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WO2018211261A1 - Procédé de mappage de région en intérieur - Google Patents

Procédé de mappage de région en intérieur Download PDF

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Publication number
WO2018211261A1
WO2018211261A1 PCT/GB2018/051312 GB2018051312W WO2018211261A1 WO 2018211261 A1 WO2018211261 A1 WO 2018211261A1 GB 2018051312 W GB2018051312 W GB 2018051312W WO 2018211261 A1 WO2018211261 A1 WO 2018211261A1
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WIPO (PCT)
Prior art keywords
notional
data
location
boundary
boundaries
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PCT/GB2018/051312
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English (en)
Inventor
Zankar Upendrakumar Sevak
Firas Alsehly
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Sensewhere Ltd
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Sensewhere Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation

Definitions

  • the invention relates to a method of mapping an indoor region and computer processing apparatus configured to map an indoor region.
  • a mobile device such as a smartphone
  • Devices may, for example, include a positioning module, such as a Global Navigation Satellite System (GNSS) which provides an estimate of a user's absolute position.
  • GNSS Global Navigation Satellite System
  • Other systems such as Pedometer Dead Reckoning (PDR) systems, provide an estimate of the user's relative position, which can be combined with previous absolute position estimates to provide a current, real-time position estimate.
  • PDR Pedometer Dead Reckoning
  • a further class of positioning system carries out range-finding on electromagnetic signals received at the device, and combines the range data with predetermined information about the sources of the signals (such as their location) to produce a position estimate.
  • the GNSS and similar systems can produce relatively accurate position estimates, but are limited in that they typically do not function indoors.
  • the relative positioning systems (such as PDR) and range-finding systems (such as Wi-Fi location systems) may be relatively less accurate, but are in some cases the only systems which provide meaningful results indoors.
  • the accuracy of range-finding positioning is limited by factors such as multi-path interference, attenuation by walls, and the like, which can result in poor performance even when the location of electromagnetic signal sources (such as Wireless Access Points, WAPs) is known precisely.
  • the accuracy of range-finding positioning systems can be improved by 'war-walking' in the vicinity of the above-mentioned electromagnetic signal sources, so as to assist in mapping particular received signals to a particular location within a building. This can be done as part of a 'crowd-sourcing' initiative, whereby a number of users contribute data over time, allowing refinement of the location database.
  • the present invention seeks to address deficiencies in the prior art and, in particular, improving the accuracy of range-finding positioning systems (particularly in indoor regions).
  • the spatial feature data, the layout data and the adjusted layout data is obtained from crowdsourced location data relating to changes in location of a plurality of mobile devices within the indoor region, all without having to perform expensive and time consuming dedicated mapping of the indoor region and without having to have prior background knowledge of the indoor region.
  • the method comprises generating the said spatial feature data from received location data relating to changes in location of a plurality of mobile devices within the said indoor region.
  • a pre-existing database of spatial features may be provided from which the spatial feature data is obtained.
  • the said layout data is generated in dependence on the locations of spatial features provided in the spatial feature data.
  • the spatial features whose locations are identified by the spatial feature data may comprise any one or more of the group comprising: (e.g.
  • the said layout data is generated in dependence on respective types of spatial features of the said one or more spatial features specified in the said spatial feature data.
  • the method is performed on one or more server computers (typically each comprising one or more computer (typically hardware) processors).
  • the step of obtaining location data relating to changes in location of a plurality of mobile devices within the said indoor region comprises receiving location data relating to changes in location of a plurality of mobile devices (e.g. from the said plurality of mobile devices). It may be that the location data contains positioning data (e.g.
  • the said notional boundaries each relate to a spatial feature comprising a (typically discrete) area or space within the indoor region in which people change location and/or which people enter and exit (such as a room, a premises (e.g. a store) or a seating area).
  • the said notional boundaries (prior to the step of adjusting the said layout data in dependence on the obtained location data) are of predetermined size and/or shape. It may be that the predetermined size and/or shape is/are selected dependent on types of spatial features the respective notional boundaries relate to. It may be that one or more or each of the said notional boundaries comprises or consists of one or more curved edges.
  • a said notional boundary comprises one or more edges comprising an opening (e.g. a representation of a portal, doorway or elevator door) or an opening may be provided between two edges of a said notional boundary.
  • the step of adjusting the said layout data in dependence on the obtained location data comprises adjusting (e.g. expanding or contracting) the notional boundaries in dependence on the obtained location data.
  • the method comprises adjusting the boundary data to thereby adjust a size and/or shape of one or more said notional boundaries in dependence on the obtained location data.
  • the method comprises determining from the location data a plurality of location sets, each location set of the said plurality of location sets comprising a plurality of locations, the said plurality of locations being locations of a mobile device of the said plurality or of a plurality of the said mobile devices in combination.
  • the step of adjusting the said layout data in dependence on the obtained location data comprises adjusting (e.g. expanding or contracting) one or more or each of the (e.g. sizes and/or shapes of the) said notional boundaries in dependence on the location sets.
  • one or more or each of the said location sets comprise locations adjacent to a said notional boundary and/or within a said notional boundary and/or crossing a said notional boundary.
  • the step of adjusting the said layout data in dependence on the obtained location data comprises adjusting the said layout data in dependence on said location sets.
  • one or more or each of the said location sets comprise a spatial feature path, each said spatial feature path extending from outside a said notional boundary to inside the said notional boundary through a portal and subsequently from inside the said notional boundary to outside the said notional boundary through the or another said portal.
  • the said spatial feature paths are paths determined to have been followed by each of one or more mobile devices. It may be that one or more of the said spatial feature paths comprise a composite spatial feature path comprising locations of a plurality of said mobile devices in combination.
  • one or more or each of the said spatial feature paths or one or more or each of the said composite spatial feature paths is a path which has been determined to have crossed a said notional boundary.
  • the said boundary data represents a plurality of notional boundaries, each of the said notional boundaries relating to (e.g. a boundary adjacent to or provided around at least a portion of or provided substantially around at least a portion of) a respective said spatial feature, the method further comprising, for one or more or each said location set and for one or more or two or more of the said notional boundaries, determining respective relevance parameters indicative (e.g. probabilities) of whether the locations of the said location set relate to the same spatial features as the said respective one or more or two or more notional boundaries.
  • the step of determining respective relevance parameters indicative (e.g. probabilities) of whether the locations of the said location set relating to the same spatial features as the said respective one or more or two or more notional boundaries is performed by fuzzy logic. It may be that the method comprises adjusting one or more of the said one or more or two or more notional boundaries in dependence on the said determined relevance parameters (e.g. probabilities). It may be that the method comprises adjusting the size and/or shape of one or more of the said one or more or two or more notional boundaries in dependence on the determined relevance parameter(s) associated with that notional boundary relative to the determined relevance parameter(s) associated with the other notional boundaries of the said one or more or two or more notional boundaries.
  • the method comprises obtaining a principal path through the indoor region, wherein the said spatial features extend (e.g. branch) from (and are typically connected to and/or are typically in communication with) the said principal path.
  • the principal path may be obtained from (for example) third party information relating to the indoor region.
  • the principal path may be determined from (e.g. the said) location data relating to changes in location of a plurality of mobile devices within the said indoor region. It may be that the said principal path is determined from location data relating to changes in location of a plurality of mobile devices within the said indoor region by detecting one or more (e.g. straight line) paths from the said location data.
  • the said principal path is determined from location data relating to changes in location of a plurality of mobile devices within the said indoor region by: determining a plurality of (e.g. straight line) paths from the location data; assigning a weighting to each of the said (e.g. straight line) paths based on a number of times devices have followed the respective (e.g. straight line) path (e.g. optionally only from location data relating to changes in location of one or more mobile devices having a speed exceeding a predefined threshold); and determining the said principal path in dependence on the weightings assigned applied to the (e.g. straight line) paths (e.g. selecting the (e.g.
  • the principal path may be determined by correlating and/or connecting a plurality of said (e.g. straight line) paths.
  • the principal path may extend between an indoor location (e.g. for example including at least one location where global navigation satellite system measurements are not available) to at least one outdoor location (e.g. where global navigation satellite system measurements are available). This can enable better estimation of the absolute position of the principal path.
  • the method comprises for one or more or each said location set, identifying the said one or more or two or more notional boundaries (typically prior to determining the said relevance parameters) by: identifying which of the notional boundaries represented by the boundary data meet one or more proximity criteria with respect to the locations of the location set. It may be that the method comprises, for one or more or each said location set, identifying the said one or more or two or more notional boundaries (typically prior to determining the said relevance parameters) by: determining a segment of the said principal path which meets one or more proximity criteria in respect of (e.g. closest to) the locations of the location set; and identifying which of the notional boundaries represented by the boundary data meet one or more proximity criteria with respect to the said segment of the said principal path.
  • the method comprises obtaining (e.g. receiving) signal source data relating to electromagnetic signals detected by one or more of the said plurality of mobile devices at one or more locations within the indoor region, or within one or more said notional boundaries, from one or more (typically terrestrial) electromagnetic signal sources (e.g. Wi-Fi access points or Bluetooth beacons).
  • the signal source data may comprise received signal strengths of electromagnetic signals received by one or more said mobile devices at one or more locations determined to be within a said notional boundary from respective (typically terrestrial) electromagnetic signal sources.
  • the signal source data may comprise timing data relating to the timing of electromagnetic signals received by one or more said mobile devices at one or more locations determined to be within the said notional boundary from respective electromagnetic signal sources (e.g.
  • the signal source data may comprise angle of arrival data relating to electromagnetic signals received by one or more said mobile devices at one or more locations determined to be within the said notional boundary from respective electromagnetic signal sources (e.g. the angles or directions of arrival of signals received by the said mobile devices from the respective electromagnetic signal sources). It may be that the signal source data comprises identifiers of electromagnetic signal sources detected by one or more said mobile devices at one or more locations determined to be within the said notional boundary. It may be that the method comprises obtaining (e.g. receiving) signal source data relating to electromagnetic signals detected by one or more of the said plurality of mobile devices within each of one or more said notional boundaries from one or more (typically terrestrial) electromagnetic signal sources (e.g.
  • the method further comprises obtaining, for each of the said notional boundaries, signal source data relating to electromagnetic signals detected by one or more said mobile devices at one or more locations determined to be within the said notional boundary from one or more (typically terrestrial) electromagnetic signal sources (e.g. Wi-Fi access points or Bluetooth beacons); and deriving respective signal source profiles for each of the said notional boundaries from said signal source data.
  • Each said signal source profile is typically associated with a respective said notional boundary.
  • the signal source profile associated with each said notional boundary comprises, for each of one or more electromagnetic signal sources, expected values (e.g. averages, e.g. accumulated means, of previously determined values by one or more said mobile devices) of one or more parameters relating to or derived from electromagnetic signals received from one or more said electromagnetic signal sources by one or more said mobile devices within the said notional boundary. It may be that the signal source profile associated with each said notional boundary comprises, for each of one or more electromagnetic signal sources, expected signal strengths (e.g. averages, e.g. accumulated means, of previously determined signal strengths) of electromagnetic signals received from one or more said electromagnetic signal sources by said mobile devices within the said notional boundary.
  • expected signal strengths e.g. averages, e.g. accumulated means, of previously determined signal strengths
  • the signal source profile associated with each said boundary comprises, for each of one or more electromagnetic signal sources, expected timing data relating to, such as times of flight of, (e.g. averages, e.g. accumulated means, of previously determined times of flight) electromagnetic signals received from one or more electromagnetic signal sources by said mobile devices within the said notional boundary. It may be that the signal source profile associated with each said boundary comprises, for each of one or more electromagnetic signal sources, expected angle of arrival data relating to, such as angles or directions of arrival of, (e.g. averages, e.g. accumulated means, of previously determined angles or directions of arrival) electromagnetic signals received from one or more electromagnetic signal sources by said mobile devices within the said notional boundary.
  • the signal source profile associated with each said notional boundary comprises data relating to expected rates of change with respect to location within the said notional boundary of one or more parameters (e.g. received signal strengths, timing parameters, angle of arrival parameters) relating to electromagnetic signals received from one or more said electromagnetic signal sources by said mobile devices within the said notional boundary. It may be that the method comprises dynamically updating the said signal source profiles over time. It may be that the method comprises: obtaining (e.g.
  • the method comprises adjusting the layout data responsive to said determination.
  • the method further comprises determining a said composite spatial feature path comprising locations of a plurality of devices in combination (typically including the said first and second mobile devices in combination) in dependence on said determination that said locations of the first and second mobile devices relate to the same said spatial feature.
  • the respective signal source data relating to said signals received by the first and second devices may be compared directly, or more typically the respective signal source data is compared to one or more said signal source profiles (typically each of which is associated with a respective said notional boundary) to determine whether the signal source data from one or both devices matches a said signal source profile to thereby determine whether the said locations of the first and second mobile devices are locations relating to the same said spatial feature (e.g. are part of the same room).
  • the method comprises, for one or more or each said location set: obtaining (e.g. receiving) signal source data relating to electromagnetic signals received by the said one or more mobile devices at the locations of the location set from one or more electromagnetic signal sources; comparing the said signal source data to the signal source profiles associated with the said one or more or two or more notional boundaries; and determining the said respective relevance parameters associated with the said respective one or more or two or more notional boundaries in dependence on the said comparison between the signal source data and the respective signal source profiles.
  • the method comprises processing said location data (e.g. the location sets) in dependence on the said signal source profiles (and typically on the obtained signal source data) prior to the step of adjusting the said layout data in dependence on the obtained location data.
  • the method comprises obtaining and processing signal source data relating to electromagnetic signals received within the indoor region by one or more said mobile devices at one or more locations outside of a said notional boundary; determining a similarity between said signal source data and a said signal source profile associated with the said notional boundary; and responsively adjusting a said notional boundary to include (e.g. enclose or substantially enclose) one or more of the said one or more locations outside of the said notional boundary (prior to said adjustment).
  • the layout data comprises boundary data representing a plurality of notional boundaries of the indoor region each of which relates to a respective said spatial feature, the method further comprising: obtaining and processing signal source data relating to electromagnetic signals received within the indoor region (e.g.
  • the method comprises comparing the said signal source data to signal source profiles associated with the said pair of notional boundaries and identifying a said transition path extending between a pair of (e.g.
  • the method comprises determining from the signal source data data relating to rates of change with respect to location of one or more parameters relating to or derived from electromagnetic signals received from one or more said electromagnetic signal sources by one or more said mobile devices in the indoor region (e.g.
  • the method comprises deriving a transition path signal source profile from signal source data relating to signals detected from one or more electromagnetic signal sources by one or more mobile devices on the said transition path between the said pair of notional boundaries. It may be that the method comprises determining that a said mobile device is on the said transition path by comparing signal source data relating to electromagnetic signals received on the said transition path by the said mobile device to the transition path signal source profile. It may be that the layout data comprises boundary data representing a plurality of notional boundaries each of which relates to a respective said spatial feature, the method further comprising: obtaining and processing (e.g. the said) location data relating to changes in location of one or more mobile devices within the said indoor region (e.g.
  • the method comprises: obtaining location specific geographical data relating to the indoor region, the location specific geographical data comprising one or more layout restrictions; and adjusting the layout data in dependence on the said layout restrictions.
  • the layout restrictions are typically indicative of areas which cannot be occupied by the determined layout of the indoor region.
  • the layout restrictions comprise any one or more of the group comprising: outdoor regions; roads; building boundaries; concourses; restricted areas; principal paths.
  • the location specific geographical data comprises or is derived from any one or more of the group comprising: a map; one or more images of (at least a portion of) the indoor region; one or more satellite images of a built environment (e.g.
  • the method comprises: obtaining further location data relating to changes in location of a plurality of mobile devices within the said indoor region; and adjusting the said adjusted layout data in dependence on the said obtained further location data. It may be that the method comprises determining one or more points of interest in the indoor region and storing data associating the said one or more determined points of interest with the indoor region. It may be that the method comprises determining one or more points of interest associated with one or more or each said notional boundary and storing data associating the said one or more determined points of interest with the respective notional boundary. It may be that the method comprises determining the said points of interest from one or more map sources (such as Openstreet maps).
  • map sources such as Openstreet maps
  • the method comprises determining the said points of interest by obtaining one or more images (typically created and/or transmitted and/or received by one or more mobile devices) georeferenced to a location within the indoor region (e.g. within a respective notional boundary); and processing said images to identify one or more points of interest within the indoor region (or determined to be within a respective said notional boundary). It may be that the method comprises determining the said points of interest by obtaining one or more LIDAR scans of the indoor region (or of an area within a respective boundary); and processing the said LIDAR scans to identify one or more points of interest within the indoor region (or determined to be within a respective said notional boundary).
  • the method comprises determining the said points of interest by obtaining social media data relating to the indoor region (or to an area within a respective boundary); and processing the said social media data to identify one or more points of interest within the indoor region (or determined to be within a respective said notional boundary).
  • Said social media data may be related to the indoor region (or to a respective said notional boundary) by being georeferenced to a location within the indoor region (or a location determined to be within a respective said notional boundary).
  • Said social media traffic may comprise any one or more of: text data; audio data; image data; video data (for example).
  • the method comprises determining the said points of interest by obtaining audio data relating to the indoor region (or relating to a respective said notional boundary); and processing the said audio data to identify one or more points of interest within the indoor region (or within a respective said notional boundary).
  • Said audio data may be audio data georeferenced to a location within the indoor region or within a respective said notional boundary.
  • the method comprises identifying one or more patterns in said audio data to thereby identify one or more points of interest within the indoor region (or within a respective said notional boundary), e.g. by comparing one or more patterns in said audio data to one or more models to thereby identify one or more points of interest within the indoor region (or within a respective said notional boundary).
  • a second aspect of the invention provides a computer processing system comprising one or more computer processors (typically hardware processors e.g. microprocessors or microcontrollers), the computer processing system being configured to perform a method comprising: obtaining spatial feature data identifying locations of one or more spatial features of the indoor region.
  • the method further comprises generating layout data relating to (a layout of) the indoor region in dependence on the obtained spatial feature data.
  • the method further comprises obtaining location data relating to changes in location of a mobile device or a plurality of mobile devices within the said indoor region.
  • the method comprises adjusting the said layout data in dependence on the obtained location data.
  • a third aspect of the invention comprises computer program code for causing a computer processing system comprising one or more computer processors to perform the method according to the first aspect of the invention.
  • a fourth aspect of the invention comprises a non-transitory computer readable medium storing computer program code according to the third aspect of the invention.
  • Figure 1 is a schematic block diagram of a mobile telecommunications device in communication with a server computer
  • Figure 2 is a schematic representation of a layout of an indoor region
  • Figure 3 is a schematic representation of a layout of the indoor region determined from spatial feature data, the determined layout of the indoor region being superimposed on the schematic of Figure 2
  • Figure 4a is a flow chart illustrating a method of generating and adjusting the layout data of Figure 3
  • Figure 4b is a flow chart illustrating step 70 from Figure 4a in more detail
  • Figure 4c is a block diagram illustrating a fuzzy logic system for determining a probability of which notional boundary contains the locations of a location set
  • Figure 5 is the schematic representation of Figure 3 also showing paths followed by three mobile devices changing location in the indoor region of Figure 2
  • Figure 6 is a schematic representation of the layout of the indoor region of Figure 3 adjusted in dependence on changes of movement of the mobile devices shown in Figure 5
  • Figure 7 is a schematic representation of the
  • FIG. 1 is a block diagram of a mobile telecommunications device 1 (such as a mobile smartphone, phablet, tablet, laptop, personal data assistant or wearable device such as a smartwatch) comprising a (typically hardware) computer processor 2 (which is typically a general purpose computer processor such as a microprocessor or microcontroller), a memory 4, an accelerometer 5, data communication antennas 6 (one or more or each of which may be directional antennas) and an orientation sensor (such as a gyroscope or magnetometer) 7.
  • a computer processor 2 which is typically a general purpose computer processor such as a microprocessor or microcontroller
  • a memory 4 which is typically a general purpose computer processor such as a microprocessor or microcontroller
  • an accelerometer 5 data communication antennas 6 (one or more or each of which may be directional antennas)
  • an orientation sensor such as a gyroscope or magnetometer
  • the data communication antennas 6 typically comprise a cellular telecommunications antenna, a Wi-Fi antenna and a Bluetooth antenna (not shown) configured to allow the mobile communications device 1 to communicate by cellular telecommunications, Wi-Fi and Bluetooth.
  • the mobile device 1 further comprises a location sensor 9.
  • the location sensor 9 comprises a Global Navigation Satellite System (GNSS) antenna (e.g. Global Positioning System (GPS) antenna) configured to detect signals from GNSS satellites and a GNSS processor configured to process the signals received from the satellites to estimate the location of the mobile device 1 (alternatively processor 2 of the mobile device processes the signals from the GNSS satellites).
  • GNSS Global Navigation Satellite System
  • GPS Global Positioning System
  • the location sensor 9 may additionally or alternatively comprise a Wireless Positioning System (WPS) computer program application (typically comprising stored computer program instructions) configured to cause the processor 2 of the mobile device 1 (or an additional dedicated processor) to estimate a location of the mobile device 1 by processing electromagnetic signals detected by one or more of the antennas 6 from terrestrial electromagnetic signal sources of known (e.g. two dimensional) location (e.g. the two dimensional locations of electromagnetic signal sources may be stored in memory 4) and processing the received signals together with the known two dimensional locations of the electromagnetic signal sources to estimate the location of the mobile device 1 , for example using received signal strengths together with the locations of the electromagnetic signal sources in a triangulation algorithm or with stored (e.g.
  • WPS Wireless Positioning System
  • the location sensor 9 may additionally or alternatively comprise a pedestrian dead reckoning application (typically comprising computer program instructions) configured to cause the processor 2 of the mobile device 1 to determine the location of the mobile device from accelerometer and orientation data from the accelerometer and orientation sensor 5, 7 respectively.
  • the mobile device 1 can communicate with a server computer 8 by way of a wireless telecommunications link 11 (which typically connects to a base station which propagates data to and from the server 8 across a telecommunications network) using one or more of the data communication antennas 6.
  • the server computer 8 comprises a processor 10 (which is again typically a general purpose computer processor such as a microprocessor or microcontroller) and a memory 12, the processor 10 being configured to execute computer program instructions stored in the memory 12.
  • the mobile device 1 typically determines location data as it changes location, and sends the location data to the server 8 using one or more of its data communication antennas 6.
  • the location data may comprise estimates of the location of the mobile device 1 (e.g. determined by the location sensor 9) or it may comprise positioning data from which locations of the mobile device 1 can be estimated (e.g. signal source data detected from a plurality of electromagnetic signal sources, or accelerometer and orientation sensor data from which a location of the device 1 can be estimated).
  • the processor 10 of the server 8 may be configured to execute computer program instructions stored on server memory 12 to cause the server to estimate the said locations of the mobile devices from the positioning data.
  • the server 8 can track the location of the mobile device 1. Heading data (typically measured by the orientation sensor 7 of the mobile device) is also typically transmitted by the mobile device 1 to the server 8. A plurality of such devices 1 is provided which report location data to the server 8.
  • Figure 2 is a schematic representation of an indoor region 20 of a built environment (typically a building) such as a shopping mall or airport.
  • the indoor region comprises a straight main thoroughfare 22 (which in use carries a relatively high level of footfall), five rooms 24-32 branching off the main thoroughfare at intermediate portions along the length of the main thoroughfare, the rooms 24-32 being offset from each other along the length of the main thoroughfare and being accessible to the public, and a turning point 34 at an end of the main thoroughfare 22 leading to a further straight thoroughfare.
  • WO2016/066987 which is incorporated in full herein by reference, discloses a method of obtaining and updating a database of spatial features associated with a region.
  • the method comprises: receiving positioning data that has been collected at a plurality of locations within the region; processing the collected positioning data to identify at least one candidate spatial feature associated with the region; identifying at least one other spatial feature corresponding to said at least one candidate spatial feature, said at least one other spatial feature and said at least one candidate spatial feature as a whole constituting matching spatial features; processing said matching spatial features; and updating the database of spatial features in dependence on the processing of said matching spatial features.
  • This allows spatial features of an indoor region to be identified from unstructured, crowdsourced positioning data from multiple mobile devices moving in the indoor region.
  • the identified spatial features may be areas (or spaces) within the indoor region (such as rooms or corridors), linear features (e.g. walls), gaps in features (e.g.
  • the spatial features are (in some cases discrete) areas or spaces within the region in which people change location and/or which people enter and exit.
  • the specific types of the spatial features identified may be stored in the database, together with the locations of the spatial features.
  • a principal path 40 (see Figure 3) of the indoor region 20, corresponding to a main route through the main thoroughfare 22, can be identified from location data relating to changes in location of a plurality of mobile devices within the indoor region 20.
  • the principal path 40 can be identified by detecting one or more straight line paths from the said location data.
  • the straight line paths may be correlated to identify straight line paths in common between devices.
  • Detected straight line paths (which may comprise only validated straight line paths which have been detected from more than one device, and may be averaged straight line paths derived from a plurality of similar detected straight line paths) may be concatenated to provide the principal path 40.
  • the principal path typically extends through indoor regions but may extend into outdoor regions, indeed can be preferable for the principal path to extend outdoors, or at least be derived using some measurement of the location of mobile devices which are outdoors, as this may enable better global navigation satellite system position measurements to be used to estimate the absolute position of the principal path.
  • the identification of the principal path 40 may also involve taking into account the volume of traffic following detected straight line paths and a speed of movement of the mobile devices along the detected straight line paths. For example, it may be that only detected straight line paths which have been followed by a number of devices (and/or a number of times) which exceeds a threshold number. It may additionally or alternatively be that only changes in location by devices changing location at a speed exceeding a threshold speed are taken into account when detecting straight line paths, or only straight line paths detected from location data relating to changes in location of devices changing location at a speed exceeding the threshold speed are considered for correlation and/or concatenation.
  • Respective detected straight line paths may be weighted in dependence on, for example, the number of devices determined to have followed the respective path, and/or on the speed of movement along the path. Such weightings may be taken into account when deriving the principal path 40 from the detected paths.
  • the paths are typically straight line paths, they are not necessarily. Straight line paths can be derived using linear regression.
  • the principal path 40 may be determined from third party data relating to the indoor region.
  • Figure 4a is a flowchart of a method of generating and adjusting layout data representing the layout of the indoor region 20, typically performed by the processor 10 of the server 8 executing computer program instructions stored in server memory 12.
  • a first step 60 spatial feature data relating to locations (and preferably also the types) of spatial features of the indoor region 20 is obtained.
  • step 60 is performed by the server 8 receiving location data collected and transmitted thereto by mobile devices 1 at a plurality of locations within the indoor region 20 and processing the location data to generate spatial feature data relating to spatial features within the building using the techniques described in WO2016/066987.
  • determining spatial feature data from crowdsourced location data relating to changing locations of a plurality of mobile devices within the indoor region is not an essential feature of the present invention, and that a database of spatial features may already exist (which may have been previously generated for example using the methods of WO2016/066987 or by any other means) in which case it may be that step 60 may involve obtaining the said spatial feature data from a memory.
  • a next step 62 (or optionally as part of step 60), the principal path 40 is determined or obtained as described above.
  • a next step 64 for each spatial feature identified in step 60, notional boundaries (typically in the form of polygons) are generated, taking into account the locations of the respective spatial features from the spatial feature data (and, where provided, the types of spatial features - e.g. different spatial features may be provided with different notional boundaries of different shapes and/or sizes).
  • the notional boundaries are initially relatively small in size (e.g. a rectangle of 1 m by 2m), typically smaller than the spatial features they represent, such that adjusting a notional boundary typically comprises expanding the said notional boundary.
  • layout data 42 representing the layout of the indoor region 20 is generated as shown in Figure 3 (overlaid on the schematic of Figure 2 for clarity) from the determined principal path 40 and the notional boundaries.
  • the layout data 42 comprises a straight line 40 representing the principal path, notional boundaries 42-50 representing the rooms 24-32 respectively and a further notional boundary 52 representing the turning point 34.
  • the boundaries 42-52 extend from the principal path 40.
  • the boundaries 42-52 are of predefined size and shape selected in dependence on the types of spatial feature to which they relate.
  • This location data may comprise or consist of the same location data used to identify the spatial features; alternatively, this location data may comprise or consist of location data obtained subsequently (or even in some cases prior) to the location data used to identify the said spatial features.
  • the location data may provide a plurality of locations determined by the respective devices or data from which a plurality of locations of the said respective devices can be determined.
  • step 68 also comprises determining the said locations of the respective devices.
  • Step 68 typically involves deriving respective location sets from the location data and outputting each location set in turn to step 70, steps 70 to 76 being repeated so as to process each of the location sets.
  • Each location set typically comprises a set of locations (e.g.
  • a fuzzy rule is then implemented to determine a point on the principal path 40 which is closest to the path formed together by the locations of the location set.
  • a segment of the principal path 40 of predetermined length (e.g. 5 or 10m) centred on that point is then output by the rule to step 70b.
  • the output of step 70a is a (e.g. 5m or 10m long) segment of the principal path 40 roughly extending in a straight line along thoroughfare 22 between points A and point B on Figure 5.
  • the fuzzy logic algorithm determines one or more notional boundaries 42-52 near the identified segment of the principal path between points A and B on Figure 5.
  • the inputs required for this step are: data identifying the the segment of the principal path 40 identified in step 70a; and the layout data generated in step 66 relating to locations of the connections of the respective notional boundaries 42-52 relative to the principal path 40.
  • the fuzzy rule applied by the fuzzy logic system at this step determines which of the notional boundaries 42-52 have a connection to the principal path 40 which is less than a predetermined distance (e.g. 10 metres) from the segment of the principal path 40 identified in step 70a. Firstly, the shortest distances of the respective connection points to the principal path 40 of the notional boundaries 42-52 to the segment of the principal path 40 identified in step 70a are calculated.
  • the inputs required for this step are: the shortest distances of the connection points of the notional boundaries identified in step 70b to the segment of the principal path 40 identified in step 70a; the directions of movement of the mobile device(s) along the spatial feature path comprising the locations of the location set; and the respective numbers (or proportions) of locations of the location set which are located within the respective notional boundaries (42, 44) identified in step 70b.
  • the directions of movement of mobile devices 1 may be determined from heading orientation data received from the mobile devices 1 , and/or from the locations of the location set.
  • the number of locations of the location set within the respective boundaries can be determined by comparing the locations of the location set with the locations surrounded by the respective boundaries.
  • the fuzzy logic system implements a fuzzy rule at this stage which calculates a probability value for each notional boundary identified in step 70b.
  • the boundary 42 may be expanded (e.g. by the minimum expansion necessary, or by discrete changes in one or more dimensions of the boundary 42) so that the adjusted boundary 42' contains a number of locations of the location set provided outside of the notional boundary 42 prior to adjustment determined in dependence on the probability calculated for the notional boundary in step 70c.
  • Adjusting the notional boundaries may include adding new edges to existing polygons. This is useful for example to avoid excessively extending a long side of a polygon if new location data is only available along one side of the polygon (for example).
  • the iterations may for example take place periodically, or at predetermined times, or when a predetermined amount of path data has been aggregated (e.g. for a region).
  • devices 1 e.g. devices X, Y, O
  • they receive electromagnetic signals from terrestrial electromagnetic signal sources (which are themselves typically located within the indoor region), such as Wi-Fi access points, Bluetooth beacons and so on (not shown), using their data communications antennas 6.
  • the device processors 2 are configured to execute computer program instructions which derive signal source data from electromagnetic signals they receive within the said notional boundaries and this signal source data is transmitted to the server 8.
  • the signal source data is typically georeferenced to the location at which the signals from which it is derived were received.
  • the signal source data may comprise received signal strengths and/or timing data (e.g. the times of flight of received signals) and/or angle of arrival data (e.g. the angles or directions of arrival of signals received by the said mobile devices) relating to electromagnetic signals received by the mobile devices within the said respective notional boundaries 42-52 from respective electromagnetic signal sources. It may be that the signal source data comprises identifiers of electromagnetic signal sources detected by one or more said mobile devices within the said respective notional boundaries 42-52.
  • the server processor 10 typically executes computer program instructions stored on server memory 12 to derive respective signal source profiles for each of the notional boundaries 42-52 from signal source data received from the said mobile devices 1 (e.g. devices X, Y, O) at locations within the said notional boundaries 42-52.
  • the devices X, Y, O) in the indoor region can be compared to determined signal profiles associated with notional boundaries 42-52 in order to validate estimated locations of the said devices provided in or estimated from the said location data. For example if it is estimated that a said device is located in a said notional boundary, this can be validated or invalidated by comparing signal source data collected at that location to the signal source profile associated with that notional boundary. Additionally or alternatively, signal source data collected at a location inside or outside of a notional boundary can be compared to the signal source profiles associated with one or more notional boundaries relating to respective spatial features to determine whether that location is associated with the same spatial feature as one of the said notional boundaries (e.g. if the signal source data conforms to the signal profile).
  • Device Y begins to transmit to the server 8 location data relating to its changing location within the indoor region 20 when it is within an area of the room 26 outside notional boundary 44 (e.g. because it was switched on at that location, or because location services were switched on at that location on device Y) at a location adjacent to the finally detected location of device X.
  • Device Y initially changes location towards the end 84 of the room 26 before turning through 180° and changing location towards the main thoroughfare 22. This causes the device Y to change location through the end 86 of the notional boundary 44 and into the area around which the notional boundary 44 is provided.
  • Device Y keeps changing location in that direction until it reaches the main thoroughfare 22, where it turns through 90° and changes location along the main thoroughfare towards the turning point 34.
  • Said social media data may comprise any one or more of: text data; audio data; image data; video data (for example). Text, image or video data can be processed as set out above.
  • points of interest can be identified by obtaining audio data (which may be for example social media data (e.g. audio data uploaded to social media) or data recorded by the mobile device 1 and transmitted to the server 8) relating to the indoor region or notional boundary 42-52 (e.g. detected by one or more mobile devices 1 in the indoor region or in a respective notional boundary 42-52); and processing the said audio data to identify one or more points of interest within the indoor region or notional boundary 42-52.
  • audio data which may be for example social media data (e.g. audio data uploaded to social media) or data recorded by the mobile device 1 and transmitted to the server 8) relating to the indoor region or notional boundary 42-52 (e.g. detected by one or more mobile devices 1 in the indoor region or in a respective notional boundary 42-52); and processing the said audio data to identify one or more points of interest

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

L'invention concerne un procédé de mappage d'une région en intérieur, le procédé consistant à : obtenir des données de caractéristiques spatiales identifiant des emplacements d'une ou de plusieurs caractéristiques spatiales de la région en intérieur ; générer des données de disposition relatives à la région en intérieur en fonction des données de caractéristiques spatiales obtenues ; obtenir des données d'emplacement relatives à des changements d'emplacement d'une pluralité de dispositifs mobiles à l'intérieur de ladite région en intérieur ; et ajuster lesdites données de disposition en fonction des données d'emplacement obtenues.
PCT/GB2018/051312 2017-05-15 2018-05-15 Procédé de mappage de région en intérieur Ceased WO2018211261A1 (fr)

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