WO2015167172A1 - Système et procédé destinés au positionnement, à la mise en correspondance et à la gestion de données à l'aide d'une externalisation ouverte - Google Patents

Système et procédé destinés au positionnement, à la mise en correspondance et à la gestion de données à l'aide d'une externalisation ouverte Download PDF

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WO2015167172A1
WO2015167172A1 PCT/KR2015/004109 KR2015004109W WO2015167172A1 WO 2015167172 A1 WO2015167172 A1 WO 2015167172A1 KR 2015004109 W KR2015004109 W KR 2015004109W WO 2015167172 A1 WO2015167172 A1 WO 2015167172A1
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WIPO (PCT)
Prior art keywords
data
radio frequency
segments
map
regions
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PCT/KR2015/004109
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English (en)
Korean (ko)
Inventor
모리코이치
수다르산사티쉬
베네트대니
찌앙이페이
펠렛야닉
Original Assignee
삼성전자 주식회사
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Priority claimed from US14/639,076 external-priority patent/US9510154B2/en
Application filed by 삼성전자 주식회사 filed Critical 삼성전자 주식회사
Priority to EP15786005.7A priority Critical patent/EP3139196B1/fr
Priority to CN201580023462.8A priority patent/CN106461768B/zh
Publication of WO2015167172A1 publication Critical patent/WO2015167172A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S11/00—Systems for determining distance or velocity not using reflection or reradiation
    • G01S11/02—Systems for determining distance or velocity not using reflection or reradiation using radio waves
    • G01S11/06—Systems for determining distance or velocity not using reflection or reradiation using radio waves using intensity measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/03—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers
    • G01S19/05—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers providing aiding data

Definitions

  • It relates to location determination, mapping, and data management using crowdsourcing.
  • Location-based services are a type of computer service that uses location data of a user device to perform tasks for a user or to control one or more computer-based operations. When the user is outdoors, the location of the user may already be determined.
  • a user's communication device may include a positioning system (GPS) receiver that enables to determine accurate location information of the communication device.
  • GPS positioning system
  • GPS location data there may be a problem in providing high quality location based services to the user.
  • a method of determining segments for a plurality of trajectories includes radio frequency (RF) data of a communication device. Determining; Determining radio frequency characteristics of the segments; Forming clusters of segments according to radio frequency characteristics; Generating routes of the map using clusters; It may include.
  • RF radio frequency
  • FIG. 1 is a diagram illustrating an example of a communication system.
  • FIG. 2 is a configuration diagram illustrating an example of a data processing system.
  • FIG. 3 is a flowchart illustrating an example of a path generation method.
  • FIG. 4 is a flow diagram illustrating an example of a method of determining radio frequency characteristics for segments.
  • 5 is a flow chart illustrating an example of another method of determining radio frequency characteristics for segments.
  • FIG. 6 is a flow diagram illustrating an example of a method of forming a cluster of segments.
  • FIG. 7 is a flowchart illustrating an example of a first aspect of generating a route for a map.
  • FIG. 8 is a flow diagram illustrating an example of a second aspect of generating a route of a map.
  • FIG. 9 is a diagram illustrating an example of a plurality of trajectories by pedestrian dead reckoning.
  • FIG. 10 is a diagram illustrating an example of a method of determining segments from an example of a pedestrian dead reckoning trajectory.
  • FIG. 11 illustrates an example of a method of forming a cluster from a plurality of segments.
  • FIG. 12 shows an example of a method of determining the length and reference point of a path segment.
  • FIG. 13 is a diagram illustrating an example of a method of determining segment trees.
  • FIG. 14 is a diagram illustrating an example of a method of generating routes of a map.
  • 16 is a configuration diagram illustrating an example of a map of an indoor structure.
  • 17 is a flowchart illustrating an example of a method of checking data.
  • 19 is a diagram illustrating an example of a map for managing data validity.
  • 20 is a diagram illustrating an example of a map after receiving data units specifying a trajectory.
  • 21 is a diagram illustrating an example of a map after receiving a plurality of trajectories.
  • 22 is a flowchart illustrating an example of a path generation method through data inspection.
  • generating the paths may include generating segment trees from clusters; Creating a map using the segment trees; It may include.
  • the radio frequency data includes Wi-Fi data;
  • the radio frequency characteristic may include a WiFi characteristic.
  • each of the Wi-Fi characteristics may include a trend of a radio identifier that is located on a particular distance from one of the radio identifiers and segments.
  • radio frequency data includes magnetic field data; Radio frequency characteristics may include magnetic field characteristics.
  • radio frequency data includes wifi data and magnetic field data; Radio frequency characteristics may include Wi-Fi characteristics and magnetic field characteristics.
  • a method may include: associating radio frequency data with an area of a map; Adjusting the validity score of the radio frequency data units based on a data density of a region associated with each radio frequency data unit, wherein determining the segments comprises: determining, among the radio frequency data units, of the segments Selecting and using only radio frequency data units having a validity score exceeding a minimum validity score; It may include.
  • a method may include determining data inefficiency of regions based on data density of regions; The method may further include reducing the validity score of the radio frequency data units over time using the data inefficiency of the area on the map associated with each of the radio frequency data units. have.
  • a system includes a processor; A memory containing at least one instruction; The at least one instruction, when executed by the processor, determines segments for the plurality of trajectories, wherein each of the plurality of trajectories includes radio frequency data of the communication device; Determining radio frequency characteristics of the segments; Forming clusters of segments according to radio frequency characteristics; Generating routes of the map using clusters; It may include instructions for performing a method comprising a.
  • each of the plurality of trajectories includes radio frequency data of a communication device; Determining radio frequency characteristics of the segments; Forming clusters of segments according to radio frequency characteristics; Generating routes of the map using clusters;
  • a non-transitory computer-readable recording medium having recorded thereon instructions which can be executed by a processor is provided.
  • a method includes receiving data units from a plurality of communication devices, each data unit including location information; Associating data units with regions on a map using location information; Determining a data density of the regions, wherein the data density is indicative of the number of data units received for a predetermined time from the region; And providing an indicator of the data density of the regions; It may include.
  • the method may further include allocating region specific validity information to each of the data units; Determining data inefficiency for regions of the map; Reducing the effectiveness score of the data units over time using the data inefficiency of the area on the map associated with each of the data units; Invalidating the data units if the validity score of the data units does not exceed the minimum validity score; It may further include.
  • a method may include merging a plurality of selected regions into one large region in response to determining that the data density for each of the plurality of selected regions is lower than a data density threshold; And a plurality of selected areas are adjacent to at least one of the plurality of selected areas.
  • the method may further include updating data inefficiency for an area on the map based on the data density of the area over time; It may further include.
  • a system includes a processor; A memory containing at least one instruction; The at least one instruction, when executed by the processor, comprises receiving data units from a plurality of communication devices, each of the data units including location information; Associating data units with regions on a map using location information; Determining a data density of the regions, wherein the data density is indicative of the number of data units received for a predetermined time from the region; And providing an indicator of the data density of the regions; It may include instructions for performing a method comprising a.
  • the crowdsourced data may include different kinds of data obtained over time from one or more communication devices of one or more users.
  • a map of a particular geographic location and / or structure may be automatically generated.
  • the map may be for the interior of the structure.
  • “automatic” means no user intervention.
  • the term “user” means a person.
  • the crowdsourced data may include location data obtained from the communication device.
  • the location data may include an estimated location of the user device determined using pedestrian dead reckoning (PDR) technology applied to the communication device.
  • PDR pedestrian dead reckoning
  • the location data may be specified as Global Positioning System (GPS) data, that is, coordinate data.
  • GPS Global Positioning System
  • the crowdsourced data may include Radio Frequency (RF) data.
  • Radio frequency data may include Wi-Fi data, magnetic field data, or a combination of both data. Wi-Fi data and magnetic field data have different characteristics, but both data show relative stability over time.
  • the technical idea described herein may be embodied as a method or process performed by a data processing system.
  • the technical idea may be embodied as an apparatus such as a data processing system with a processor.
  • a processor executing program code may perform one or more operations disclosed herein.
  • the technical idea may be embodied as a computer readable non-transitory recording medium including program code for performing an operation and / or executing a method or process when executed in a processor and / or system. Can be.
  • the communication system 100 can include one or more communication devices 105, 110, and 115.
  • “communication device” means a device that can communicate with another device using a communication channel.
  • One embodiment of the communication device may include, but is not limited to, a mobile device, a portable device such as a smartphone, a computing device having a Wi-Fi or other wireless transceiver such as a tablet computer, or the like.
  • the number of communication devices shown in FIG. 1 is merely an example and is not limited thereto.
  • System 100 may include three or fewer or three or more communication devices.
  • the communication devices 105, 110, and 115 are communicatively connected with the data processing system 120 through the network 135.
  • data processing system means a computing system or two or more networked computing systems that properly execute operating system software and one or more applications and / or services.
  • data processing system 120 may be implemented as one or more physical servers, cloud computing facilities, one or more virtual servers running on one or more physical servers, or a combination thereof.
  • Data processing system 120 may include one or more processors 132 capable of executing one or more applications, services, or other program code modules.
  • system 120 may include map generator 125 and data inspector 130.
  • the map generator 125 and the data inspector 130 may be implemented in one or more processors 132.
  • the system 120 may include one or more memories 134 configured to store data received from the communication devices 105, 110, and / or 115.
  • Network 135 is a medium used to provide a communication link between various devices and data processing systems that are connected to each other in system 100.
  • the network 135 may include a connection, such as a wire, a wireless communication link, or a fiber optic cable.
  • the network 135 may be a wide area network (WAN), a local area network (LAN), a wireless network of a WAN or a LAN, a mobile network, a virtual private network (VPN), the Internet, a general switched telephone network. It may be implemented using or including a variety of different communication technologies, such as Public Switched Telephone Network (PSTN) or the like.
  • PSTN Public Switched Telephone Network
  • Structure 140 may be a building.
  • the building can be for personal or office use.
  • structure 140 may be an office facility such as an office, a private home, or the like.
  • the structure 140 is shown by way of example and not limitation.
  • structure 140 may include a plurality of floors, a plurality of rooms, a corridor, and the like (not shown).
  • Each communication device 105, 110, and / or 115 may provide data units periodically for a period of time, with occasional or similar criteria. Thus, when one data unit is provided to each communication device 105, 110, and 115, each communication device 105, 110, and 115 may transmit a plurality of data units over a period of time. Accordingly, the data units 145, 150, and 155 may be stored in the processing and / or memory 134 using the processor 132.
  • System 120 may be configured to determine a map for structure 140. The map may be for the interior of the structure 140. For example, the map determined by the system 120 may be to specify one or more routes within the structure 140.
  • data received from communication devices 105, 110, and / or 115 may be invalidated at different rates. For example, if the data unit 145 has been determined by the system 120 from a region having a large data density, the data unit 145 may be shorter than the data unit 150 determined from a region having a small data density. Can be negated at a high speed. Invalidated data will not be used in the map generation process. In areas where new data is expected to appear more often and more, data units can be quickly invalidated to prevent excessive storage of unnecessary data. In areas where new data is not expected to appear frequently, data units can be invalidated at a slow rate so that enough new data can be received before invalidating too much data.
  • any additional data received from the communication device at the structure 140 may be displayed on the map with respect to the map specific routes. It can be located at. Users of the communication device may be provided with location-based services with higher quality and higher accuracy when located inside a structure, such as structure 140.
  • configuration 200 is a configuration diagram illustrating an example of a data processing system.
  • the configuration 200 can be applied to the system 120 of FIG. 1.
  • Configuration 200 may also be used to implement various devices and / or systems, including memory and processors, that may perform the operations disclosed herein.
  • certain devices and / or systems implemented using configuration 200 may include fewer or more components than shown in FIG. 2.
  • various operating systems and / or applications may be included.
  • configuration 200 may be used to implement a communication device by including one or more applications, such as sensors, such as appropriate transceivers and / or magnetic fields, mobile operating systems, and / or pedestrian dead reckoning applications.
  • configuration 200 includes at least one processor 205 coupled with memory 210 via a system bus 215 or other suitable circuitry.
  • processor may mean at least one hardware circuit (eg, an integrated circuit) configured to perform instructions included in program code.
  • the processor may include one or more cores. Examples of processors include central processing unit (CPU), array processor, vector processor, digital signal processor (DSP), field programmable gate array (FPGA), programmable logic array (PLA), application specific integrated circuit (ASIC), programmable There may be a logic circuit and a controller, but the present invention is not limited thereto.
  • Input / output devices such as keyboard 230, display device 235, and pointing device 240 may additionally be connected to configuration 200.
  • one or more input / output devices may be combined.
  • the touch screen may be used as the display device 235, the keyboard 230, and the pointing device 240.
  • the input / output device may be connected directly to the configuration 200 or with an input / output controller therebetween.
  • One or more network adapters 245 may be coupled with the configuration 200.
  • configuration 200 may be connected to other systems, computer systems, remote printers, and / or remote storage devices via a private or public network.
  • Modems, cable modems, Ethernet cards, wireless transceivers, and / or radio devices are examples of different kinds of configurations that may be used with network adapter 245 in configuration 200.
  • the type of network adapter or network adapters may vary depending on the device used with configuration 200.
  • memory 210 stores operating system 250 and one or more applications 255.
  • Application 255 may include, for example, map generator 125 and / or data inspector 130.
  • operating system 250 and application 255 may be implemented in the form of executable program code and executed in configuration 200.
  • the operating system 250 and the application 255 may be viewed as components integrated in the configuration 200.
  • Operating system 250, applications 255, and all data items used, created, and operated in configuration 200 are data structures that may have functionality when implemented as part of the system using configuration 200.
  • system 120 may receive data units from one or more communication devices over a period of time. When viewed as a whole, the received data units may be referred to as crowdsourced data.
  • the communication device may be located within a structure, such as structure 140.
  • the system may generate a route in the map for the interior or interior portion of the structure.
  • the communication device may be located outdoors.
  • the area where the communication device is located may be obtained in real time, and various paths may be required. For example, known routes may become unavailable due to obstacles such as garbage, breakage, or other reasons. In such cases, various techniques disclosed herein may be used to determine alternative routes.
  • the system can generate a route to a map of the outdoor area.
  • the method 300 may begin with data items for a structure collected for a period of time from one or more different communication devices.
  • the data unit may include one or more data items.
  • the data item may include pedestrian dead reckoning position data and radio frequency data.
  • Radio frequency data may include magnetic field data and Wi-Fi data.
  • the pedestrian dead reckoning position data may be specified in two-dimensional coordinates.
  • a communication device may use, for example, a pedestrian that can utilize acceleration data and compass data that can be used to estimate the position as (x, y) coordinates on a two-dimensional coordinate system having x and y axes perpendicular to each other. It may include a dead reckoning application.
  • Wi-Fi data may include a wireless access device identifier (WAP ID) and a corresponding RSSI.
  • the magnetic field data may include the strength of the magnetic field and may further include the direction data of the magnetic field.
  • Data units such as radio frequency data units, when viewed as a whole, may specify one or more trajectories.
  • the trajectory Ti represents time-series (for example ⁇ t, x, y ⁇ ) Ti.pdr and radio frequency data representing two-dimensional points (e.g., pedestrian dead reckoning position data) of a given communication device.
  • ⁇ t, rf ⁇ can be defined as Ti.rf.
  • Ti.rf may consist of Ti.mag representing the magnitude of the magnetic field in time series (eg ⁇ t, m ⁇ ) and may further include the direction of the magnetic field.
  • Ti.rf may be composed of Ti.wifi representing the detected radio access device identifier (WAP ID) and the corresponding RSSI in time series (e.g. ⁇ t, WAPID, RSSI ⁇ ).
  • the system can form clusters of segments.
  • the system identifies segments that are similar to each other.
  • the system forms a group of segments called clusters. Segments in a cluster based on comparisons to radio frequency characteristics are similar to different segments in a cluster.
  • the system creates one or more routes on the map for the clusters determined in step 315.
  • the map may be a segment graph specifying one or more routes.
  • the path may be a path through which the user can pass. In other embodiments, the path may be determined using one or more segment trees, which will be described later herein.
  • 4 is a flow diagram illustrating an example of a method of determining radio frequency characteristics for segments in connection with step 310 of FIG. 3. 4 illustrates a method of determining magnetic field characteristics.
  • the system can apply a smoothing technique to the strength of the magnetic field.
  • a smoothing technique may be "simple averaging using time-based windows.” In this way, values close in time can be weighed and averaged together.
  • the smoothing technique may be applied on a segment basis.
  • the system can determine an average of the smoothed magnetic field strengths. The average may be determined on a segment basis.
  • the system can subtract the mean from each magnetic field strength. Step 415 may also be applied on a segment basis. Thus, the average for a given segment is subtracted from the smoothed magnetic field strength for that segment.
  • the system determines the highest (ie, high) and lowest (ie, low) values according to the smoothed magnetic field.
  • the system can determine or calculate the distance from the end point of the segment (eg, the same reference end point of the segment for each distance calculation) to each maximum and / or minimum location.
  • the magnetic field characteristic may be a series of one or more distances and corresponding pairs of highest or lowest points.
  • FIG. 5 is a flow diagram illustrating an example of another method of determining radio frequency characteristics for segments associated with step 310 of wireless FIG. 3. 5 illustrates a method of determining Wi-Fi characteristics.
  • the system may delete the Wi-Fi data pair (eg, radio identifier and RSSI pair) having an RSSI value below the RSSI threshold.
  • the system may apply a distance-based smoothing technique for RSSI on a segment basis. Examples of smoothing techniques may include, but are not limited to, simple averaging using distance-based windows. In this case, RSSI values adjacent to each other in distance may be measured and averaged based on a radio access unit identifier unit. In one embodiment, the distance may be 5 meters or less, but this is for illustrative purposes and is not limited thereto.
  • the system may determine a radio identifier indicating the RSSI trend for each segment.
  • trends may include, but are not limited to, increasing, decreasing, minimum and maximum.
  • “trend” can mean one or more Wi-Fi data and data pairs for a segment. In this case, when graphically represented, the RSSI is greater than the lowest slope of positive in the "up” tendency or the lowest of the negative in the "down” tendency. It may have a value less than the slope value, or may define minimum or maximum values.
  • the system can determine the distance from the end of the segment (eg, the same reference end) to the beginning of each trend.
  • the Wi-Fi characteristic is a time series representation of one or more radio identifiers, the corresponding trend for the segment and the starting position of each trend (eg, “trending position”).
  • the system may use the most frequently observed radio identifier in each segment as a hash key.
  • the hash key may be generated based on the length of the segment. Segments with different lengths (eg, having a difference greater than a predetermined value) can be thought of as representing different paths.
  • the hash key may allow the system to more efficiently exclude pairs of segments that do not explicitly match. For example, if two segments have different hash keys, the system may determine that the two segments are not similar.
  • the system may generate a similarity matrix using radio frequency characteristics.
  • the similarity matrix may be generated using only Wi-Fi characteristics.
  • the similarity matrix may be generated using only magnetic field characteristics.
  • the similarity matrix may be generated using both Wi-Fi and magnetic field characteristics.
  • the similarity matrix may be represented by a similarity matrix (SIM), and may be calculated for all segments using radio frequency characteristics selected between the two segments.
  • SIM [(i, j), (k, l)] combination of Wi-Fi and magnetic similarity scores between segments Sij and Skl. have.
  • the two scores may be stored together as a similarity matrix.
  • the calculation method may be a sparse matrix calculation in which the aforementioned hash key is included or considered.
  • the system may update the similarity matrix by sharing the score between the segments connected to each other.
  • upstream and downstream neighbors of the segments, reflected in each element may be identified.
  • the upstream and downstream neighbors may be segments connected at each end of the segment.
  • neighbors are (i, j-1), (i, j-2), ..., (i, 0) and (i, j + 1), (i , j + 2), ..., (i, Mi).
  • the system may replace SIM [(i, j), (k, l)] with the sum of the similarity scores between the neighbors of the two segments.
  • the system may convert the similarity matrix into a binary matrix.
  • the similarity matrix may be converted to a binary matrix using a threshold score.
  • the system may form a segment similarity graph using the binary matrix.
  • FIG. 7 is a flowchart illustrating an example of a first aspect of generating a route for a map.
  • segment trees representing the route of the cluster may be determined.
  • the system may identify anchor points that form the end points of the segments for each cluster, determine the distance between the reference points by calculating the length of the segments in each cluster, and derive a segment tree. The coordinates of the reference points can be determined.
  • FIG. 7 illustrates a method for determining a segment tree, which may be performed at the first side (320-1 of FIG. 7) of step 320 of FIG. 3.
  • the system determines two reference points (eg, reference points A0 and A1) of the selected path segment at step 705.
  • the selected path segment may be referred to as H0 and may be the largest cluster initially.
  • the reference points may be end points of the selected path segment.
  • the system can determine the length of the selected path segment as the median of the lengths of the segments of the cluster.
  • the system can determine or select the location and orientation of the selected path segment. The location and path of the selected path segment can be arbitrarily selected. Each path segment connected to the selected path segment will have a position and direction relative to the selected path segment. Later, when the segment trees are merged to form a map, the resulting structure may be associated with one or more known inlet and / or exit points to reorient and position the resulting map to known coordinates, such as GPS coordinates. .
  • the system may combine the radio frequency characteristics of the segments to determine the radio frequency fingerprint of the selected path segment.
  • the radio frequency characteristics combined to form the radio frequency signature may be dependent on the embodiment, and may mean only the Wi-Fi characteristics or the magnetic field characteristics, or may mean both the Wi-Fi characteristics and the magnetic field characteristics.
  • the radio frequency characteristic for the path segment may be a combination of radio frequency characteristics of the member segments of the cluster.
  • step 725 determine each adjacent segment of the segment that is a member of the cluster that specifies the selected path segment. For example, if the segments of the cluster representing the selected path segment are associated with segment set S, an adjacent segment for each segment of segment set S is determined. As discussed above, the segments have a unique order established from the original pedestrian dead reckoning trajectory data. Segments may have adjacent to each of the two end points, have only adjacent one end point, or may not have adjacent ones.
  • the system can cluster adjacent segments into clusters. Adjacent segments may be clustered using the clustering techniques disclosed herein.
  • the system may define a new path segment. Furthermore, the system may determine, for each new path segment defined at step 740, the length of the path segment, one new reference point (since one reference point is shared with a previous adjacent path segment) and an angle with the previous path segment. . In step 745, the system can determine a radio frequency fingerprint for the new path segments.
  • the system may compare the new path segments with the existing path segments using radio frequency characteristics. If a matching path segment of the new path segments is found, the system can store the association between the two matching path segments in the association matrix. Associations between route segments can be used for the segment tree to form a complete route graph (eg, a map with routes). For example, if a segment appears in a plurality of segment trees, the system can use it to determine how to accurately rotate, transform, and merge the segment tree.
  • a complete route graph eg, a map with routes
  • step 755 the system determines which unprocessed neighboring segments for the new path segments defined in step 740 should be processed. If so, the method returns to step 730 to process adjacent segments, and the segment tree continues to grow. If no further processing is needed for unprocessed neighboring segments, the method ends.
  • the method shown in FIG. 7 is performed and repeated in each cluster. Clusters can be processed in descending order of size. Thus, upon completing multiple iterations in FIG. 7, the system will generate a plurality of different segment trees.
  • 8 is a flow diagram illustrating an example of a second aspect of generating a route of a map. 8 illustrates an example of a method that may be performed as part of step 320 of FIG. 3, and is classified as 320-2.
  • the system operates with a plurality of segment trees formed.
  • the system identifies a set of path segments that are unique from the union of the path segments of all segment trees.
  • the system identifies a unique set of reference points that form the end points of the segments identified in step 805.
  • the system determines the distance between the selected reference point pairs based on the path segment length.
  • step 820 the system derives the coordinates of the reference point of the route segments and uses the derived coordinates to create a map, such as a route segment graph.
  • step 820 may be performed by applying a global optimization / node embedding technique to the reference point and segments to obtain the coordinates of the reference point.
  • a global optimization / peak mapping technique is Dabek et al, “Vivaldi: A Decentralized Network Coordinate System.”
  • global kinematics techniques can be used in place of global optimization / vertex mapping techniques.
  • the system can determine whether new path segments match and / or overlap with existing path segments. If so, the matching and / or overlapping path segments are merged, and the angle between the path segments can be adjusted according to the merging result. For example, the angle of the connected path segment can be adjusted such that the two overlapping path segments completely overlap.
  • FIG. 9 is a diagram illustrating an example of a plurality of trajectories by pedestrian dead reckoning.
  • An example of a pedestrian dead reckoning trajectory may be operated as described above with respect to step 305 of FIG. 3.
  • FIG. 10 is an illustration of a method of determining segments from the example of the pedestrian dead reckoning trajectory of FIG. 9 as described above with respect to step 305 of FIG. 3.
  • FIG. 11 is an illustration of a method of forming a cluster from a plurality of segments as described above with respect to step 315 of FIG. 3. 11 illustrates the formation of clusters.
  • the cluster is shown within elliptical boundary 1105 and is indicated by a thick line.
  • the cluster may be the first or first cluster that includes each of the thick segments in elliptical boundary 1105. Segments of the cluster may differ from each other due to incorrect or arbitrarily set angles in the original pedestrian dead reckoning trace.
  • FIG. 12 is an illustration of a method of determining the length and reference point of a path segment as described above with respect to step 315 of FIG. 3. Reference points 1205 and 1210 are shown for the identified clusters.
  • FIG. 13 shows an example of a method of determining segment trees as described above with respect to FIG. 7.
  • FIG. 14 illustrates an example of a method for generating routes of a map as described above with respect to step 320 of FIG. 3.
  • the method 1500 is a flowchart illustrating an example of a method of performing location determination.
  • the method 1500 may be performed by the system described above with respect to FIGS. 1 and 2.
  • the method 1500 may be performed using routes of a map generated as described above with respect to FIG. 3.
  • the system can receive one or more data units (eg, radio frequency data units received from a communication device).
  • the communication device may be located indoors or outdoors, such as structure 140.
  • the received data unit may include pedestrian dead reckoning location data and radio frequency data.
  • Radio frequency data may include Wi-Fi data and / or magnetic field data.
  • Each data unit may further include a time stamp.
  • the system can determine the radio frequency characteristic from the received data units. For example, the system may determine the Wi-Fi characteristic from the received data units, determine the magnetic field characteristic from the received data units, or determine both the Wi-Fi characteristic and the magnetic field characteristic from the received data units.
  • the method 1500 may be performed repeatedly to provide location based services to a user.
  • the system can send the map and the location of the communication device to the communication device so that the user can track his location indoors.
  • the map 1600 may be generated and displayed on the screen and / or display device of the data processing system to indicate a place and / or point of interest to the user.
  • the structure of map 1600 may be a retail store.
  • Various compartments of the structure may represent different kinds or sectors of goods and / or services (collectively referred to as “goods”).
  • blocks 1605-1650 can represent a shelf or display area that includes merchandise that a user can purchase.
  • the checkout counter 1655 may indicate an area within the structure in which the user pays for the item to be purchased.
  • a path taken by the users through the structure may be determined.
  • the user's level of interest in a particular product may also be determined.
  • the system can determine the time the user stayed in a particular passage or portion of the passage, i. Since the trajectory and the road are described extensively herein, the trajectory and the length are not shown in FIG. 16. However, FIG. 16 shows regions 1660, 1665, 1670, 1675, and 1680 that represent portions where a user visited more frequently or spent more time than other regions.
  • trajectory information from the communication device may be analyzed, segmented, and used on the indoor map using radio frequency characteristics. May be associated.
  • the system may determine where the user is staying in a given segment based on the received time stamp data and the change (or not changing) of the radio frequency characteristic over time.
  • the length of time a user stays in the same area may be used to measure the user's interest in a particular product or category of merchandise. The longer the time, the higher the level of interest, so the interests can be shown on the map.
  • the system may send a message to the user as part of the location service.
  • the message may include coupons, advertisements, and the like.
  • the specific content of the message may be related to the location of the user. For example, in response to determining that the user has stayed in an area associated with a particular category of goods for more than a minimum amount of time, the system may send a coupon or the like to encourage purchase of a product of the category associated with the user's current location. .
  • FIG. 16 is shown in black and white, different colors, patterns, or visual indications may be used to indicate places frequently visited by users or areas visited over different times. Further, the user may show the path taken while passing through the structure 1600.
  • FIG. 16 is described in relation to a retail store for illustration purposes, the techniques disclosed herein may be used to locate a user in a room of various structures. Various shelf areas may also be another area of interest for other indoor settings.
  • FIG. 16 describes an indoor setup
  • the techniques disclosed herein may be applied to an outdoor setup.
  • the various blocks may represent a wall in an outdoor environment, a stand of an outdoor market, and the like.
  • FIG. 17 is a flowchart illustrating an example of a method of checking data.
  • the method 1700 may be practiced using the system described above with respect to FIGS. 1 and 2 herein.
  • the method 1700 may be applied to verify received data when receiving data collected using crowdsourcing. Invalidated data or data units are excluded (eg, deleted and / or discarded from the data set) for use in various services. For example, data determined invalid using method 1700 may not be used to determine the path of the map as described above with respect to FIG. 3.
  • the type of data to be examined using the technique illustrated in FIG. 17 may vary.
  • data from one or more communication devices of one or more users, received as part of map generation and / or user location solution using crowdsourcing may be verified.
  • crowdsourced data such as rank for interest, may be verified.
  • each data unit may include or be specified as a location, a time stamp, and optionally include or as specified a data payload.
  • the location may be GPS coordinates if GPS is available, and may be location information based on pedestrian dead reckoning or similar.
  • the location may be specified by a radio identifier.
  • the payload may include various different data items. Examples of data items may include radio frequency data, sensor data from a communication device, user input, application data, or the like as disclosed herein.
  • the system may associate the data item with the regions of the map using the location information. For example, the system can determine a particular area on the map from which each received data item originated. The region from which the data unit comes from is associated with the data unit.
  • the data unit may be stored in a data structure in the form of a database as it is associated with an area.
  • each area of the map may be associated with data inefficiency. Therefore, the inefficiency for each data can be specialized depending on the area. Two or more regions may be determined to have the same data inefficiency or different data inefficiencies according to the data density of each region. Regions with high data densities have higher inefficiencies than regions with low data densities.
  • the data density of a region can have two components. Specifically, the data density may have a geographic element and a temporal element. Geographical factors can be applied using data inefficiencies specific to the region.
  • the temporal component can be determined based on a particular amount of time. For example, the data density may be determined by considering the number of times data received from the region is received in time units.
  • the system may assign a validity score for each data unit.
  • the assigned validity score may be the original validity score.
  • data inefficiency may vary from region to region, depending on data density, so the validity scores assigned for each region may be the same.
  • each data unit is assigned an initial validity score of 100.
  • the initial validity score can be less than 100 or greater than 100 as implemented in each system.
  • the system may decrease the validity score of the data units over time.
  • the system may reduce the validity score of the data units periodically for a period of time, occasionally or if a particular event is detected.
  • the system can determine a particular area associated with that data unit.
  • the system can determine data inefficiency of the region associated with the data unit (eg, the region from which the data unit originated).
  • the system can use the data inefficiency of the region to reduce the effectiveness score. Since data units are received over time, and the data density of a region varies, the data inefficiency of each region may also change over time. Thus, the amount by which the effectiveness score decreases for a given data unit may also vary over time.
  • the case where the first data unit has an initial validity score of 100 and the data inefficiency is 10 may be considered.
  • the second data unit may have an initial validity score of 100, and the data inefficiency may be 20.
  • the two data units are associated with different areas on the map.
  • the system may reduce the validity score of the first data unit to 90.
  • the system may reduce the effectiveness score of the second data unit to 80.
  • the system can reduce the validity score of the first data unit to 75 and reduce the validity score of the second data unit to 60.
  • the system may invalidate data units having a validity score that does not exceed a threshold validity score.
  • the threshold validity score may be set to zero.
  • the threshold validity score can be set to any value less than zero or greater than zero.
  • the system may invalidate data items identified as having a validity score of zero or less than zero.
  • invalidated data items may be excluded from use in location determination operations and / or map generation. For example, in excluding data units, the system can access a storage device that stores data units and delete invalidated data from the storage device to change and update the data.
  • valid data may be stored in some areas of the memory device, and invalidated data may be stored in other areas of the memory device or other memory devices, or may be erased and / or overwritten from the memory device.
  • invalidated data units may be marked to distinguish them from valid data units, or may be moved to a specific portion or area of physical memory allocated to invalidated data units to separate valid data units from invalidated data units. have.
  • the method 1700 may be performed repeatedly for data items. In one embodiment, the method 1700 may be performed periodically, occasionally or in response to various events. For example, the method 1700 may be performed in batch mode in response to receiving the minimum number of data units. In another embodiment, the method 1700 may be performed repeatedly in real time as a data unit is received.
  • the method 1800 is a flowchart illustrating an example of a method of managing areas of a map for data inspection.
  • the method 1800 may be practiced using the system described above with respect to FIGS. 1 and 2 herein.
  • the method 1800 shows an example of a technique for updating regions of a map based on data density.
  • the method 1800 may be performed for regions on the map periodically, for a period of time, occasionally or as a response to a particular event. For example, the method 1800 may be performed for a region when a data unit originating from that region is invalidated.
  • the method 1800 may be performed in response to receiving at least the minimum data units from a particular region for a preset time.
  • the method 1800 may also be performed concurrently with the method 1700 of FIG. 17.
  • the system divides the region into two or more regions.
  • the region can be divided into half, third, quadrant, and the like.
  • each region may be rectangular.
  • each region may be square.
  • the result of the quadrants of the regions is also square.
  • the system updates the data density of each region that is the result of the partitioning operation.
  • the system can determine whether the region should be merged with one or more other regions. For example, the system can compare the data density of the selected region with the minimum threshold data density. The system may determine to merge the selected region with one or more other regions in response to determining that the data density of the selected region and one or more adjacent regions does not exceed a minimum threshold data density. If the system determines that the regions should be merged, the method 1800 proceeds to step 1825. If not, the method 1800 proceeds to step 1830.
  • the system may update the data inefficiency of the areas that are processed as the method 1800 is repeated. For example, the system updates data inefficiency for a region created as a result of a region partitioning operation, a region generated as a result of a region merging operation, and / or for a selected region for which no region partitioning or merging operation has been performed.
  • data inefficiency is determined by data density.
  • the system can take a data density and store a representation that outputs data inefficiency.
  • the data inefficiency may be data density.
  • the system may store a table that associates data density and / or range of data density with data inefficiency.
  • 19 is a diagram illustrating an example of a map for managing data validity. 19 shows the initial state of the map 1900 before generating any region.
  • FIG 20 is a diagram illustrating an example of a map after receiving data units specifying a trajectory. As shown, the trajectory proceeds through region 2020 and passes through region 2005 and region 2010.
  • Regions 2102, 2106, 2108, 2110, 2138, 2140, 2148, 2154, 2156, 2160, and 2162 have the brightest contrast and the lowest data density. Because new data units to replace invalidated data units are expected to reach the region at a slow rate or rate, the regions 2102, 2106, 2108, 2110, 2138, 2140, 2148, 2154, 2156, 2160, and 2162.
  • the data inefficiency may be set low (eg, lowest in this example).
  • the rate at which data units are invalidated may match the rate at which new data units are expected to come in for a given area.
  • Map 1900 also shows an example of a split and merge operation.
  • an area such as the area 2015
  • it may be divided into four areas having the same size as the area 2116.
  • it may be divided into four areas having the same size as the area 2118.
  • four regions having the same size as the region 2118 may be merged to form a region having the same size as the region 2116.
  • Four regions having the same size as the region 2116 may be merged to form a region having the same size as the region 2015.
  • the data density of the region can be updated dynamically. Regions can be dynamically partitioned and / or merged because the data density for each region is constantly changing.
  • Map 1900 is merely shown for illustrative purposes. Thus, the number, size and / or shape of the regions does not limit the technical spirit disclosed herein.
  • the data validity management techniques disclosed herein may be used to process data units used to generate a map or to perform preprocessing steps.
  • the system may receive data units over a period of time.
  • the system can associate the data units with the regions on the map 1900 using the location information and the data density of the region.
  • the system can display the data density (eg, display on the display device).
  • the display device may provide a data density and how the data density of each region changes with time.
  • the data verification technique as described above with respect to FIGS. 17 to 21 may be applied to portions other than geographic data.
  • the location parameter instead of a location parameter indicating a geographic location, the location parameter may specify a particular category within the plurality of categories, a specific group within the classification scheme, a particular level within the data hierarchy, and the like. Data inefficiency may be calculated according to data density based on group units, category units, or level units.
  • an area may indicate a category, a group of a classification scheme, a level of a hierarchy, and the like.
  • the regions may be split and / or merged later to ensure availability for the secondary level, secondary category and secondary group of the hierarchy.
  • Data units of a given group, level or category may be invalidated (eg deleted) if they fail to exceed a threshold validity score.
  • the threshold similarity may be automatically or dynamically increased or decreased to increase or decrease the number of data units included in the group. For example, to reduce the number of data units in a group, the threshold similarity for the group may be increased. An increase in critical similarity results in fewer data units being considered similar and grouped together. In order to increase the number of data units included in the group, the threshold similarity for the group may be reduced. Reducing the critical similarity results in more data units being considered similar and grouped together. The number of data units maintained in the group can be maintained within a given range or at a certain number, etc. by automatically increasing or decreasing the threshold similarity according to the number of data units in the group. If there are too many data units in the group, the threshold similarity may increase. If there are too few data units in the group, the threshold similarity may decrease. Furthermore, groups may be assigned different threshold similarities. In one embodiment, the threshold similarity may be assigned on a group basis.
  • FIG. 22 is a flowchart illustrating an example of a data management and path generation method.
  • data verification associated with steps 2205-2255 is performed prior to path generation.
  • data verification can also be performed after path generation.
  • data verification may be performed in response to an event such as periodically receiving a predetermined number of data units prior to path generation and / or update of the next step.
  • the system receives radio frequency data units from the communication device over a period of time.
  • the radio frequency data unit specifies a trajectory for the communication device.
  • the system associates radio frequency data with the area on the map.
  • the map may indicate an area outside the bounded indoors and the route information may not be included.
  • the map may include updated route information using the route generation techniques disclosed herein. Regions can be associated using GPS coordinates, pedestrian dead reckoning data, radio access device identifiers, and the like.
  • the system determines the data density for the area of the map.
  • the system assigns a validity score to each radio frequency data unit.
  • step 2225 the system determines whether to segment the area of the map. The system determines whether to partition by area, as disclosed herein. If one or more regions are identified to be split in step 2225, the method 2200 proceeds to step 2230. In step 2230, the system performs region partitioning and updates the data density of the regions identified in step 2225. If no area is identified to be split in step 2225, the method 2200 proceeds to step 2235.
  • the system can determine whether to merge the areas on the map. If at step 2235 it is determined that two or more regions are to be merged, the method 2200 proceeds to step 2240. In step 2240, the system merges the areas identified in step 2235. If at least two regions are not identified to be merged, the method 2200 proceeds to step 2245 and the system updates the data inefficiency of the regions.
  • the system may reduce the validity score of the data units over time using the data inefficiency of the region associated with each data unit.
  • the system may invalidate data units having a validity score that does not exceed the threshold validity score.
  • invalidating the data unit may include deleting the data units from the storage device that is storing the data units.
  • the system may move the invalidated data units from a portion of the memory that contains valid data units to another portion of the memory that contains the invalidated data units.
  • the system can activate only valid radio frequency data units.
  • only the latest crowdsourced data can be used for path generation.
  • Using up-to-date data can ensure that path generation is accurate and associated with recently received crowdsourced data.
  • the generated path can reflect changes in the environment.
  • the system determines segments for the trajectory.
  • the trajectories are specified in a plurality of radio frequency data units. That is, only valid radio frequency data units are specified.
  • the system determines radio frequency characteristics for the segments.
  • the system can determine magnetic field characteristics, Wi-Fi characteristics or magnetic and Wi-Fi characteristics.
  • the system forms clusters of segments.
  • the system creates routes of the map. The method 2200 may be repeated occasionally, periodically or continuously to update the path based on valid radio frequency units and to manage the radio frequency units used for path generation.
  • one means one or more.
  • “Plurality” means more than one.
  • “Other” means at least a second.
  • “linked” includes those connected directly and indirectly with one or more intermediaries. The two elements can be mechanically, electrically or communicatively connected using roads, networks, systems or communication channels.
  • “If”, “when”, “in” means a response to detection and / or decision or a response to detection and / or decision.
  • the phrase “if [state or event mentioned] is detected” means “in response to the state or event mentioned”.
  • “in response” or “in response” means to respond to an action, event, or state, or to show a ready response.
  • the second action was performed “in response” to the first action, it means that there is a causal relationship between the occurrence of the first action and the occurrence of the second action, and “in response” means such a causal relationship. .
  • Computer-readable storage medium means a storage medium that stores program code associated with a system, apparatus, or device capable of executing instructions.
  • Computer-readable storage media is non-transitory and can propagate signals by itself.
  • the computer readable storage medium may be composed of, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination.
  • a rough list of more specific examples of computer readable storage media is as follows: portable computer diskette, hard disk, RAM, ROM, flash memory, static random access memory (SRAM). memory), mechanically encoded devices such as CD-ROMs, DVDs, memory sticks, floppy disks, punch cards, and any suitable device combination may be used.
  • Real-time means the degree of response that a user or system feels to be immediate enough for a particular process or decision, or the state of the processor for it.
  • the computer program product may include a computer readable storage medium including computer readable program instructions that enable a processor to perform an embodiment.
  • Computer readable program instructions may be downloaded from a network (eg, the Internet, a LAN, a WAN, and / or a wireless network) from a computer readable storage medium or an external computer or an external storage device to each computing / processing device.
  • the network may include end devices including copper transmission cables, optical communication cables, wireless transmissions, routers, firewalls, switches, gateway computers, and / or end servers.
  • Computer-readable program instructions that enable the implementation of one embodiment of the present specification include assembly instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, It may be source code or object code written in state setting data or a combination of one or more programming languages.
  • Computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, or run on a standalone software package, some on the user's computer, some on the remote computer, or a remote computer or It can run entirely on the server.
  • the remote computer can be connected to the user's computer using any kind of network. For example, it may include any connection that can connect to a LAN, WAN, or external computer (eg, an Internet connection through an Internet service provider).
  • electrical circuitry including, for example, programmable logic circuitry, FPGA, or PLA may execute computer-readable program instructions. Can be.
  • Computer-readable program instructions may be provided for use in a processor of a general purpose computer, special purpose computer, or other programmable processing device. When instructions are executed in a processor of a programmable processor, each block, step, and combination thereof may be implemented.
  • the computer readable program instructions may be stored in a computer readable storage medium.
  • the computer readable storage medium includes computer readable program instructions on which each block and step of the flowchart and the diagram, and combinations thereof, may be practiced and connected to a computer, a programmable data processing device, and / or another device. Can function in a specific way.
  • Computer-readable program instructions may also be directed to a computer, another programmable data processing device, or another device that enables a series of operations on a computer, another programmable data processing device, or a device that generates a process that can be executed on the computer. May be loaded, thus allowing a computer, other programmable device, or other device to perform the blocks or steps in the flowcharts and / or schematics.
  • each block of the flowchart and each block in the schematic diagram may represent a module, segment or portion of one or more executable instructions that may perform a particular operation.
  • the steps illustrated in the figures may operate out of the order shown. For example, the following two steps may be executed in association in order, or sometimes in reverse order, depending on the function.
  • the steps of the block diagram or the flowchart of the schematic diagram and the combination of the schematic diagram and the flowchart can be implemented in a hardware system having a specific purpose to perform a special function or action.
  • the method includes determining segments for the plurality of trajectories using a processor. Each trajectory contains radio frequency data of the communication device. Determining a radio frequency characteristic for the segments using a processor, and determining a cluster of segments according to the radio frequency characteristic using the processor. The method includes using the processor to generate routes for the map using clusters.
  • Generating a route may include generating segment trees using clusters and generating a map using segment trees.
  • the radio frequency data may include Wi-Fi data.
  • the radio frequency characteristic may include a WiFi characteristic.
  • one or more or all of the Wi-Fi characteristics may include a tendency of the radio identifier to which the radio identifier and the radio identifier are located on a particular distance from one of the segments.
  • the radio frequency data may include magnetic field data.
  • Radio frequency characteristics may include magnetic field characteristics.
  • one or more or all of the magnetic field characteristics may include an intensity of the magnetic field above the critical magnetic field strength, located on a particular distance from one of the segments.
  • the radio frequency data may include Wi-Fi data and magnetic field data.
  • Radio frequency characteristics may include Wi-Fi characteristics and magnetic field characteristics.
  • the method may include associating radio frequency data with an area of the map. According to the data density of the area associated with each radio frequency data unit, adjust the validity scores of the units of the radio frequency data over time, and use only selected radio frequency data units having an effectiveness score exceeding the minimum validity score. Determine the segments for the trajectory of.
  • the system includes a processor programmed to execute an operation.
  • An executable operation includes determining segments for a plurality of trajectories. Each trajectory contains radio frequency data of the communication device.
  • the feasible operation includes determining radio frequency characteristics for the segments and determining a cluster of segments in accordance with the radio frequency characteristics.
  • Executable operations include generating routes for the map using clusters.
  • Generating a route may include generating segment trees using clusters and generating a map using segment trees.
  • the radio frequency data may include Wi-Fi data.
  • the radio frequency characteristic may include a WiFi characteristic.
  • one or more or all of the Wi-Fi characteristics may include a tendency of the radio identifier to which the radio identifier and the radio identifier are located on a particular distance from one of the segments.
  • the radio frequency data may include magnetic field data.
  • Radio frequency characteristics may include magnetic field characteristics.
  • one or more or all of the magnetic field characteristics may include an intensity of the magnetic field above the critical magnetic field strength, located on a particular distance from one of the segments.
  • the radio frequency data may include Wi-Fi data and magnetic field data.
  • Radio frequency characteristics may include Wi-Fi characteristics and magnetic field characteristics.
  • the executable operation may include associating radio frequency data with an area of the map. According to the data density of the area associated with each radio frequency data unit, adjust the validity scores of the units of the radio frequency data over time, and use only selected radio frequency data units having an effectiveness score exceeding the minimum validity score. Determine the segments for the trajectory of.
  • Non-transitory computer-readable recording media store instructions and, when executed on a processor, may perform a method.
  • the method includes determining segments for the plurality of trajectories using a processor. Each trajectory contains radio frequency data of the communication device. Determining a radio frequency characteristic for the segments using a processor, and determining a cluster of segments according to the radio frequency characteristic using the processor.
  • the method includes using the processor to generate routes for the map using clusters.
  • Generating a route may include generating segment trees using clusters and generating a map using segment trees.
  • the radio frequency data may include Wi-Fi data.
  • the radio frequency characteristic may include a WiFi characteristic.
  • one or more or all of the Wi-Fi characteristics may include a tendency of the radio identifier to which the radio identifier and the radio identifier are located on a particular distance from one of the segments.
  • the radio frequency data may include magnetic field data.
  • Radio frequency characteristics may include magnetic field characteristics.
  • one or more or all of the magnetic field characteristics may include an intensity of the magnetic field above the critical magnetic field strength, located on a particular distance from one of the segments.
  • the radio frequency data may include Wi-Fi data and magnetic field data.
  • Radio frequency characteristics may include Wi-Fi characteristics and magnetic field characteristics.
  • the method may include associating radio frequency data with an area of the map. According to the data density of the area associated with each radio frequency data unit, adjust the validity scores of the units of the radio frequency data over time, and use only selected radio frequency data units having an effectiveness score exceeding the minimum validity score. Determine the segments for the trajectory of.
  • the method may include receiving data units for a period of time from the plurality of communication devices using a processor.
  • Each data unit may include location information.
  • associate data units with an area on the map using location information and determine, using a processor, the data density of the areas, the data density being specified as the number of data units received over a period of time from the area. Can be.
  • the method may also provide an indication of the data density of the regions.
  • the method adjusts the validity score of the selected data unit over time based on the data density of the area associated with the selected data unit and selects the data in response to determining that the validity score of the selected data unit does not exceed the minimum validity score. You can invalidate a unit.
  • the method assigns validity information specific to an area to each data unit, determines data inefficiency for regions of the map, and uses the data inefficiency of the region on the map associated with each data unit. Their effectiveness score can be reduced over time.
  • the method may invalidate the data units in response to determining that the validity score of the data units does not exceed a minimum validity score.
  • the method may include subdividing the region into a plurality of small regions in response to determining that the region has a data density above a data density threshold.
  • the method merges the plurality of selected regions into one large region in response to determining that the data density for each of the plurality of selected regions is lower than the data density threshold, and the plurality of selected regions Each may include adjacent to at least one of the plurality of selected regions.
  • the method may also include updating data inefficiency for the area on the map based on the data density of the area over time.
  • the system can include a processor programmed to initiate an executable operation.
  • the executable operation may receive data units for a period of time from the plurality of communication devices. Each data unit may include location information. The location information is used to associate data units with areas on the map, determine data density of the areas, and the data density may be specified as the number of data units received from the area over a period of time.
  • the executable operation may also include providing an indication of the data density of the regions.
  • the executable action is to adjust the validity score of the selected data unit over time based on the data density of the region associated with the selected data unit and in response to determining that the validity score of the selected data unit does not exceed the minimum validity score. You can invalidate the selected data unit.
  • the executable operation may be to assign validity information specific to an area to each data unit, determine data inefficiency for areas of the map, and use the data inefficiency of the area on the map associated with each data unit.
  • the validity scores of the data units can be decreased over time.
  • the method may invalidate the data units in response to determining that the validity score of the data units does not exceed a minimum validity score.
  • the executable operation may include subdividing the region into a plurality of small regions in response to determining that the region has a data density above a data density threshold.
  • the executable operation is to merge the plurality of selected regions into one large region in response to determining that the data density for each of the plurality of selected regions is lower than the data density threshold,
  • Each of the regions may include adjacent to at least one of the plurality of selected regions.
  • the executable action may also include updating the data inefficiency for the area on the map based on the data density of the area over time.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
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Abstract

La présente invention concerne un procédé comprenant les étapes suivantes : détermination des segments destinés à une pluralité de trajectoires, chaque trajectoire parmi la pluralité de trajectoires comprenant des données de fréquence radio d'un dispositif de communication; détermination des caractéristiques de fréquence radio des segments; formation des groupes des segments selon les caractéristiques de la fréquence radio; et génération des trajets d'une carte à l'aide des groupes.
PCT/KR2015/004109 2014-04-28 2015-04-24 Système et procédé destinés au positionnement, à la mise en correspondance et à la gestion de données à l'aide d'une externalisation ouverte WO2015167172A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP15786005.7A EP3139196B1 (fr) 2014-04-28 2015-04-24 Système et procédé destinés au positionnement, à la mise en correspondance et à la gestion de données à l'aide d'une externalisation ouverte
CN201580023462.8A CN106461768B (zh) 2014-04-28 2015-04-24 通过使用众包进行定位、绘图和数据管理的系统和方法

Applications Claiming Priority (6)

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US201461985140P 2014-04-28 2014-04-28
US61/985,140 2014-04-28
US14/639,076 2015-03-04
US14/639,076 US9510154B2 (en) 2014-04-28 2015-03-04 Location determination, mapping, and data management through crowdsourcing
KR10-2015-0057331 2015-04-23
KR1020150057331A KR102282367B1 (ko) 2014-04-28 2015-04-23 크라우드소싱을 이용한 위치 결정, 매핑 및 데이터 관리 시스템 및 방법

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WO2015167172A1 true WO2015167172A1 (fr) 2015-11-05

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