laid-Open No. 2011-237452 JP 2011-017610 A JP 2007-101526 A

P. Bahl and VN Padmanabhan, “RADAR: an in-building RF-based user location and tracking system,” in Proceedings of 19thAnnual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM '00), vol. 2, pp. 775 -784, Tel Aviv, Israel, March 2000.

  By the way, recently, in order to obtain a sufficient positioning accuracy with a low equipment cost, an indoor positioning method that combines environmental positioning and autonomous positioning has been attracting attention.

  However, a radio signal from a base station installed on the environment side has a large fluctuation in attenuation with respect to the propagation distance in an actual environment. On the other hand, in autonomous positioning, there is an error due to the environment of each sensor. For example, the output data of a sensor (particularly a gyro sensor) may include noise (direction noise), and the position of the pedestrian may not be accurately measured. The amount of noise of the gyro sensor depends on the climate (temperature, humidity, atmospheric pressure, etc.) of the day. The environmental conditions vary depending on the building and underground. Therefore, various external factors that generate noise cannot be understood unless you actually enter the building or basement.

  Therefore, when environmental positioning and autonomous positioning are combined, there is a problem in that both errors in the radio signal and errors in the sensor are synergistic and the error increases.

  The present invention has been made in view of such a situation, and provides a technique for estimating an indoor movement trajectory with high accuracy when environmental positioning and autonomous positioning are combined.

  In order to solve the above problems, in the present invention, the processor executes autonomous positioning processing using data of the acceleration sensor and the gyro sensor, generates a plurality of trajectory candidates, evaluates the plurality of trajectory candidates, and performs 1 One optimal trajectory is generated as target trajectory information. Here, when determining the optimum trajectory, the processor compares the signal intensity data at a position on the trajectory candidate within a predetermined distance from the base station with a predetermined threshold for each of the plural trajectory candidates. And a trajectory determination process for determining target trajectory information from a plurality of trajectory candidates based on the result of the comparison process.

  According to the present invention, an indoor movement trajectory can be estimated with high accuracy by combining environmental positioning and autonomous positioning.

  Further features related to the present invention will become apparent from the description of the present specification and the accompanying drawings. The embodiments of the present invention can be achieved and realized by elements and combinations of various elements and the following detailed description and appended claims.

  The descriptions in this specification are merely exemplary, and are not intended to limit the scope of the claims or the application of the invention in any way.

1 is a diagram illustrating a schematic configuration example of a mobile terminal device (also referred to as an information processing system or a walking trajectory estimation system) 100 according to a first embodiment of the present invention. It is a figure which shows the structural example of the sensor data used by embodiment of this invention. It is a figure which shows the structural example of the base station coordinate data used by embodiment of this invention. It is a figure for demonstrating the outline | summary of the walk locus | trajectory (movement locus | trajectory) estimation process by 1st Embodiment. It is a figure for demonstrating the detail of the pedestrian autonomous positioning process. 5 is a flowchart for explaining details of a walking locus evaluation process 402; It is a figure for demonstrating the specific example of a walk locus | trajectory evaluation process. It is a figure which shows the schematic structural example of the mobile terminal device (it is also called an information processing system, a walking locus estimation system) 100 by the 2nd Embodiment of this invention. It is a figure which shows the structural example of the building data. It is a figure for demonstrating the outline | summary of the walk locus | trajectory (movement locus | trajectory) estimation process by 2nd Embodiment. It is a flowchart for demonstrating the detail of the walk locus | trajectory evaluation process by 2nd Embodiment. It is a figure for demonstrating the specific example of the walk locus | trajectory evaluation process by 2nd Embodiment. It is a figure which shows the schematic structural example of the mobile terminal device (it is also called an information processing system, a walking locus estimation system) 100 by the 3rd Embodiment of this invention. It is a figure which shows the structural example of the indoor radio signal intensity distribution data used by 2nd Embodiment. It is a figure for demonstrating the outline | summary of the walk locus | trajectory (movement locus | trajectory) estimation process by 3rd Embodiment. It is a figure which shows the schematic structural example of the mobile terminal device (it is also called an information processing system, a walking locus estimation system) 100 by the 4th Embodiment of this invention. It is a figure which shows the schematic structural example of the information processing system (it is also called a walking locus estimation system) by the 5th Embodiment of this invention. FIG. 11 is a diagram showing a configuration example of movement locus data 1800 stored in a movement locus data storage area 2025. It is a figure which shows the example of schematic structure of the information processing system (it is also called a walking locus estimation system) by the 6th Embodiment of this invention. It is a figure which shows the structural example of building data. It is a figure which shows the structural example of operation history data. It is a figure which shows the schematic structural example of the information processing system (it is also called a walking locus estimation system) by the 7th Embodiment of this invention. It is a flowchart for demonstrating the content of the user induction | guidance | derivation process which guide | induces a user to a predetermined | prescribed place when the reliability of received radio wave intensity is low. It is a figure which shows the structural example of the building data containing the information of a landmark. It is a figure which shows the schematic structural example of the information processing system (it is also called a walking locus estimation system) by the 8th Embodiment of this invention. It is a figure which shows schematic structure of the information processing system by 9th Embodiment. It is a figure which shows the structure which implement | achieved the information processing system by 9th Embodiment with the mobile terminal device 100 and the server 200. FIG. It is a figure which shows the structural example of radio field intensity data. It is a conceptual diagram for demonstrating the process of associating (associating) an estimated walking trajectory (movement trajectory) of a pedestrian with measured radio wave intensity data by the data integration program 1065. It is a figure which shows the example of a basic display of the link | linking information of locus | trajectory data and electromagnetic wave intensity data. It is a figure which shows the example which can expand and display the detailed link information of locus data and radio wave intensity data.

  Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the accompanying drawings, functionally identical elements may be denoted by the same numbers. The attached drawings show specific embodiments and implementation examples based on the principle of the present invention, but these are for understanding the present invention and are not intended to limit the present invention. Not used.

  This embodiment has been described in sufficient detail for those skilled in the art to practice the present invention, but other implementations and configurations are possible without departing from the scope and spirit of the technical idea of the present invention. It is necessary to understand that the configuration and structure can be changed and various elements can be replaced. Therefore, the following description should not be interpreted as being limited to this.

  Furthermore, the embodiment of the present invention may be implemented by software running on a general-purpose computer, or may be implemented by dedicated hardware or a combination of software and hardware.

  In the following embodiment, the processor (control unit) is configured to execute various programs stored in the memory. However, various processing units corresponding to the respective programs share the respective processes to obtain final data. May be generated and output.

  In the following description, each information of the present invention will be described in a “table” format. However, the information does not necessarily have to be expressed in a data structure by a table, such as a data structure such as a list, a DB, a queue, or the like. It may be expressed as Therefore, “table”, “list”, “DB”, “queue”, etc. may be simply referred to as “information” to indicate that they do not depend on the data structure.

  In addition, when explaining the contents of each information, the expressions “identification information”, “identifier”, “name”, “name”, “ID” can be used, and these can be replaced with each other. It is.

  In the embodiment described below, an indoor pedestrian is targeted, but not only a pedestrian but also a vehicle moving indoors may be targeted.

(1) First Embodiment <Configuration of Mobile Terminal Device>
FIG. 1 is a diagram illustrating a schematic configuration example of a mobile terminal device (also referred to as an information processing system or a walking trajectory estimation system) 100 according to the first embodiment of the present invention. The mobile terminal device 100 includes an acceleration sensor 101 that measures in real time the acceleration that changes as the pedestrian moves, a gyro sensor 102 that measures in real time the angular velocity that changes as the pedestrian moves, Auxiliary storage device 104 having a radio signal receiver 103 that receives a radio signal (such as a wireless LAN or indoor GPS) from a radio base station installed in), a sensor data storage area 1041, and a base station coordinate data storage area 1042. A processor 105 that executes various programs, and a memory 106 that stores various programs.

The sensor data storage area 1041 includes pedestrian acceleration data (gravity g and acceleration m / s ² during movement, and only gravity g when there is no change in speed) and pedestrians measured sequentially. Angular velocity data (degree / s) and signal intensity data from each base station are stored in association with the acquisition time. The base station coordinate data storage area 1042 stores indoor coordinate information for specifying the location where the base station is installed (see FIG. 3).

  The memory 106 uses the sensor data acquisition program 1061 that acquires measurement data from the acceleration sensor 101 and the gyro sensor 102 and stores the measurement data in the sensor data storage area 1041 of the auxiliary storage device 104 in association with the measurement time, and various sensor signals. Indoor positioning program 1062 that performs indoor positioning and generates a plurality of indoor walking trajectory candidates (in the case of non-walking movement, it can also be referred to as a moving trajectory), and a plurality of walking trajectory candidates are evaluated and optimized. A walking trajectory evaluation program 1063 for acquiring a correct walking trajectory.

<Example of sensor data configuration>
FIG. 2 is a diagram illustrating a configuration example of sensor data used in the embodiment of the present invention. 2A shows an example of acceleration sensor data, FIG. 2B shows an example of gyro sensor data, and FIG. 2C shows an example of wireless reception signal strength data. These sensor data are accumulated in each of the corresponding storage areas as they move.

  In the embodiment of the present invention, the acceleration sensor 101 and the gyro sensor 102 are configured to detect accelerations or angular velocities in three axis (XYZ) directions. Therefore, as shown in FIGS. 2A and 2B, acceleration and angular velocity are detected in each of the XYZ directions corresponding to the measurement times T1, T2, T3,... And accumulated in the sensor data storage area 1041. It will be.

  The wireless signal strength data (FIG. 2C) is information indicating the signal strength received from each base station at each measurement time T1, T2,. Since the radio signal receiver 103 may receive signals from a plurality of base stations at the same time, only a single signal strength is shown in FIG. 2C. Signal strength data is acquired and managed.

  The direction of each sensor in the embodiment of the present invention is based on the mobile terminal device 100. For example, the XY plane is set on the wide surface of the mobile terminal device 100, and the Z direction is defined in a direction perpendicular to the XY plane. The Therefore, the direction of the axis may be different from the normal XYZ space.

<Configuration example of base station coordinate data>
FIG. 3 is a diagram showing a configuration example of base station coordinate data used in the embodiment of the present invention. The mobile terminal device 100 has coordinate data of base stations installed corresponding to each of a plurality of buildings. When determining the indoor walking trajectory, the processor 105 obtains base station coordinate data corresponding to the building specified by the GPS signal and executes each process in order to determine which building has entered by GPS (not shown). It will be.

  The base station coordinate data 300 shown in FIG. 3 includes a base station ID 301 for identifying and specifying a base station installed in a building, a coordinate X302, a coordinate Y303, and the number of floors where the corresponding base station is installed. And the floor (Z) information 304 indicating the configuration information.

<Process overview>
FIG. 4 is a diagram for explaining an outline of the walking trajectory (movement trajectory) estimation process according to the first embodiment.

  The walking trajectory estimation process of the present embodiment includes a pedestrian autonomous positioning process 401 and a walking trajectory evaluation process 402. The pedestrian autonomous positioning process 401 is a process executed by the indoor positioning program 1062, and the walking path evaluation process 402 is a process executed by the walking path evaluation program 1063.

  The pedestrian autonomous positioning process 401 acquires the acceleration data 403 and the angular velocity data 404, and changes the environmental parameter 405 to generate a plurality of walking locus candidates 406. Here, as the environment parameter 405, for example, a stride, a gyro sensor error (drift component), and an azimuth can be considered. If the number of stride parameters is k, the number of gyro sensor error (drift component) parameters is l, and the number of azimuth parameters is m, the number of environment parameters is k × l × m, and the number of walking path candidates 406 is also k × l ×. m pieces are generated. The stride is changed by 0.1 m, for example, between 0.5 m and 1.5 m. The gyro sensor error depends on temperature, humidity, atmospheric pressure, etc., and the environment varies depending on the building. Therefore, for example, a plurality of gyro sensor error values corresponding to various environmental conditions are prepared in advance. With respect to the azimuth, for example, the azimuth parameter may be changed by 5 degrees and used as the azimuth parameter for all the azimuths, or the number of azimuth parameters may be reduced only for the azimuth that is likely to match the actual situation. As the azimuth parameter, a magnetic azimuth obtained by a azimuth magnet may be used. In this case, one azimuth parameter is specified.

  The walking trajectory evaluation process 402 evaluates the plurality of calculated walking trajectory candidates 406 using the measured radio wave intensity data 407 and the respective base station coordinate data 408, and acquires the optimal walking trajectory 409.

<Details of pedestrian autonomous positioning processing>
FIG. 5 is a diagram for explaining the details of the pedestrian autonomous positioning process 401. The pedestrian autonomous positioning process 401 includes a relative azimuth calculation process 4011 for calculating a relative azimuth in consideration of a gyro sensor error 4051 input from the angular velocity data 404, and an azimuth parameter input using the relative azimuth. A walking direction calculation process 4012 for calculating a walking direction based on each of them, a walking speed analysis process 4013 for analyzing a walking speed from the acceleration data 403, a stride parameter 4053, a walking speed, and a walking direction are used. And a walking trajectory generation process 4014 for generating a walking trajectory candidate 406.

  In addition to the above, the gyro sensor error (drift component) 4051 may take, for example, a value that changes by 1 degree (step width is 1 degree / s) from an angular velocity of 1 degree / s to 360 degree / s. . In addition, the drift component value is prepared from 1 degree / s to 360 degree / s as a default, but the drift component value is optimized while deleting the drift value determined to be unacceptable in the process of using the mobile terminal device 100. It is also possible to determine a certain range.

  The relative azimuth calculation processing 4011 acquires gyro sensor data (angular velocity data) 404 and a drift component (gyro sensor error) 4051, and subtracts each gyro sensor error component from the acquired gyro sensor data to thereby reduce noise. Information on the removed relative azimuth is acquired. The number of relative azimuth angle information is generated by the number of gyro sensor errors 4051. The relative azimuth angle corresponding to each gyro sensor error component is held in a memory or the like (not shown).

  The walking direction calculation process 4012 acquires a plurality of pieces of relative azimuth information and reference azimuth information (azimuth parameter) 4052 from which input noise has been removed by the relative azimuth calculation process 4011. Then, the walking direction calculation processing 4012 sets the reference azimuth parameter as an absolute azimuth at the start of walking, and integrates the relative azimuth at each measurement time for each of a plurality of pieces of relative azimuth information to determine the walking direction (walk The shape of the trajectory) is calculated and stored in a memory or the like (not shown).

  The walking trajectory generation process 4014 acquires walking speed information at each time obtained by the walking speed analysis process 4013, input pedestrian stride information, and information on a plurality of walking directions obtained by the walking direction calculation process. To do. Then, the walking trajectory generation process 4014 obtains a walking trajectory for each walking direction and step length, and stores the walking trajectory in a memory or the like (not shown).

<Walking locus evaluation process>
FIG. 6 is a flowchart for explaining details of the walking trajectory evaluation process 402. Here, the operation subject is described as the walking trajectory evaluation program 1063, but the execution subject of the walking trajectory evaluation program 1063 is the processor 105, and may be read as the processor 105.

  The walking trajectory evaluation program 1063 first uses a plurality of starting points (s) prepared in advance using each of a plurality of walking trajectory candidates (n) generated by the pedestrian autonomous positioning process 401 (indoor positioning program 1062). ) Are generated, and N (n × s) walking locus candidates to be processed are obtained (S601). Accordingly, there are trajectories having the same shape starting from each starting point.

  The walking trajectory evaluation program 1063 sets P = 1 (S602), and selects a Pth walking trajectory candidate from N candidates (S603).

  The walking trajectory evaluation program 1063 calculates distances from the respective coordinate positions on the walking trajectory candidate selected in S603 to each of a plurality of installed base stations (S604). Although the plurality of walking trajectory candidates (n) 406 obtained by the pedestrian autonomous positioning processing 401 indicate only relative positions where the starting point is not determined, the coordinate value at each time on the trajectory is unknown. Since the starting point is determined for N candidates, the coordinate value on the trajectory can be known.

  The walking trajectory evaluation program 1063 identifies the coordinates on the walking trajectory candidates whose distance from each base station is within a predetermined distance, and the time at the coordinates and the radio wave from each base station corresponding to that time. Get the strength (measured value). Then, the walking trajectory evaluation program 1063 obtains the number exceeding the predetermined threshold in the acquired radio wave intensity (S605).

  The walking trajectory evaluation program 1063 determines whether the processes of S604 and S605 have been completed for all of the N processing target walking trajectory candidates (S606). If the process is complete, the process moves to S608. If not completed, the process proceeds to S607, the next walking path candidate to be processed is selected (S603), and the processes of S604 and S605 are executed.

  On the other hand, in S608, the walking trajectory evaluation program 1063 determines a walking trajectory candidate having the maximum number of radio wave intensity values exceeding the threshold as an optimal walking trajectory. In this embodiment, the evaluation value of the walking trajectory is the number exceeding the threshold value of the radio field strength, but it should be a value that evaluates the consistency between the received radio field intensity estimated from the estimated coordinates and the actually measured radio field intensity. It ’s fine.

<Specific example of walking trajectory evaluation processing>
FIG. 7 is a diagram for explaining a specific example of the walking trajectory evaluation process. Here, it is assumed that there are two base stations, the radio field intensity of the signal from each base station is 701 and 702, and loci 703 to 708 are given as walking trajectory candidates to be processed (in FIG. Only the trajectory is shown). Trajectories 704 and 705 are trajectories having the same shape but different starting points.

  First, a trajectory candidate to be processed is selected according to S603. Here, for example, the locus 705 is assumed to be a processing target.

  Subsequently, according to S604, the distances from the respective coordinate positions C1, C2,... Cn on the locus 705 to the base stations 1 and 2 are calculated.

  Then, according to S605, for the coordinate positions C2 and Ck within the predetermined distance D from the base stations 1 and 2, the radio field intensity S1 from the base station 1 or 2 at that time (the time when the coordinate position is assumed to pass) It is determined whether each S2 exceeds the radio field intensity threshold value STH. In the example of FIG. 7, since the radio field intensity at C2 and Ck exceeds STH, it is counted as a coordinate point exceeding the threshold.

  The processing as described above is executed for all trajectory candidates 703, 704, and 706 to 708 to be processed.

  According to S608, the number of radio field intensities exceeding the threshold value of each trajectory candidate is compared, and the largest number of walking trajectory candidates is selected as the optimal walking trajectory. When there are a plurality of trajectories indicating the largest number, the one having the shortest distance obtained from the trajectories may be selected as the optimal trajectory.

(2) Second Embodiment In addition to the process of the first embodiment, the second embodiment relates to a process for obtaining an optimal walking locus by executing a walking locus evaluation using building data.

<Configuration of mobile terminal device>
FIG. 8 is a diagram illustrating a schematic configuration example of a mobile terminal device (also referred to as an information processing system or a walking trajectory estimation system) 100 according to the second embodiment of the present invention.

  In addition to the configuration according to the first embodiment (see FIG. 1), the mobile terminal device 100 according to the second embodiment stores building data, which is information related to the external shape and internal structure of a plurality of buildings, in the auxiliary storage device 104. It has a building data storage area 1043 for storing.

<Configuration example of building data>
FIG. 9 is a diagram illustrating a configuration example of the building data 900. The building data 900 includes a building ID 901 for uniquely identifying / identifying the target building, an information ID 902 for uniquely identifying / identifying the information type of the building data, a type 903 indicating the type of the corresponding information, The configuration item includes an attribute 904 indicating an attribute of the corresponding information, a belonging floor 905, a name 906, and a coordinate 907 indicating a target arrangement location corresponding to the name 906.

  In addition, although the structure without a building is managed as building data here, the form which manages only building external shape data may be sufficient.

<Process overview>
FIG. 10 is a diagram for explaining the outline of the walking trajectory (movement trajectory) estimation process according to the second embodiment.

  In addition to that of the first embodiment, the walking locus estimation process according to the second embodiment further obtains an optimum walking locus using building data in the walking locus evaluation process 402. Specifically, the radio wave intensity is used as in the first embodiment, but in addition to this, a trajectory that falls within the building data (building outline) is selected as the optimal walking trajectory.

<Details of the walking trajectory evaluation process>
FIG. 11 is a flowchart for explaining details of the walking trajectory evaluation process according to the second embodiment.

  The walking trajectory evaluation process 402 according to the second embodiment is the same as the walking trajectory evaluation process 402 according to the first embodiment from S601 to S607, but unlike the first embodiment (S608), the building outline is changed. Furthermore, the optimum trajectory is finally obtained by using it.

  In S1101, the walking trajectory evaluation program 1063 has the maximum number of radio field intensities exceeding a predetermined threshold for all the processing trajectory candidates (N) for which all the processes up to S605 have been completed, and the corresponding building. The walking trajectory that fits in the outer shape of is determined as the optimal walking trajectory.

<Specific example of walking trajectory evaluation processing>
FIG. 12 is a diagram for explaining a specific example of the walking trajectory evaluation process according to the second embodiment. Here, two base stations, signal strengths 1101 and 1102 of signals from each base station, trajectories 1103 to 1108, and building outline 1109 are given as walking trajectory candidates to be processed (in FIG. 12). Only the upper bound of the space is shown). The trajectories 1104 and 1105 are trajectories having the same shape but different starting points.

  In accordance with S1101, it is determined that the maximum number of the radio field strengths at the coordinate positions within a predetermined distance from each base station exceeds three in the trajectory candidates 1104, 1105, and 1107. In the first embodiment, it is difficult to determine which of these three trajectories is optimal.

  Therefore, in the second embodiment, a trajectory that fits into the building outline is determined as the optimum trajectory using the building outline. In the example of FIG. 11, since the trajectory candidates 1104 and 1107 deviate from the building outline 1109, the trajectory candidate 1105 is determined as the optimal walking trajectory without being selected as the optimal walking trajectory. When there are a plurality of trajectory candidates whose radio field intensity values exceed a predetermined threshold and are within the building outline, the shortest trajectory distance may be determined as the optimal walking trajectory.

(3) Third Embodiment In the third embodiment, in addition to the processing of the first embodiment, the walking trajectory evaluation is performed using the signal intensity distribution data and the building data measured in advance, and the optimum walking trajectory is executed. This is related to the process of obtaining.

  The distribution (contour) of the radio wave intensity indicating a predetermined threshold value (STH) according to the environment in which the base station is installed (objects are arranged, the shape of the room, the structure, etc.) is equidistant from the base station (circular as shown in FIG. 12). ) Is not necessarily in the case, but may be distorted.

  In order to cope with such a situation, in the third embodiment, the radio field intensity around the base station is measured in advance, and the region in which the radio field intensity equal to or higher than the predetermined threshold is obtained in advance. I have to.

<Configuration of mobile terminal device>
FIG. 13 is a diagram illustrating a schematic configuration example of a mobile terminal device (also referred to as an information processing system or a walking trajectory estimation system) 100 according to the third embodiment of the present invention.

  In addition to the configuration according to the first embodiment (see FIG. 1), the mobile terminal device 100 according to the third embodiment stores building data that is information regarding the external shape and internal structure of a plurality of buildings in the auxiliary storage device 104. A building data storage area 1043 and a signal intensity distribution data storage area 1044 for storing signal intensity distribution data obtained by measurement in advance.

<Configuration example of signal intensity distribution data>
FIG. 14 is a diagram illustrating a configuration example of indoor wireless signal intensity distribution data used in the third embodiment. FIG. 14A shows information indicating the radio wave intensity at a position away from each base station by a predetermined distance (5 m, 10 m,...), And FIG. 14B shows each base at a predetermined coordinate (P1, P2,...). This is information indicating the intensity of radio waves from the station.

  The signal intensity distribution data 140 shown in FIG. 14A includes a base station ID 1401 for uniquely identifying and identifying a base station, a radio wave intensity value 1402 within a radius of 5 m measured in advance, and a radio wave intensity within a radius of 10 m measured in advance. A value 1403 and... Are included as configuration items.

  The signal intensity distribution data 141 according to FIG. 14B includes a coordinate ID 1411 for uniquely identifying and identifying the coordinates of the measurement location, a coordinate X1412 indicating the value of the X coordinate, a coordinate Y1413 indicating the value of the Y coordinate, Constituent items include a floor 1414 indicating the floor number (Z) and radio field intensity values 1415, 1416,... Received from the base stations (AP1, AP2,...).

  When FIG. 14B is used, signal intensity distribution data may be created based on this. That is, in FIG. 14B, the radio field intensity from each base station at each coordinate is shown, but the radio field intensity at a coordinate that has not been measured cannot be known as it is. Therefore, the radio wave intensity at coordinates that are not measurement points may be estimated by interpolation processing using the data in FIG. 14B. According to this, the radio field intensity data of all coordinates can be acquired. Details are described in, for example, Non-Patent Document 1, and thus the description thereof is omitted here.

<Process overview>
FIG. 15 is a diagram for explaining the outline of the walking locus (movement locus) estimation process according to the third embodiment.

  In addition to that of the second embodiment, the walking locus estimation process according to the third embodiment further obtains an optimal walking locus using the signal intensity distribution data 1501 in the walking locus evaluation process 402. In the third embodiment, unlike the second embodiment, the radio field intensity threshold (STH) is not set uniformly according to the radial distance from the base station, but each coordinate measured in advance. Is set two-dimensionally according to the radio field intensity. That is, it is possible to cope with the case where the position where the radio field intensity threshold is taken is not uniform according to the radial distance from the base station.

Therefore, in the third embodiment, after obtaining the distance between the coordinates on the selected walking locus candidate and the base station in S604 (see FIG. 11), the coordinates are set two-dimensionally in the process of S605. It is determined whether or not the position is included in an area where the predetermined radio field intensity threshold should be taken, and it is determined whether or not the radio field intensity (measured value during movement) at the coordinates included in the threshold area exceeds the threshold. Count the number exceeding.
Other processes are the same as those in the second embodiment.

(4) Fourth Embodiment In the third embodiment, signal intensity data is obtained in advance, but in the fourth embodiment, signal intensity from each base station at each coordinate while walking and moving. This relates to a process for measuring signal intensity and generating signal intensity distribution data.

<Configuration of mobile terminal device>
FIG. 16 is a diagram illustrating a schematic configuration example of a mobile terminal device (also referred to as an information processing system or a walking trajectory estimation system) 100 according to the fourth embodiment of the present invention.

  In addition to the configuration according to the third embodiment (see FIG. 13), the mobile terminal device 100 according to the fourth embodiment includes coordinates at the time of signal strength measurement while the user is walking and moving, distances from each base station, and each A user interface 107 for inputting signal strength from the base station, and a signal strength setting program 1064 for configuring signal strength distribution data as shown in FIG. 14B based on data input by the user. Yes.

(5) Fifth Embodiment The fifth embodiment relates to a process of learning signal intensity distribution data using radio wave intensity from each base station measured when estimating a plurality of walking loci. .

  Creating signal intensity distribution data in advance as in the third embodiment may be complicated. For this reason, it is efficient if the signal intensity distribution data can be generated while executing various processes for estimating the walking trajectory.

  Therefore, in the initial stage, the default value (theoretical design value) is held as the signal intensity distribution data, and the walking trajectory estimation process is executed to obtain the signal intensity data (the signal intensity at a certain user's time and certain coordinates). By accumulating, learning is performed to obtain more accurate signal intensity distribution data.

<Configuration of information system>
FIG. 17 is a diagram illustrating a schematic configuration example of an information processing system (also referred to as a walking trajectory estimation system) according to the fifth embodiment of the present invention.

  The information system includes a mobile terminal device 100 and a server device 200, which are connected via a network 150. Here, the data and functions are shared between the mobile terminal device 100 and the server 200, but this is for reducing the load on the mobile terminal device 100. And each function may be provided.

  The mobile terminal device 100 includes an acceleration sensor 101, a gyro sensor 102, a wireless signal receiver 103, an auxiliary storage device 104 having a sensor data storage area 1041, a processor 105 that executes various programs, and a sensor data acquisition program 1061. And a communication interface 108 for data communication with the server 200.

  The server 200 includes a processor 201 for executing various programs, an auxiliary storage device 202 for storing various data, a memory 203 for storing various programs, and a communication interface 204 for performing data communication with the mobile terminal device 100. Have.

  The auxiliary storage device 202 includes a building data storage area 2021 for storing building data relating to the external shape, shape, structure, etc. of a plurality of buildings, a sensor data storage area 2022 for storing sensor data received from the mobile terminal device 100, and a plurality of users. Alternatively, signal intensity distribution data (default (design) value in the initial stage) generated (learned) from a movement trajectory (estimated trajectory) by a plurality of walking movements by the same user and corresponding signal intensity data (see FIG. 18). Estimated by the signal intensity distribution data storage area 2023 to be stored, the base station coordinate data storage area 2024 for storing the coordinate data of each base station, and data obtained at the time of multiple walking movements by a plurality of users or the same user. Trajectory data that stores signal strength values (radio field strength values) corresponding to the travel trajectory and coordinate values on the trajectory It has a paid area 2025, the.

  The memory 203 includes a signal intensity distribution correction program 2031 that learns signal intensity data obtained by actual measurement and generates signal intensity distribution data, and a walking locus estimation program 2032 that estimates a walking locus. Yes. The walking trajectory estimation program 2032 executes the processing of the indoor positioning program 1062 and the walking trajectory evaluation program 1063 according to the first to fourth embodiments.

<Configuration example of movement trajectory data>
FIG. 18 is a diagram illustrating a configuration example of the movement trajectory data 1800 stored in the movement trajectory data storage area 2025.

  The moving locus data 1800 includes a moving person ID 1801 for uniquely identifying and identifying a pedestrian (moving person), a time 1802 indicating an actual time when the radio wave intensity is measured, and a walking locus obtained by the walking locus estimation process. A coordinate X1803 indicating the X coordinate above, a coordinate Y1804 indicating the Y coordinate on the walking locus, a floor 1805 indicating the floor of the measurement location, and the radio field intensity value of the signal received from each base station at the corresponding coordinate position Radio wave intensity (AP1) 1806, radio wave intensity (AP2) 1807,... FIG. 18 shows the radio wave intensity measured on the same walking locus by the moving person H1.

  In the present embodiment, an optimal walking trajectory is estimated for each walking movement (the user may be the same or different) using the processing according to the first embodiment. The data of each walking trajectory is such that the moving person ID, the coordinates on the trajectory, the radio wave intensity value from each base station at the coordinates, and the measurement time of the radio wave intensity value constitute the moving trajectory data 1800 shown in FIG. Be organized.

  If the movement trajectory data is accumulated for each estimated walking trajectory in this way, for example, if the measured values of the radio field intensity at very close coordinate points are comparable, the measured values are very reliable. It becomes clear that the data is highly specific. Then, the signal intensity distribution correction program 2031 accumulates such highly reliable data, corrects the default value of the initially set signal intensity distribution data, and generates the signal intensity distribution data as shown in FIG. 14B. Generate. Note that the signal intensity distribution correction program 2031 is such that the radio field intensity values on the same coordinate point are close to each other on a plurality of estimated walking loci, and a predetermined value with a number of other radio field intensity values on the coordinate point. If different, the trajectory indicating the different radio field intensity value is excluded from the data for generating the signal intensity distribution data. Then, for the walking trajectory corresponding to the excluded movement trajectory data, the optimal walking trajectory is estimated again according to the same method as in the first embodiment, using correctly learned signal intensity distribution data. Also good.

(6) Sixth Embodiment The sixth embodiment relates to a process of learning signal intensity distribution data using movement trajectory data (see FIG. 18) and user operation history.

  The coordinate values (FIG. 18) in the fifth embodiment use estimated values, but in the sixth embodiment, in addition to that, when the user actually purchases a product at a store or the like, the history information ( The signal intensity distribution data is learned by making effective use of the store position and time.

<Configuration of information system>
FIG. 19 is a diagram illustrating a schematic configuration example of an information processing system (also referred to as a walking trajectory estimation system) according to the sixth embodiment of the present invention.

  In addition to the configuration of the information processing system according to the fifth embodiment, the information system includes a user interface 107 in the mobile terminal device 100 and a user operation history data storage area 2026 in the auxiliary storage device 202 of the server 200. ing.

  For example, when the user (107) purchases at a cash register at an actual store, the user (pedestrian) performs a predetermined operation (for example, a payment operation using an IC card built in the mobile terminal device 100). Used when

<Configuration example of building data>
FIG. 20 is a diagram illustrating a configuration example of building data. The building data is information for managing the user operation history acquisition location such as the outline of each of the plurality of buildings and the cash register installed in each of the buildings.

  The building data 2000 includes a building ID 2001 that uniquely identifies and identifies a building, an item ID 2002 that uniquely identifies and identifies the management target of the building, a type 2003 that indicates the type of management target, and a management target attribute. An attribute 2004 to indicate, a belonging floor 2005 indicating the floor where the management target is installed, a name 2006 indicating the name of the management target, and a coordinate 2007 indicating the location where the management target is installed (the floor apex in the case of the floor itself) And as constituent items.

  This building data is referred to when a purchase place (register) is specified when a user purchases a product at a store (register) and performs a predetermined operation. When the user performs a payment operation, the purchase time and purchase place are accumulated as operation history data. Further, the radio field intensity of the signal from each base station at that time is measured, and the data is also stored as operation history data.

<Configuration example of operation history data>
FIG. 21 is a diagram illustrating a configuration example of operation history data. The operation history data 2100 specifies a mover ID 2101 for uniquely identifying and identifying a mover (pedestrian), a home country 2102 indicating an operation time, an operation 2103 indicating the content of the operation, and a terminal as an operation place. Item ID (corresponding to item ID 2002 in FIG. 20) 2104, a coordinate X2105 indicating the X coordinate of the operation location, a coordinate Y2106 indicating the Y coordinate of the operation location, a floor 2107 indicating the floor of the operation location, and the operation .., Which are signal strengths from the base stations (AP1, AP2,...) At the location, are included as configuration items.

  Thus, the radio field intensity of the signal from each base station when a predetermined operation (product purchase) is performed at an operation place such as a cash register is acquired. The value of the radio wave intensity at a specific coordinate value (correct location) is used for the learning process of the signal intensity distribution data. That is, for example, as in the fifth embodiment, signal intensity distribution data (two-dimensional threshold) is learned by acquiring radio wave intensity values at estimated coordinate values on a plurality of walking loci, The intensity distribution correction program 2031 compares the radio wave intensity value at the user's operation location with the radio wave intensity of the coordinate value on the walking locus that matches or is close to the location. Then, the signal intensity distribution correction program 2031 leaves the data if the difference is equal to or less than a predetermined value, but excludes it if the reliability exceeds the predetermined value because the reliability is low. By doing so, the signal intensity distribution data can be learned more accurately.

(7) Seventh Embodiment In the seventh embodiment, when the walking locus is estimated by the method of the first embodiment, in order to improve the accuracy when the accuracy of the positioning process is low, a pedestrian is used. The present invention relates to processing for guiding to a predetermined place.

  In the method of the first embodiment, when the radio field intensity of the signal from the base station is not reliable enough to determine the optimal walking path from a plurality of walking path candidates (for example, the pedestrians The walking trajectory estimation process cannot be executed efficiently when the vicinity is not moved and there is no received radio wave intensity that takes a value equal to or greater than the predetermined threshold.

  Therefore, in the seventh embodiment, in such a case, the positioning accuracy is determined by guiding the pedestrian to go near a predetermined landmark (an easy-to-understand place in the building: for example, a convenience store or a facility information counter). Is trying to improve.

<Configuration of information system>
FIG. 22 is a diagram illustrating a schematic configuration example of an information processing system (also referred to as a walking trajectory estimation system) according to the seventh embodiment of the present invention.

  The information system includes a mobile terminal device 100 and a server device 200, which are connected via a network 150.

  The mobile terminal device 100 includes an acceleration sensor 101, a gyro sensor 102, a wireless signal receiver 103, an auxiliary storage device 104 having a sensor data storage area 1041, a processor 105 that executes various programs, and a sensor data acquisition program 1061. , A user interface 107 for executing a user operation, and a communication interface 108 for data communication with the server 200.

  The server 200 includes a processor 201 for executing various programs, an auxiliary storage device 202 for storing various data, a memory 203 for storing various programs, and a communication interface 204 for performing data communication with the mobile terminal device 100. Have.

  The auxiliary storage device 202 includes a building data storage area 2021 for storing building data relating to the external shape, shape, structure, etc. of a plurality of buildings, a sensor data storage area 2022 for storing sensor data received from the mobile terminal device 100, and a plurality of users. Alternatively, signal intensity distribution data (default (design) value in the initial stage) generated (learned) from a movement trajectory (estimated trajectory) by a plurality of walking movements by the same user and corresponding signal intensity data (see FIG. 18). Estimated by the signal intensity distribution data storage area 2023 to be stored, the base station coordinate data storage area 2024 for storing the coordinate data of each base station, and data obtained at the time of multiple walking movements by a plurality of users or the same user. Trajectory data that stores signal strength values (radio field strength values) corresponding to the travel trajectory and coordinate values on the trajectory It has a paid area 2025, the.

  The memory 203 performs indoor positioning using the sensor data, and generates an indoor positioning program 1062 that generates a plurality of walking trajectory candidates, and a walking trajectory that evaluates each of the generated walking trajectory candidates and determines an optimal walking trajectory. An evaluation program 1063, a positioning accuracy evaluation program 2033 that evaluates the accuracy of indoor positioning, and a positioning accuracy improvement guidance program that executes processing for guiding the user to a predetermined location so that the positioning accuracy is improved when the positioning accuracy is low 2034.

<Contents of user guidance processing>
FIG. 23 is a flowchart for explaining the contents of a user guidance process for guiding a user to a predetermined place when the received radio wave intensity is low in reliability.

  First, according to the method described in the first embodiment, the indoor positioning program 1062 and the walking trajectory evaluation program 1063 generate a walking trajectory up to the present (S2301), and display the current position on a screen of a display unit (not shown). (S2302). The current position is displayed on the screen even if it is not the correct position. If the signal strength from each base station has been low since the user entered the building, all of the multiple walking trajectory candidates required for indoor positioning processing are unreliable, so it is determined which candidate is optimal. Can not do it. In such a case, coordinates on an arbitrary walking locus candidate corresponding to the measurement time are displayed as the current position. Moreover, the reliability of the signal strength from the base station was high up to a certain position in the building (having a value greater than a predetermined threshold), and the optimal trajectory could be determined by the walking trajectory evaluation process. When the signal strength from the station becomes extremely low, the coordinates of any point among the plurality of locus candidates derived from the position where the optimum locus has been determined is displayed as the current position. Like that.

  Then, the positioning accuracy program 2033 determines whether the positioning error is large by comparing the combination of the current position (coordinate value) and the signal intensity obtained by measurement with the signal intensity distribution data (S2303). More specifically, a position (coordinate value) corresponding to or close to the current position in the signal intensity distribution data (for example, see FIG. 14) is specified, and the measurement signal intensity at the current position and the specified position are identified. The signal strength is compared. As a result of this comparison, if the difference in signal strength is less than or equal to a predetermined value, it is determined that the positioning error is small, the process proceeds to S2301, and the walking locus estimation process by indoor positioning is continued. On the other hand, if the difference in signal strength exceeds a predetermined value, it is determined that the positioning error is large, and the process proceeds to S2304.

  The positioning accuracy improvement guidance program 2034 displays a list of registered landmarks in the corresponding building (a list including names of shops and rooms, facility information counters, etc.) on a display screen of a display unit (not shown), and walks. The user (user) is prompted to select (S2304). When the user selects one landmark, the positioning accuracy improvement guidance program 2034, for example, blinks the position of the selected landmark (with a base station) on the map and guides the user to that location. To do. The landmark information is included in the building data 2021.

  The guided user goes to the location and receives a signal from the base station installed there. Then, the walking trajectory evaluation program 1063 can use the signal intensity data of the received signal as a reference for the walking trajectory estimation process as a position (coordinates) where the signal intensity is equal to or higher than the predetermined threshold value. This improves the accuracy of the walking trajectory estimation.

<Example of building data configuration>
FIG. 24 is a diagram illustrating a configuration example of building data including landmark information. The building data shows only landmark information, but also includes information shown in FIG.

  The building data 2400 indicates a building ID 2401 for uniquely identifying / identifying a building, an item ID 2402 for uniquely identifying / identifying a target in the building, a type 2403 indicating the type of the target, and a target attribute. The configuration item includes an attribute 2404, an assigned floor 2405 indicating the number of floors on which the target is installed, a name 2406 indicating the name of the target, and coordinates 2407 indicating the position of the target on the XY plane.

  In S2304 described above, information on landmarks arranged in the building where the pedestrian is present is extracted from the building data shown in FIG. 24, and the extracted information is displayed, for example, superimposed on the building indoor map. become.

(8) Eighth Embodiment The eighth embodiment uses a plurality of walking trajectories obtained by the method of the first embodiment, and uses a specific area in a building or a pedestrian (operator ) For evaluating the work efficiency.

<Configuration of information system>
FIG. 25 is a diagram illustrating a schematic configuration example of an information processing system (also referred to as a walking trajectory estimation system) according to the eighth embodiment of the present invention.

  The information system includes a mobile terminal device 100 and a server device 200, which are connected via a network 150. Since the configuration of the mobile terminal device 100 is the same as that of the fifth embodiment, description thereof is omitted. The configuration of the server 200 is almost the same as that of the fifth embodiment, but the data stored in the auxiliary storage device 202 and the program stored in the memory 203 are different.

  In the present embodiment, the auxiliary storage device 202 includes a building data storage area 2021 that stores building data related to the outer shape, shape, structure, and the like of a plurality of buildings, and a sensor data storage area that stores sensor data received from the mobile terminal device 100. 2022, a base station coordinate data storage area 2024 for storing the coordinate data of each base station, a movement trajectory estimated by a plurality of users or the same user obtained during a plurality of walking movements, and on the trajectory A movement trajectory data storage area 2025 for storing signal intensity values (radio wave intensity values) corresponding to the coordinate values.

  The memory 203 performs indoor positioning using the sensor data, and generates an indoor positioning program 1062 that generates a plurality of walking trajectory candidates, and a walking trajectory that evaluates each of the generated walking trajectory candidates and determines an optimal walking trajectory. An evaluation program 1063 and a building evaluation program 2035 for evaluating an area utilization rate and work efficiency using a plurality of walking trajectories (moving trajectories).

  When evaluating work efficiency, the building evaluation program 2035 acquires, for example, a plurality of walking loci in a predetermined building of a designated worker from the movement trajectory data storage area 2025, and superimposes the acquired plurality of walking loci. . Then, statistical information such as the density of the overlapping walking trajectory, the degree of swelling, the distribution of the movement time of each trajectory, and the average value of the distance of the trajectory obtained from the walking trajectory is calculated. When there are a plurality of designated workers, the building evaluation program 2035 takes the average of the respective information, and determines how each worker's information differs from the average value of the plurality of workers. Calculate work efficiency. The work efficiency is calculated, for example, with respect to the work of transporting goods (moving work) in a warehouse, but can be used as a material for educating workers with poor work efficiency.

  When evaluating the area utilization rate, the building evaluation program 2035 acquires a plurality of walking trajectories in a specific building from the movement trajectory data storage area 2025 and passes a specific area (which may be specified). The ratio of the total pedestrians to the pedestrians is calculated as the area utilization rate. This area utilization rate can be used, for example, for marketing (specifying a place where a poster is pasted or determining whether to open a store).

(9) Ninth Embodiment The ninth embodiment relates to a process of superimposing and displaying the radio wave intensity data measured on the walking trajectory obtained in the first to eighth embodiments.

<Configuration of information processing system>
(I) FIG. 26 is a diagram showing a schematic configuration of an information processing system according to the ninth embodiment. The configuration shown in FIG. 26 implements the information processing system as the mobile terminal device 100, and includes an input / output device 109 in addition to the configuration of the mobile terminal device of the first embodiment. The auxiliary storage device 104 further includes a building data storage area 1043 for storing building data (see FIG. 9) and a radio wave intensity storage area 1045 for storing radio wave intensity data. The memory 106 includes a data integration program 1065 and a radio wave intensity display. A program 1066 is further stored. The radio wave intensity data may be held as sensor data as in the first embodiment.

  The radio wave intensity data storage area 1045 stores radio wave intensity data indicating the radio wave intensity measured corresponding to the measurement (reception) time (see FIG. 28). In addition to the acceleration sensor data and the gyro sensor data, the sensor data 1041 includes some of azimuth data, geomagnetic data, atmospheric pressure data, GPS data, RFID data (time and various data are associated). You may make it.

  The data integration program 1065 executes processing for integrating the radio wave intensity data and the walking locus. The radio wave intensity display program 1066 executes a process of outputting the data integrated by the data integration program 1065 to the input / output device 109 as a radio wave intensity display.

  When a plurality of building information is prepared, the input / output device 109 inputs at least a selection instruction regarding which building the walking trajectory and radio wave intensity should be displayed in association with each other, or a data integration program This is for outputting the data integrated by 1065.

(Ii) FIG. 27 is a diagram illustrating a configuration in which the information processing system according to the ninth embodiment is realized by the mobile terminal device 100 and the server 200. The information system includes a mobile terminal device 100 and a server device 200, which are connected via a network 150.

  The mobile terminal device 100 includes an acceleration sensor 101, a gyro sensor 102, a wireless signal receiver 103, an auxiliary storage device 104 having a sensor data storage area 1041 and a radio wave intensity data storage area 1045, and a processor 105 that executes various programs. A memory 106 for storing a sensor data acquisition program 1061 and a sensor data output program 1067, a communication interface 108 for data communication with the server 200, and an input / output device 109 for executing input / output of information. Have. The sensor data output program 1067 is a program that realizes a function of outputting various sensor data detected by various sensors and a function of performing radio wave intensity display instructed from the server 200 in the input / output device 109.

  The server 200 includes a processor 201 for executing various programs, an auxiliary storage device 202 for storing various data, a memory 203 for storing various programs, and a communication interface 204 for performing data communication with the mobile terminal device 100. Have.

  The auxiliary storage device 202 includes a building data storage area 2021 for storing building data relating to the external shape, shape, structure, etc. of a plurality of buildings, a sensor data storage area 2022 for storing sensor data received from the mobile terminal device 100, and each base A base station coordinate data storage area 2024 for storing station coordinate data; and a radio wave intensity data storage area 2027 for storing radio wave intensity data received from the mobile terminal apparatus 100.

  The memory 203 performs indoor positioning using the sensor data, and generates an indoor positioning program 1062 that generates a plurality of walking trajectory candidates, and a walking trajectory that evaluates each of the generated walking trajectory candidates and determines an optimal walking trajectory. Evaluation program 1063, data integration program 1065 for integrating the radio wave intensity data and the walking trajectory, and radio wave intensity display for transmitting the integrated data to the mobile terminal device 100 and outputting the radio wave intensity display to the input / output device 109 Program 1066.

<Configuration example of radio wave intensity data>
FIG. 28 is a diagram illustrating a configuration example of radio wave intensity data. The radio wave intensity data 2800 includes a time 2801 indicating a measurement time and a radio wave intensity 2802 indicating a measured radio wave intensity value as configuration items.

<Processing content>
FIG. 29 is a conceptual diagram for explaining a process of associating (associating) an estimated walking trajectory (movement trajectory) of a pedestrian with measured radio wave intensity data by the data integration program 1065.

  As shown in FIG. 29, the estimated time of the trajectory data 2901 and the measurement time of the radio wave intensity data 2902 do not necessarily match. This is due to the fact that the measurement (acquisition) time (sampling rate) of various sensor data that is the basis for generating the walking trajectory may not match the measurement (acquisition) time of the radio wave intensity. Since the two cannot be associated with each other as they are, the radio wave intensity data is interpolated to associate the two.

  That is, when trajectory data exists between two radio wave intensity data measurement times, radio wave intensity data at an earlier time is associated with the trajectory data. For example, as shown in FIG. 29, the data integration program 1065 sets the radio wave intensity data 29021 as trajectory data 29011 to 29013, the radio wave intensity data 29902 as trajectory data 29014 and 29015, and the radio wave intensity data 29023 as trajectory data 29016 and 29017. Be tied to each. In FIG. 29, the radio wave intensity data is interpolated based on the value of the radio wave intensity data at the early time, but the method of interpolating by taking the average of the values indicated by the radio wave intensity data at the two times, A method of interpolating radio wave intensity data between two times by weighting according to the value may be used.

<Radio wave intensity display>
30 and 31 are diagrams showing display examples of association information between trajectory data and radio wave intensity data. FIG. 30 shows a basic display example, and FIG. 31 shows an example in which enlargement and detailed display are possible. The radio wave intensity display program 1066 (and the sensor data output program 1067) executes such display processing.

(I) In FIG. 30, when the generated walking trajectory is indicated by a dotted line 3001, and the measured radio field intensity data is indicated by 3002 to 3012 (FIG. 30A), the radio field intensity data 3002 to 3011 are used. When the radio wave intensity data is interpolated and displayed on the walking locus 3001, FIG. 30B is obtained. In FIG. 30B, the shading of each radio wave intensity data indicates the radio wave intensity, and the darker the display, the stronger the radio wave intensity.

  In FIG. 30, a locus portion 3013 of the walking locus 3001 is out of the building area. Therefore, the walking trajectory 3001 is made to enter the building area by using a building correction process (a process in which the walking trajectory is at least partially reduced to fit within the building outline when the walking trajectory goes out of the building outline). May be. However, if the entire walking trajectory 3001 is reduced, as a result of the accurate trajectory estimation, the trajectory portion 3014 comes off the passage even though the trajectory portion 3014 is on the passage. In such a case, the building correction process may not be performed on the trajectory portion 3014 without performing the above-described building correction process on the entire walking trajectory 3001.

(Ii) As shown in FIG. 31, the radio wave intensity display includes a scale bar 3101 and a display area 3102. FIG. 31A shows a standard display form. The display granularity changes depending on the position of the scale bar 3101. In the case of standard display, the scale bar 3101 is located, for example, in the middle. When the scale bar 3101 is moved in the “wide area” direction, the display is switched to the wide area display 3103 (FIG. 31B). At this time, the radio wave intensity is displayed collectively. Further, when the scale bar 3101 is moved in the “detail” direction, the display is switched to a detail display 3104 in which a part of the building is enlarged (FIG. 31C). Note that the relationship between the display granularity and the position of the scale bar 3101 is predetermined.

(10) Summary (i) In the first embodiment, a plurality of trajectory candidates are generated by executing autonomous positioning processing while changing environmental parameters using sensor data. Then, for each of the plurality of trajectory candidates, signal strength data at a position on the trajectory candidate within a predetermined distance from each base station (within a predetermined radius from the base station) is specified. The identified signal strength data is compared with a predetermined threshold (a value of a predetermined signal strength level), and the number of points (coordinates) within the predetermined radius exceeding the predetermined threshold on the trajectory candidate is counted. Then, the trajectory candidate having the maximum count value is determined as the optimal walking trajectory. By doing in this way, the result of an autonomous positioning can be correct | amended using a reliable radio signal, and an exact walk locus | trajectory can be estimated.

  The environmental parameters include stride data, gyro sensor error, and azimuth data. Since each of these takes a plurality of values (k, l, m), k × l × m trajectory candidates are generated. For each of them, the optimal walking locus is narrowed down as described above using the signal strength data of the radio signal. This makes it possible to accurately estimate the walking trajectory even in the actual usage environment even if the environmental parameter changes.

(Ii) In the second embodiment, in addition to the processing of the first embodiment, it is further determined whether or not a plurality of trajectory candidates can fit within the outer shape of the building. Then, a trajectory candidate that falls within the building outline and has the maximum count value is determined as an optimal walking trajectory. By doing so, it becomes possible to accurately estimate a walking trajectory that is consistent with the shape of the building.

(Iii) In the third and fourth embodiments, the signal strength distribution data indicating the signal strength from each base station at a plurality of points in the building is used, and the signal strength is a region that takes a value equal to or greater than the predetermined threshold value ( Determine the two-dimensional threshold). Then, it is determined from the signal intensity distribution data whether the coordinates on the locus candidate are included in this region, and the signal intensity data at the included coordinate position is compared with a predetermined threshold value. By doing so, it is possible to cope with a case where the threshold cannot be determined uniformly only by the distance from the base station due to various factors. Even in such a case, the walking trajectory can be accurately performed. Can be estimated.

  In the third embodiment, the signal strength distribution data is measured in advance. In the fourth embodiment, the strength data (users) of the signal from the base station measured while executing the autonomous positioning process is used. Is used to manually determine at which position data is acquired.

(Iv) In the fifth embodiment, the walking trajectory is estimated by the same method as in the third embodiment using theoretical design values as signal intensity distribution data at the system operation start stage. Once accumulated, the signal intensity data from the base station at each position (coordinate position) on each trajectory is used to correct the signal intensity distribution data to data suitable for the use environment while learning. By doing in this way, since a walking locus candidate can be evaluated using highly reliable signal intensity distribution data, a walking locus can be estimated more accurately.

(V) In the sixth embodiment, a history of a predetermined operation (for example, a product purchase operation) of a user in a building (including time and place corresponding to the predetermined operation, and signal intensity data at the place) and movement trajectory data (See FIG. 18), the signal intensity distribution data is learned and the signal intensity distribution data is corrected. In this way, since more reliable data such as the signal intensity at the place where the predetermined operation is performed is used, the walking locus can be estimated with higher accuracy.

(Vi) In the seventh embodiment, the position of any one of the plurality of locus candidates is set as the current position of the moving user (it is not necessary to be an accurate position) and is displayed on the display screen. Then, the signal intensity data corresponding to the current position obtained from the signal intensity distribution data is compared with the measured signal intensity data at the current position, and it is determined whether or not the difference between the two exceeds a predetermined value. When the difference between the two exceeds a predetermined value, it is determined that the positioning error is large, and a display for guiding the user to a predetermined landmark in the building is performed. The guided user receives a signal from a nearby base station at the landmark, and uses the signal strength data for the walking locus estimation. By doing so, the accuracy can be improved when the positioning accuracy is low.

(Vii) In the eighth embodiment, the area utilization rate or work efficiency is evaluated by statistically processing a plurality of pieces of walking trajectory information estimated by the method of the first embodiment. By doing so, it is possible to take measures for improving work efficiency, and it is possible to use the area utilization rate as marketing information.

(Viii) In the ninth embodiment, data is integrated by associating (linking) the obtained walking trajectory with the indoor radio wave intensity measured discretely, and the integrated data is presented to the user. . As a result, it is possible to easily grasp the radio field intensity at which position in the room. Moreover, since it is possible to measure the radio field intensity between different work by carrying the device according to the application example to a person who moves indoors, it is not necessary to employ a contractor for measuring the radio field intensity, and the radio field intensity measurement is required. Cost can be reduced.

(Ix) The present invention can also be realized by software program codes that implement the functions of the embodiments. In this case, a storage medium in which the program code is recorded is provided to the system or apparatus, and the computer (or CPU or MPU) of the system or apparatus reads the program code stored in the storage medium. In this case, the program code itself read from the storage medium realizes the functions of the above-described embodiments, and the program code itself and the storage medium storing the program code constitute the present invention. As a storage medium for supplying such program code, for example, a flexible disk, CD-ROM, DVD-ROM, hard disk, optical disk, magneto-optical disk, CD-R, magnetic tape, nonvolatile memory card, ROM Etc. are used.

  Also, based on the instruction of the program code, an OS (operating system) running on the computer performs part or all of the actual processing, and the functions of the above-described embodiments are realized by the processing. May be. Further, after the program code read from the storage medium is written in the memory on the computer, the computer CPU or the like performs part or all of the actual processing based on the instruction of the program code. Thus, the functions of the above-described embodiments may be realized.

  Further, by distributing the program code of the software that realizes the functions of the embodiment via a network, it is stored in a storage means such as a hard disk or memory of a system or apparatus, or a storage medium such as a CD-RW or CD-R And the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage means or the storage medium when used.

  Finally, it should be understood that the processes and techniques described herein are not inherently related to any particular apparatus, and can be implemented by any suitable combination of components. In addition, various types of devices for general purpose can be used in accordance with the teachings described herein. It may prove useful to build a dedicated device to perform the method steps described herein. Various inventions can be formed by appropriately combining a plurality of constituent elements disclosed in the embodiments. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, constituent elements over different embodiments may be appropriately combined. Although the present invention has been described with reference to specific examples, these are in all respects illustrative rather than restrictive. Those skilled in the art will appreciate that there are numerous combinations of hardware, software, and firmware that are suitable for implementing the present invention. For example, the described software can be implemented in a wide range of programs or script languages such as assembler, C / C ++, perl, shell, PHP, Java (registered trademark).

  Furthermore, in the above-described embodiment, control lines and information lines are those that are considered necessary for explanation, and not all control lines and information lines on the product are necessarily shown. All the components may be connected to each other.

  In addition, other implementations of the invention will be apparent to those skilled in the art from consideration of the specification and embodiments of the invention disclosed herein. Various aspects and / or components of the described embodiments can be used singly or in any combination. The specification and specific examples are merely exemplary, and the scope and spirit of the invention are indicated in the following claims.

DESCRIPTION OF SYMBOLS 100 ... Mobile terminal device 101 ... Acceleration sensor 102 ... Gyro sensor 103 ... Radio signal receiver 104 ... Auxiliary storage device 105 ... Processor 106 ... Memory 107 ... User interface 108 ... Communication interface 109 ... Input / output device 150 ... Network 200 ... Server device 201 ... Processor 202 ... Auxiliary storage device 203 ... Memory 204 ... Communication interface 300 ...・ Base station coordinate data

Claims (15)

An information processing system for generating trajectory information of a target moving in a building using data of an acceleration sensor and a gyro sensor,
A radio signal receiver for receiving radio signals from at least one base station located in the building;
A storage device for storing coordinate data of the base station, data of the acceleration sensor, data of a gyro sensor, and signal strength data of a signal received from the base station by a radio signal receiver;
A processor that executes autonomous positioning processing using data of the acceleration sensor and the gyro sensor to generate a plurality of trajectory candidates, evaluates the plurality of trajectory candidates, and generates one trajectory as trajectory information of the target; Have
For each of the plurality of trajectory candidates, the processor compares the signal intensity data at a position on the trajectory candidate within a predetermined distance from the base station with a predetermined threshold, and results of the comparison processing A trajectory determination process for determining trajectory information of the target from the plurality of trajectory candidates based on the information processing system. In claim 1,
The information processing system, wherein the processor changes the environmental parameters including stride data, gyro sensor error, and direction data, and executes the autonomous positioning process to generate the plurality of trajectory candidates. In claim 1,
In the determination process, the processor determines a trajectory candidate having the largest number of signal intensity data exceeding the predetermined threshold as trajectory information of the target. In claim 1,
The storage device further stores building data including information for determining the outer shape of the building,
The processor further executes an outer shape determination process for determining whether or not the plurality of trajectory candidates can fit within the outer shape of the building. In the trajectory determination process, in addition to the result of the comparison process, the outer shape determination process The information processing system characterized by determining the trajectory information of the object based on the result of. In claim 1,
The storage device further stores signal strength distribution data indicating signal strength from the base station at a plurality of points in the building,
In the comparison process, the processor determines whether a point on the trajectory candidate is included in an area indicating the signal intensity equal to or higher than the predetermined threshold from the signal intensity distribution data, and the signal intensity data at the included point and the signal intensity data An information processing system that compares a predetermined threshold value. In claim 5,
The signal strength distribution data is composed of signal data from the base station measured in advance or signal strength data from the base station measured while performing the autonomous positioning process. Processing system. In claim 5,
The processor further uses the signal strength data from the base station at each position on the trajectory in each of a plurality of trajectory information obtained by the trajectory determination process to correct the signal strength distribution data. Execute data correction processing,
The processor determines an area showing a signal intensity equal to or greater than the predetermined threshold using the corrected signal intensity distribution data. In claim 7,
The storage device further stores user operation history data indicating a history of a predetermined operation of the user in the building,
In the signal intensity distribution correction process, the processor corrects the signal intensity distribution data by using the place where the predetermined operation is executed from the user operation history data and the signal intensity data at the place. Information processing system. In claim 5,
The processor further sets a position on any trajectory of the plurality of trajectory candidates as a current position of a moving user, and obtains signal intensity data corresponding to the current position obtained from the signal intensity distribution data; Compared with the measured signal strength data at the current position and determining whether or not the difference between the two exceeds a predetermined value, and if the difference between the two exceeds the predetermined value, the building An information processing system that performs a guidance process for performing a display for guiding a user to a predetermined landmark in the information processing system. In claim 1,
The storage device further stores a plurality of pieces of trajectory information determined by the trajectory determination process,
The information processing system, wherein the processor further executes a trajectory evaluation process for evaluating an area utilization rate or work efficiency by statistically processing the plurality of trajectory information. In claim 1,
The processor further executes a data integration process for associating and integrating the determined trajectory information and the signal intensity data, and an output process for outputting the integrated data generated by the data integration process,
Each of the trajectory information and the signal strength data includes time information,
In the data integration process, the processor associates the trajectory information with the signal intensity data based on the time information. In claim 11,
In the data integration process, the processor integrates the trajectory information and the signal strength data by interpolating the signal strength data when the acquisition time interval of the signal strength data is longer than the generation time interval of the trajectory information. An information processing system characterized by association. In an information system comprising a processor, at least one storage device, and a radio signal receiver for receiving radio signals from at least one base station arranged in a building, using the data of the acceleration sensor and the gyro sensor, An information processing method for generating trajectory information of a target moving in a building,
The storage device stores coordinate data of the base station, data of the acceleration sensor, data of a gyro sensor, and signal strength data of a signal received from the base station by the wireless signal receiver,
The processor executing a trajectory candidate generation process for generating a plurality of trajectory candidates by executing an autonomous positioning process using data of the acceleration sensor and the gyro sensor;
The processor performs a trajectory information generation process for evaluating the plurality of trajectory candidates and generating one trajectory as trajectory information of the target;
In the trajectory information generation process, the processor compares, for each of the plurality of trajectory candidates, the signal intensity data at a position on the trajectory candidate within a predetermined distance from the base station and a predetermined threshold value. A trajectory determination process for determining trajectory information of the target from the plurality of trajectory candidates based on a result of the comparison process. In claim 13,
In the determination process, the processor determines a trajectory candidate having the maximum number of signal intensity data exceeding the predetermined threshold as trajectory information of the target. In claim 13,
The storage device further stores building data including information for determining the outer shape of the building,
The information processing method further includes a step of executing an outer shape determination process in which the processor determines whether or not the plurality of trajectory candidates fit within the outer shape of the building,
In the trajectory information determination process, the processor determines the target trajectory information based on a result of the outer shape determination process in addition to a result of the comparison process.

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