WO2015079260A1 - Appareil de recherche d'emplacement et procédés associés - Google Patents

Appareil de recherche d'emplacement et procédés associés Download PDF

Info

Publication number
WO2015079260A1
WO2015079260A1 PCT/GB2014/053550 GB2014053550W WO2015079260A1 WO 2015079260 A1 WO2015079260 A1 WO 2015079260A1 GB 2014053550 W GB2014053550 W GB 2014053550W WO 2015079260 A1 WO2015079260 A1 WO 2015079260A1
Authority
WO
WIPO (PCT)
Prior art keywords
estimated
movement
particle
data
particle set
Prior art date
Application number
PCT/GB2014/053550
Other languages
English (en)
Inventor
Simon Mark JORDAN
Trevor Michael WOOD
Original Assignee
Cambridge Consultants Limited
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Cambridge Consultants Limited filed Critical Cambridge Consultants Limited
Publication of WO2015079260A1 publication Critical patent/WO2015079260A1/fr

Links

Classifications

    • 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
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/14—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by recording the course traversed by the object
    • 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
    • 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
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • 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
    • 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
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
    • 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
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0257—Hybrid positioning
    • G01S5/0263—Hybrid positioning by combining or switching between positions derived from two or more separate positioning systems

Definitions

  • the present invention relates to location finding.
  • the invention has particular although not exclusive relevance to location finding apparatus and associated apparatus for location finding and tracking where conventional location finding apparatus, such as global positioning system (GPS) technology, or the like cannot be used effectively (e.g. indoors, underground or the like).
  • conventional location finding apparatus such as global positioning system (GPS) technology, or the like cannot be used effectively (e.g. indoors, underground or the like).
  • GPS global positioning system
  • GPS Global Positioning System
  • magnetometers magnetometers
  • conventional location finding technology such as GPS positioning
  • GPS positioning can be unreliable in some situations, in particular in environments where GPS signals cannot be received properly such as indoors, underground or in heavily forested areas. This can lead to situations where little or no location information can be derived for a user. This issue is compounded by the many applications in which accurate location finding and/or tracking would be desirable in scenarios where the conventional location finding technology cannot be used with sufficient accuracy.
  • One such application is the tracking of a device within a building or other GPS impaired location for the purposes of security, personnel or goods tracking, time-in- motion analysis and/or the like, in order to acquire information on current location, previous places visited, time spent in certain locations etc...
  • 'dead reckoning' or 'ded ('deduced') reckoning' in which a previously 'known' position or 'fix' is used in combination with estimates of distance and direction travelled in order to estimate a current position.
  • inertial sensors INS
  • INS inertial sensors
  • a significant limitation of such inertial sensing based dead reckoning is that the sensor measurements can be noisy and errors accumulate in the process of integrating the measured quantities to obtain the desired positional measurements (i.e. effectively integrating acceleration twice to obtain displacement and hence position).
  • preferred embodiments of the present invention aim to provide methods and apparatus which overcome or at least alleviate one or more of the above issues.
  • a portable device for tracking the location of a user carrying the device, the portable device comprising: means for generating a particle set comprising a plurality of particles each particle representing a respective estimate of a candidate state of the portable device, wherein the estimated candidate state comprises an estimated position of the portable device and at least one estimated error associated with movement of said device; means for detecting and analysing movement of the portable device: to estimate distance travelled by the device; and to determine an estimated direction of movement of the device; and means for adapting the particle set to reflect an estimated change in position represented by the estimated distance travelled by the device, and the estimated direction of movement of the device.
  • the at least one estimated error associated with movement of said device may comprise an estimated error in an estimated step length for said user.
  • the at least one estimated error associated with movement of said device may comprise an estimated error in the estimated direction of movement.
  • the at least one estimated error associated with movement of said device may comprise an estimated error in an estimated altitude change for said device.
  • the means for adapting the particle set to reflect an estimated change in position may be operable to adapt the particle set to refine the at least one estimated error associated with movement of said device as the device moves from location to location.
  • the means for adapting the particle set to reflect an estimated change in position may be operable to adapt the particle set to refine the at least one estimated error associated with movement of said device based on mapping data representing a map of a location in which said device is being used.
  • the means for adapting the particle set to reflect an estimated change in position may be operable to adapt the particle set based on data from at least one external source of data.
  • the external source of data may comprise at least one of: a source of satellite positioning signals; a source of user input; a source of non-satellite radio signals; a source of visible electromagnetic radiation; and/or a source infra-red radiation.
  • the external source of data may comprise a beacon emitting at least one of non- satellite radio signals; a source of visible electromagnetic radiation; and/or a source infra-red radiation.
  • the portable device may comprise: means for obtaining mapping data representing a (or the) map of a location in which the portable device is being used; wherein the means for adapting the particle set may be operable to adapt the particle set taking account of said mapping data to eliminate, or reduce a weighting associated with, a particle exhibiting an estimated movement which said mapping data indicates is unlikely to reflect a real movement.
  • a portable device for tracking the location of a user carrying the device, the portable device comprising: means for generating a particle set comprising a plurality of particles each particle representing a respective estimate of a candidate state of the portable device, wherein the estimated candidate state comprises an estimated position of the portable device; means for detecting and analysing movement of the portable device: to estimate distance travelled by the device; and to determine an estimated direction of movement of the device; means for obtaining mapping data representing a map of a location in which the portable device is being used; and means for adapting the particle set to reflect an estimated change in position represented by the estimated distance travelled by the device, and the estimated direction of movement of the device, taking account of said mapping data to eliminate, or reduce a weighting associated with, a particle exhibiting an estimated movement which said mapping data indicates is unlikely to reflect a real movement.
  • the means for adapting the particle set may be operable to take account of said mapping data by comparing an estimated position of the device represented by the particle following or during said estimated change in position, with said mapping data, and by eliminating, or reducing a weighting associated with, a particle if the estimated position represented by the particle following or during said estimated change in position is indicative of a movement that is unlikely to represent a real movement.
  • the means for adapting the particle set may be operable to determine that an estimated change in position is indicative of a movement that is unlikely to represent a real movement if the mapping data indicates that the particle exhibiting the estimated change in movement has passed through an obstacle.
  • the means for adapting the particle set may be operable to determine that an estimated change in position is indicative of a movement that is unlikely to represent a real movement if the mapping data indicates that the particle exhibiting the estimated change in movement has followed an unlikely path.
  • the means for adapting the particle set may be operable to determine that an estimated change in position is indicative of a movement that is unlikely to represent a real movement if the mapping data indicates that the particle exhibiting the estimated change in movement has passed into a restricted area.
  • the means for detecting and analysing movement of the portable device may be operable to detect movement of the device which is indicative of at least one step by the user and to estimate distance travelled by the device based on an estimated step length for said user.
  • a portable device for tracking the location of a user carrying the device comprising: means for generating a particle set comprising a plurality of particles each particle representing a respective estimate of a candidate state of the portable device, wherein the estimated candidate state comprises an estimated position of the portable device; means for detecting and analysing movement of the portable device: to detect movement of the device which is indicative of at least one step by the user; to estimate distance travelled by the device based on an estimated step length for said user; and to determine an estimated direction of movement of the device; and means for adapting the particle set to reflect an estimated change in position represented by the estimated distance travelled by the device, and the estimated direction of movement of the device.
  • the means for detecting and analysing movement of the portable device may be operable to determine a type of step taken and to estimate distance travelled by the device based on an estimated length of said type of step for said user.
  • the means for detecting and analysing movement of the portable device may be operable to detect and analyse said movement based on inertial data.
  • the means for detecting and analysing movement of the portable device may be operable to detect and analyse said movement based on respective inertial data received from a plurality of inertial sensors and to apply a data fusion process to the respective inertial data, prior to analysing said movement, to produce refined inertial data on which the movement analysis is based.
  • the means for detecting and analysing movement of the portable device may be operable to apply a data fusion process to the respective inertial data using a Kalman filter (preferably an extended Kalman filter).
  • a Kalman filter preferably an extended Kalman filter
  • the means for detecting and analysing movement of the portable device may be operable to detect movement of the device which is indicative of a change in altitude and to estimate the change in altitude; and wherein said means for adapting the particle set to reflect an estimated change in position may be operable to adapt the particle set to reflect the change in altitude.
  • the estimated candidate state may comprise a position in at least two dimensions, optionally in at least three dimensions.
  • a method performed by a portable device for tracking the location of a user carrying the device comprising: generating a particle set comprising a plurality of particles each particle representing a respective estimate of a candidate state of the portable device, wherein the estimated candidate state comprises an estimated position of the portable device and at least one estimated error associated with movement of said device; detecting and analysing movement of the portable device: to estimate distance travelled by the device; and to determine an estimated direction of movement of the device; and adapting the particle set to reflect an estimated change in position represented by the estimated distance travelled by the device, and the estimated direction of movement of the device.
  • a method performed by a portable device for tracking the location of a user carrying the device comprising: generating a particle set comprising a plurality of particles each particle representing a respective estimate of a candidate state of the portable device, wherein the estimated candidate state comprises an estimated position of the portable device; detecting and analysing movement of the portable device: to estimate distance travelled by the device; and to determine an estimated direction of movement of the device; obtaining mapping data representing a map of a location in which the portable device is being used; and adapting the particle set to reflect an estimated change in position represented by the estimated distance travelled by the device, and the estimated direction of movement of the device, taking account of said mapping data to eliminate, or reduce a weighting associated with, a particle exhibiting an estimated movement which said mapping data indicates is unlikely to reflect a real movement.
  • a method performed by a portable device for tracking the location of a user carrying the device comprising: generating a particle set comprising a plurality of particles each particle representing a respective estimate of a candidate state of the portable device, wherein the estimated candidate state comprises an estimated position of the portable device; detecting and analysing movement of the portable device: to detect movement of the device which is indicative of at least one step by the user; to estimate distance travelled by the device based on an estimated step length for said user; and to determine an estimated direction of movement of the device; and adapting the particle set to reflect an estimated change in position represented by the estimated distance travelled by the device, and the estimated direction of movement of the device.
  • a computer implementable instructions product comprising computer implementable instructions for causing a programmable computer device to perform any one of the above methods or to become programed as one of the above devices.
  • aspects of the invention extend to computer program products such as computer readable storage media having instructions stored thereon which are operable to program a programmable processor to carry out a method as described in the aspects and possibilities set out above or recited in the claims and/or to program a suitably adapted computer to provide the apparatus recited in any of the claims.
  • Figure 1 schematically illustrates a location system
  • Figure 2 schematically illustrates operation of the indoor system of Figure 1 whilst tracking a journey through a building
  • Figure 3 schematically illustrates apparatus for use in the location system of Figure 1 to track a journey through a building such as that illustrated in Figure 2;
  • Figure 4 is a simplified flow chart illustrating operation of the apparatus of Figure 2 to classify motion
  • Figure 5 is a simplified flow chart illustrating operation of the apparatus of Figure 2 to provide position estimates
  • Figure 6 is a simplified flow chart illustrating, in more detail, operation of the apparatus of Figure 2, during part of the process of Figure 5 to update a particle set and perform a position estimate.
  • a portable device for tracking the location of a user carrying the device is described by way of example with reference to the drawings.
  • the portable device generates a particle set comprising a plurality of particles each particle representing a respective estimate of a candidate state of the portable device, wherein the estimated candidate state comprises an estimated position of the portable device. Movement of the portable device is detected and analysed to estimate distance travelled by the device and to determine an estimated direction of movement of the device and the particle set is updated to reflect an estimated change in position represented by the estimated distance travelled by the device, and the estimated direction of movement of the device.
  • FIG. 1 schematically illustrates a location system generally at 110.
  • the location system 1 10 comprises a location tracking device 1 12 that is configured for maintaining a position estimate for the location tracking device 1 12 and tracking its position as it moves through a location in which other forms of location derivation (such as global position system (GPS) location derivation, base cellular station trilateration, or the like) are not possible or not particularly effective.
  • location derivation such as global position system (GPS) location derivation, base cellular station trilateration, or the like
  • GPS global position system
  • base cellular station trilateration base cellular station trilateration
  • the location tracking device 1 12 is shown moving from an external, or Outdoor', location into an internal, or 'indoor', location in a building 1 14 via a door 116.
  • the location tracking device 112 in this example, is configured to be carried by a human user.
  • the location tracking device 112 uses inertial sensors to produce motion data for the device 1 12 as the user of the device 112 moves through the building 114.
  • the location tracking device 112 determines an estimate of the speed and azimuth bearing (' ⁇ ') of the movement of the device 1 12 based on the motion data and produces position estimates for the device 1 12 based on the estimated speed and azimuth bearing.
  • the location tracking device 1 12 classifies motion and produces estimates of the speed of the device based on pre-existing knowledge about human motion characteristics. This knowledge is used to derive more accurate motion information than simple use of the inertial sensors to produce an estimated speed and direction would normally allow.
  • the pre-existing knowledge about human motion characteristics may be in the form of one or more motion profiles including, for example, information representing an average step length ('s'), possibly for each of a plurality of different inertial characteristics associated with a respective different step type (e.g. walking / running / climbing or descending stairs or ramps etc.).
  • the pre-existing knowledge about human motion characteristics may also comprise one or more motion profiles representing movement that can be ignored such as, for example, movement indicative of non- movement related user handling (or mishandling) of the device 112.
  • each motion profile is, itself, refined dynamically (On the fly') if information from other data sources indicates that the motion profile does not precisely match the motion characteristics of the current user of the device.
  • the device 112 is able to 'self-learn' by configuring itself more and more closely to the motion characteristics of a particular user as the motion profile(s) for that user become more accurate. This helps to reduce the build-up systematic errors in the position estimates, as the device moves through the building.
  • the device 1 12 employs Bayesian statistical data fusion techniques. These techniques allow improved extraction of information from noisy data sources.
  • the location tracking device 1 12 also refines its position estimates using a particle filtering technique (also known as a 'Sequential Monte Carlo' (SMC) technique) that is enhanced to help reduce further the build up systematic errors in the position estimates, as the device moves through the building.
  • Particle filtering is a technique for estimating a state that changes over time using a sequence of potentially noisy measurements. It works by approximating the probability distribution for the state by a set of particles.
  • the particle filtering technique of this example advantageously uses a multidimensional 'state-space' with system states representing not only an estimated position of the user but also an estimate of the errors in the motion measurements that resulted in that estimate position.
  • the particle filtering technique uses a four-dimensional dimensional 'state-space' (rather than a simple two-dimensional state-space), for movement in two-dimensions (i.e. on substantially the same level), with each 'state' being represented by a position in an east-west direction (' ⁇ '), a position in a north-south direction ('y'), a bearing error (' ⁇ ⁇ ') and a step length error ('e s ').
  • the state space may be enhanced, for example to take account of movement in three dimensions with each state being a six-dimensional state represented by the 'x' position, the y position, a position in the up-down direction (' ⁇ '), a bearing error (' ⁇ ⁇ '), a step length error ('e s '), and an altitude change error ('e a ').
  • the particle filter is advantageously able to estimate the location of the device 1 12 (the quantity of interest) whilst simultaneously estimating and correcting for the error sources which are most likely to degrade performance.
  • a particle distribution for an estimated initial state ([x, y, e e , e s ] T0 ) is determined based on an estimated initial position 120-1 derived from pre-existing knowledge of the user's position immediately prior to entry to the building.
  • This determination is triggered by an event such as, for example: a loss of a conventional positioning capability (e.g. GPS / base station trilateration); a user input (e.g. pressing a button on the device 1 12); and/or the device entering a pre-defined geographic zone as determined by the conventional positioning capability.
  • the preexisting knowledge of the user's position may comprise an estimated position determined by the conventional positioning capability; a position derived from stored mapping data (e.g. a known position of the entrance door 116); and/or the like.
  • a multi-dimensional probability density function associated with the estimated initial position (e.g. based on a known error distribution associated with the conventional positioning capability) is sampled to produce a set of candidate states for the estimated initial position where each candidate state is treated as a distinct particle (S10).
  • Each particle is weighted, in this initial state, according to the probability associated with that particle from the probability density function (S12) taking account of any pre-existing knowledge about the initial position or the measurements from which the initial position was estimated (e.g. mapping data defining a known position of the entrance door 1 16 and/or uncertainty levels/error distributions associated with a conventional positioning measurement).
  • mapping data defining a known position of the entrance door 1 16 and/or uncertainty levels/error distributions associated with a conventional positioning measurement.
  • the device 1 12 When the device 1 12 moves to the second location 120-2 the device 1 12 is able to detect and classify the movement from the motion data output from the inertial sensors.
  • the device 112 is able to use this motion data to determine each time a step has been taken by a user of the device which, in combination with a motion profile representing an expected average step length ('s'), allows the distance moved to be estimated.
  • the device 1 12 is also able to determine, from the motion data output from the inertial sensors, an estimated bearing (' ⁇ ').
  • the device 1 12 is able to predict a change in position for each particle based on the estimated step length ('s'), bearing (' ⁇ ') and associated error estimates (e e , e s ) based on the inertial sensing data (S14).
  • the device 112 updates (at S16) the respective weight of each particle based on any available measurement data (including measurement data from conventional positioning features if available) and/or data entered by a user such as a room identifier.
  • any available measurement data including measurement data from conventional positioning features if available
  • data entered by a user such as a room identifier.
  • the device 1 12 is still able to make inferences about the likelihood that the movement of a particular particle and hence its current position is genuine based on stored mapping and/or other data for the building 1 14 (or other location) in which the device 1 12 is being used.
  • mapping data derived from an image of the building plan can be used as additional information to constrain possible user routes through the building and help to prevent accumulation of errors.
  • the particle's weight can be reduced to zero (or near zero) to indicate that that particle is very unlikely to represent the true position of the device 1 12.
  • a particle is found to take a lower probability route (e.g. very close to the edge of a doorway rather than through its centre) then its weight can be reduced commensurately.
  • the device 1 12 is also able to update a particle by adjusting the position and/or errors (state) associated with that particle to move it to a state that is more likely to be correct (this can be instead of, or in addition to, adjusting its weight).
  • a Bayesian statistical framework is used that allows the integration of multiple, potentially disparate types of information, for example allowing the same algorithm to make use of the information in GPS data, user inputs or other radio signals if any of these are available.
  • Bayesian statistical framework and the Bayesian statistical data fusion techniques referred to earlier employed involvejhe application of Bayes rule to calculate the probability distribution of quantities of interest from a given set of measurements.
  • Different types of measurement e.g. GPS, inertial data, wi-fi signal
  • Bayes rule is used to incorporate the information in each measurement according to its noise characteristics balanced against the existing weight of information.
  • Bayes rule or 'Bayes Theorem'
  • the modified probability density represented by the re-weighted particle distribution is re-sampled to remove low weight particles and to spawn new particles having a state close to that of the highest weight particles (at S18).
  • the new 're- sampled' particle set resulting from the re-sampling process has the same overall number of particles as were included in the old set of particles.
  • Each particle in the re-sampled particle set is then assigned a new, equal, weight whilst maintaining the unity sum of all particle weights (hence the weight of each particle is equal to the reciprocal of the number of particles in the particle set).
  • the updated particle set is used (at S20) to determine a state estimate [x, y, e e , e s ] T1 for the second location 120-2 by calculating, in this example, the mean over the set of particles.
  • a measure of covariance can also be determined directly from the updated particle set in a straightforward way.
  • This process is, in effect, repeated on a regular basis as the device 1 12 is carried around the building via each subsequent location 120-r.
  • Each estimated state is stored for subsequent download (or possibly concurrent download if appropriate communication features are available such as Wi-Fi, Bluetooth or the like) to determine the journey taken by the device.
  • the particle set for a later system state is refined based on the mapping data and any external sources of measurement data that may, intermittently, become available (e.g. GPS)
  • the previously estimated earlier 'historic' states are also refined, based on the acquired knowledge represented by the newly refined particle set. This allows the overall journey taken by the device 112 to be represented, in memory, with increasing accuracy as the journey progresses.
  • the 'map matching' or particle filter applied here advantageously keeps track not only of the position, but also the causes of the accumulation of error in position. In this way, using the map, or other available information, it is possible to estimate the errors in step length and heading in order to arrest the accumulation of error.
  • the map matched particle filtering allows the device to "learn" that the direction of heading has a 5° error.
  • information from the map can be used to refine the step length used for predicting the movement of that user without having direct knowledge of the user's height or other information from which step length can be determined directly.
  • Figure 2 illustrates, generally at 200, an exemplary journey 202 of the device 112 through a building, and in particular the refinement of a particle set 204-A to 204-E at different locations (A to E) in the journey 202.
  • the particle set at each location is represented by a number of white dots, each dot representing a respective particle, with the resulting estimated state represented as a black dot. It is assumed that, during the journey 202, there are no external sources of measurement data such as GPS, signals/fields emitted from locator beacons or the like. Further, the particle sets 204 shown represent substantially 'real time' determinations.
  • the initial particle set is relatively compact representing a relatively tight probability density.
  • the particles of the particle set begin to spread out, especially along the direction of movement, as uncertainty increases as a result of the accumulation of systematic errors in step length.
  • Systematic errors in the bearing calculation result in a deviation from the path of the actual journey 202 through the building.
  • the accumulation of systematic errors in the step length and bearing calculations, at location C has built up resulting in further spreading of the particle set and deviation from the path of the actual journey 202.
  • some uncertainty in the route taken is introduced with a number of particles 206 of the particle set 204-C being determined to have taken a path on the wrong side of a wall 213. Whilst, initially, the particles 206 following the incorrect route continue to be tracked, the emphasis placed on them is reduced by reducing their weighting because the mapping data indicates that the path taken by them (being towards a corner of a room) is less likely than the more common pathway through the doorway 212.
  • the particles 206 following the incorrect route are initially 'moved' from any position calculation that takes them through wall 213 to move alongside the wall while the route they are determined to take remains sufficiently likely but, ultimately, have their weights reduced to zero, or near zero, as they begin to deviate significantly from the route taken by the main cluster of particles and/or when they pass through another wall 214 indicating that the route taken is not merely unlikely, but is in fact impossible (assuming the mapping data is correct).
  • the particles 206 following the incorrect route are eliminated when the particle set is re- sampled and the device, in effect, learns that the bearing has had a systematic error and, effectively, corrects for it.
  • the device 1 12 is able to infer that the user of the device is close enough to the door to open it (and possibly close enough to a door release button / security pad or the like) thereby allowing the particles further from that position to have their weights reduced accordingly.
  • the changes to particle weighting based on inferences made from the mapping data and motion characteristics effectively result in a reduction in the spread of the cluster of particles and hence a refinement to the probability distribution represented by the particle set.
  • the cluster of particles at location D is more tightly packed with a system state estimate that is significantly more accurate than at location C.
  • the particle set 204 continues to be refined with particles that appear to go through walls (e.g. the circled particles at locations E and F) being reweighted to zero and eliminated during re-sampling thereby allowing further refinement both to the real-time state estimate and to the historic system states.
  • FIG 3 is a block diagram illustrating a location tracking device 1 12 for use in the location system of Figure 1 to track a journey through a building such as that illustrated in Figure 2.
  • the location tracking device 112 has a number of sources 320 of movement/position related data (and possibly other data), a processing unit 330 for processing data from the data sources, and an output unit 340 for providing the results of processing by the processing unit 340 for real-time and/or non-real-time access by the user carrying the device 1 12 or by another user (e.g. an administrative user or managerial user).
  • the data sources 320 comprise: sensors 321 for sensing movement and providing associated movement/position related data; sources of external data 322 for providing associated movement/position related data based on signals from external sources; and data storage 323 for storing movement/position related data.
  • the sensors 321 comprise inertial / direction sensors 323 for sensing motion and orientation and for providing associated data from which the device 1 12 can determine a bearing and can classify user motion appropriately (for example as a walking step, as a pause, as a step up or down a ramp or stair, a running step, or the like).
  • the inertial / direction sensors 323, in this example, comprise a multi-axis (e.g.
  • the inertial sensors 323 are sufficient to achieve this.
  • Such two- dimensional tracking may, for example, be sufficient in a building where there is little or no level ambiguity (e.g. a single floored building), or in a multi-floored building where movement around each floor is tracked separately in two-dimensions with an external input (e.g. from a user or a beacon) used to confirm which floor the user is on (and hence which floor plan representation to use from the mapping data).
  • the sensors 321 comprise a pressure sensor 324 to provide motion/position related data representing altitude (movement up/down in a 'z' direction orthogonal to the x and y directions).
  • the sources of external data 322 also comprise a user input module 325 for acquiring user input relating to the device's position (and other user input) and for providing associated measurement/position data.
  • the user input relating to the device's position may be a simple indicator (e.g. on the press of a button) that the user has entered or left a building, or may comprise an identifier of a general location (e.g. building, floor, room, doorway, or the like) that the user has entered or is about to enter.
  • the sources of external data 322 may also comprise one or more radio positioning modules 327 adapted to detect associated radio signals from sources having a known position, to measure the strength, or other characteristic, of any detected signals and to provide associated measurement/position data to improve refinement of the particle set.
  • a cellular radio positioning module may be provided for detecting and measuring the strength of radio signals from a cellular base station (macro, pico, femto or otherwise), for identifying the originating base station, and for estimating a position relative to that base station.
  • a beacon based radio positioning module may alternatively or additionally be provided for detecting signals from dedicated positioning beacons provided within the building, for identifying the originating beacon, and for providing the information identifying the beacon as measurement/position data for use in refining the estimated position of the device 112.
  • the identified beacon's location may be determined (e.g. by reference to a look-up table mapping beacon identity to a specific location) and this information may be used either by itself, or together with a signal strength based estimate of distance from the identified beacon (also provided by the radio positioning module), to refine the positioning.
  • a respective beacon having a unique identity may be provided at the exit of an elevator on each floor of a multi-floored building.
  • the device 1 12 is able to determine onto which floor (and from which elevator) of a building the device 1 12 has entered. This would allow two-dimensional tracking to continue based on mapping data representing a floor-plan of the identified floor but could also be used to refine a particle set for three-dimensional tracking by helping to reduce errors in altitude associated with pressure measurements.
  • radio positioning modules include wi-fi, Bluetooth, Near Field Communication (NFC) based radio positioning modules or the like.
  • Other positioning modules having a similar function may employ other parts of the electro-magnetic spectrum and may include, for example, modules for positioning using visible and/or infra-red parts of the spectrum
  • the data storage 323 comprises mapping data 328 representing a map of each floor of the building (or other location) that the device 1 12 is being used in. This mapping data may be pre-loaded onto the device 1 12 and/or may be loaded onto the device, or updated, using an appropriate wired or wireless communication technology.
  • the data storage 323 also includes other information for assisting location tracking including, for example, one or more motion profiles 329 each representing a particular user movement characteristic such as a walking step, a pause, a step up/down a ramp or stair, a running step, upward/downward movement in an elevator and/or the like.
  • the data storage 323 may also comprise other data for assisting positioning and/or motion classification such as, for example: look-up data for mapping beacon identities to specific geographic locations represented by the mapping data; data representing a radio signal characteristics map for comparison with corresponding measured radio signal characteristics to determine a device position relative to the radio signal characteristics map and hence in the building; and any other such data.
  • the processing unit 330 comprises a motion analysis and classification module 332 and a particle filter module 334.
  • the motion analysis and classification module 332 performs data fusion and analysis procedures on the raw data from the sensors 321 to help reduce errors and thereby provide improved motion data.
  • the motion analysis and classification module 332 classifies the motion represented by the improved motion data based on the motion profile(s) stored in the data storage 323 and reports a bearing estimate and step length (and possibly a step height for movement up or down stairs) to the particle filter module 334 each time a step is detected.
  • the motion analysis and classification module 332 may also report detection of other events to the particle filter module 334 such as, for example, detected movement in an elevator (with or without an associated altitude change estimate), an apparent pause in movement, or the like.
  • the particle filter module 334 produces and weights the particle set 204, predicts the movement of individual particles, as the device 1 12 moves through a building or other such location, based on the data from the motion analysis and classification module 332.
  • the particle filter module 334 uses Bayesian statistics to update the weighting of each particle in the particle set 204 based on data from other disparate data sources such as the satellite positioning module 325, the user interface 326, other radio positioning module(s) 327, the stored mapping data 328, or other stored data.
  • the re-weighted particle set 204 is resampled by the particle filter module 334 to produce a new set of equally weighted particles representing a refined probability density for the system state (position and errors) of the device 1 12.
  • the particle filter module 334 generates, from the re-weighted and resampled particle set 204, a real- time estimate of the system state and hence position.
  • the particle filter module 334 also uses the re-weighted and resampled particle set 204 to update particle sets, and hence estimates, for previously determined historic system states and hence position.
  • the output unit 340 provides the means by which the device 1 12 can output, to a user, the real-time position estimate 342, the historic position estimate 344 and any other real-time or previously stored data (such as real-time and/or historic particle sets, motion profiles, radio characteristics maps etc.).
  • the output unit 340 can output the information via any suitable output device or interface (e.g. audio, visual, wired or wireless data interface, or the like) either automatically (e.g. real-time on a display) or at the request of the user via the user interface 326.
  • Figure 4 is a simplified flow chart illustrating operation of the apparatus of Figure 2 to classify motion.
  • data fusion is performed on the raw inertial and any pressure sensor data 410 at S40 to refine that data.
  • a Kalman filter (or extended Kalman filter or other similar method) is used in a 'pre-processing' step, to correct errors in the measurement data from the inertial / direction sensors 323 (gyroscope / accelerometer / magnetometer) and any pressure sensor 324, such as errors associated with stray magnetic fields registered by the magnetometer, acceleration errors, and/or errors associated with pressure fluctuations registered by any pressure sensor 324.
  • the data fusion process uses the corrected data to produce refined data 412 representing quantities of interest such as device orientation, acceleration in directions of interest, and refined pressure differentials.
  • the refined data 412 is used, at S42, in combination with motion characterisation data 414 (e.g. motion profiles) to classify the motion and produce classified motion information 416 which may, for example, include information identifying the nature of steps being taken by a user (e.g. estimated step length, step frequency and severity), the user direction of heading (azimuth bearing), and whether the user is going up or down stairs.
  • motion characterisation data 414 e.g. motion profiles
  • classified motion information 416 may, for example, include information identifying the nature of steps being taken by a user (e.g. estimated step length, step frequency and severity), the user direction of heading (azimuth bearing), and whether the user is going up or down stairs.
  • Figure 5 is a simplified flow chart illustrating operation of the apparatus of Figure 2 to provide updated position estimates in more detail. In Figure 5 it is assumed that the device 1 12 has already commenced a journey and one or more previous position estimates have been made.
  • any available data 510 from the external data sources 322 e.g. satellite positioning data / user input data / or positioning data based on other radio sources
  • classified motion information 512 from the motion analysis and classification module 332 is processed at S50 to determine if there is any new information (or information that has changed) and the type of any new (or changed) information.
  • Any new or changed information found at S50 that is required by the particle filter module 334 is then extracted, to provide appropriate updated information, using one of a plurality of different information type dependent update procedures (S52-1 to S52-4).
  • the particle filter module 334 is then used, at S54, to update the weightings of the current particle set based on the updated information and any relevant mapping data 514, to resample the particle set, and then to extract a real-time position estimate 516.
  • the current and/or updated particle set can alternatively or additionally be buffered, at S56, for refinement (or further refinement) in the future based on subsequent information updates to provide a delayed but refined historic position estimate 518. Updating Particle Set based on Motion Information
  • Figure 6 is a simplified flow chart illustrating, in more detail, operation of the apparatus of Figure 2, during part of the process of Figure 5 to update a particle set and perform a position estimate.
  • the update is based on updated motion information and mapping data without other external sources of measurement such as GPS or the like.
  • classified motion information 612 from the motion analysis and classification module 332 is used, in conjunction with a current particle set 610, at S60, effectively to 'move' each particle in accordance with the estimated movement (estimated distance and estimated direction) indicated by the classified motion information 612.
  • the state represented by each particle is modified in accordance with a so called 'forward model'.
  • the model may be written as:
  • v is random noise to account for unmodelled deviations from the deterministic part of the forward model.
  • the random noise is assumed to be zero- mean multivariate and normally distributed.
  • the procedure for determining which particles have crossed a wall minimises computation by only making full particle/wall comparisons where necessary.
  • the procedure (in the case of straight walls) is as follows:
  • mapping data can be used for to make other inferences than whether the particle exhibits movement through a wall.
  • a further example of how the map could be used to update particle weights is to apply larger weights to "indoor” particles than to "outdoor” particles if no satellite tracking (e.g. GPS) signals are received for a long time period thereby indicating that the device 1 12 is indoors and that outdoor particles are less likely to represent a genuine position.
  • satellite tracking e.g. GPS
  • the particle set is re-weighted it is resampled at S66 to remove lower weight particles and spawn further particles from higher weight particles. This ensures that computational effort is focussed more efficiently by concentrating on propagating particles in relevant areas of the state space.
  • Resampling is carried out every time step as follows:
  • N the (constant) number of particles.
  • Jitter is applied in step (c) because the process of resampling can result in multiple copies of a single (higher-weight) particle. This can have the side-effect of reducing the diversity in the particle set.
  • the particle diversity can be increased by adding a small amount of random noise to each particle during resampling. This process is referred to as "jittering" and ensures that there continues to be a particle set with a diversity of states able to cover more of the possible user states.
  • jittering This process is referred to as "jittering" and ensures that there continues to be a particle set with a diversity of states able to cover more of the possible user states.
  • the addition of random jitter is known to improve performance as it increases the sample diversity of the particles.
  • the e e and e s components are each jittered by a zero mean, normally distributed component.
  • the location components of the state vector are not jittered to avoid having to perform wall- handling again.
  • the real-time system state is estimated and the historic system states updated at S68 to provide a real-time position estimate 616 and an updated historic position estimate 618.
  • the standard method for obtaining a state estimate is to calculate the mean over the set of particles. Covariance can also be calculated directly from the particle set. It will be appreciated, however, that more sophisticated state summaries which take into account the possibility of multi-modal distributions may also be inferred from the particle set. Updating Particle Set based on Location Information
  • the particle filter module 334 will sometimes receive satellite location estimates, denoted (X G PS > YGPS) along with associated uncertainty level, a GPS .
  • the weights for the set of particles are adjusted proportionally to how well they fit with the satellite location measurement.
  • d denotes the vector between the satellite location estimate and a single particle's location estimate
  • w b and w a the equation for the updated weight of the particle is given by:
  • This weighting corresponds to the assumption that satellite positioning errors are normally distributed with zero mean and standard deviation a GPS . From the viewpoint of Bayesian statistics, the above weighting corresponds to scaling the particle weighting by the likelihood function for a satellite measurement under the above assumptions.
  • the apparatus described above is configured for carrying out a method involving generating a particle set comprising a plurality of particles each particle representing a respective estimate of a candidate state of the object, wherein the estimated candidate state comprises an estimated position of the object and at least one estimated error associated with movement of said object.
  • the apparatus is further configured for carrying out a method involving detecting and analysing movement of the object: to estimate distance travelled by the object; and to determine an estimated direction of movement of the object, and adapting the particle set to reflect an estimated change in position represented by the estimated distance travelled by the object, and the estimated direction of movement of the object. It can be seen that this provides benefits in terms of improved location tracking.
  • the improvements may include, inter alia, improved accuracy of position estimates, greater flexibility to use the apparatus in a wide range of different situations and/or by different users, and faster real-time tracking of position.
  • the location tracking device may be a stand-alone dedicated tracking device or may be integrated as part of another device.
  • the location tracking device may form part of a mobile communications device such as a mobile (cellular) telephone, personal digital assistant, lap top, notebook, tablet computer, and/or the like.
  • the location tracking device may be arranged to track the location based on something other than the steps taken by a pedestrian user.
  • the technique may be applied to tracking the location of a vehicle, such as a cart used by a warehouse picker, a shopping trolley use by a supermarket customer, a ride-on mobility vehicle, or the like.
  • the errors forming part of the particle state may comprise, for example, a 'step' length error representing an error in a unit distance travelled by the vehicle.
  • the 'step' length error may, for example, represent an error in the unit distance travelled during a single rotation (or other unit number of rotations) of a wheel of the vehicle (or any other suitable distance measurement). It will be appreciated that, in such a scenario, as the particle set is refined, this error will also beneficially be refined automatically to take account of errors associated with unknown local environmental factors that may cause errors in the distance measurement.
  • wheel slippage may vary dependent on the co-efficient of friction of the surfaces on which the wheel is rotating and/or the different forces exerted by different users on the wheel. Accordingly, if a pre-configured fixed 'step' length error were used it could result in an accumulation of distance measurement errors associated with the wheel slippage and hence significant inaccuracies on position estimates. Contrastingly, by including the step length error as part of the particle state, the error component of the particle state is automatically refined without requiring any detailed knowledge of the local environment such as surface types, use case etc..
  • wall handling as part of Forward Model
  • wall handling could be provided as part of the forward model.
  • the implementation of wall handling as part of the forward model may, for example, involve: performing the forward model step; if a particle is found to go through a wall as part of the forward model step re-sampling the particle again; and repeating until the particle no-longer passes through a wall.
  • a more sophisticated approach to alleviate this noise source may be employed which takes advantage of the fact that satellite errors are known to be time-correlated.
  • the device 1 12 may be improved by the use of two additional dimensions in the system state, to take account of satellite offset errors, and to allow the time- correlated satellite errors to be taken into account. This effectively incorporates additional states into the particle filter to represent satellite offset error in the same way that step length error and heading error are handled.
  • the location components (x, y) of the system state are not jittered, in order to avoid performing wall-handling multiple times for a one time step, it will be appreciated that they may be jittered if particularly high performance is needed. Nevertheless, accurately estimating the main error sources generally has a larger impact on performance than small positional variations and so performance is not significantly degraded, for most applications, by not jittering the position.
  • a number of processing modules and processing steps were described. As those skilled in the art will appreciate, where the processing modules and/or steps described above are implemented in software the software may be provided in compiled or un-compiled form and may be supplied to the device as a signal over a computer network, or on a recording medium. Further, the functionality performed by part or all of this software may be performed using one or more dedicated hardware circuits.

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Navigation (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

La présente invention porte sur un appareil de suivi d'emplacement d'objet. L'appareil génère un ensemble de particules comprenant une pluralité de particules, chaque particule représentant une estimation respective d'un état candidat de l'objet, l'état candidat estimé comprenant une position estimée de l'objet. Un déplacement de l'objet est détecté et analysé pour estimer une distance parcourue par l'objet et pour déterminer une direction estimée de déplacement de l'objet et l'ensemble de particules est actualisé pour refléter un changement estimé de position représenté par la distance estimée parcourue par l'objet, et la direction estimée de déplacement de l'objet.
PCT/GB2014/053550 2013-11-29 2014-11-28 Appareil de recherche d'emplacement et procédés associés WO2015079260A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
GB1321129.7 2013-11-29
GB1321129.7A GB2520751A (en) 2013-11-29 2013-11-29 Location finding apparatus and associated methods

Publications (1)

Publication Number Publication Date
WO2015079260A1 true WO2015079260A1 (fr) 2015-06-04

Family

ID=49979569

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/GB2014/053550 WO2015079260A1 (fr) 2013-11-29 2014-11-28 Appareil de recherche d'emplacement et procédés associés

Country Status (2)

Country Link
GB (1) GB2520751A (fr)
WO (1) WO2015079260A1 (fr)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105865448A (zh) * 2016-03-18 2016-08-17 常州大学 一种基于imu的室内定位方法
US9817102B1 (en) 2016-06-10 2017-11-14 Apple Inc. De-weighting of outlier signals in a radio frequency navigation system
CN108072371A (zh) * 2016-11-18 2018-05-25 富士通株式会社 定位方法、定位装置和电子设备
US10214180B2 (en) 2016-06-24 2019-02-26 Emerson Electric Co. Systems and methods for machine sensing and communication
WO2021009499A1 (fr) 2019-07-17 2021-01-21 Waymap Limited Appareil et procédés associés permettant d'estimer une longueur de pas
CN116866842A (zh) * 2023-09-05 2023-10-10 成都健康医联信息产业有限公司 一种传染病跨时空目标追踪预警方法、系统、终端及介质

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3680618A1 (fr) 2019-01-10 2020-07-15 Technische Universität München Procédé et système de suivi d'un dispositif mobile
US20210396522A1 (en) * 2020-06-17 2021-12-23 Microsoft Technology Licensing, Llc Pedestrian dead reckoning using map constraining features
GB2612124A (en) * 2021-10-22 2023-04-26 Waymap Ltd Location finding apparatus and associated methods

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1923763A1 (fr) * 2006-11-16 2008-05-21 Samsung Electronics Co., Ltd. Procédé, appareil, et support pour évaluer la pose d'un robot mobile utilisant un filtre à particules
WO2012158441A1 (fr) * 2011-05-13 2012-11-22 Google Inc. Localisation en intérieur de dispositifs mobiles
US20130053056A1 (en) * 2011-08-29 2013-02-28 Qualcomm Incorporated Facilitating mobile device positioning
WO2013055959A1 (fr) * 2011-10-11 2013-04-18 Qualcomm Incorporated Système et/ou procédé pour navigation de piéton
US8504292B1 (en) * 2011-05-05 2013-08-06 Bentley Systems, Incorporated Indoor localization based on ultrasound sensors
US8548738B1 (en) * 2011-07-08 2013-10-01 Google Inc. Constructing paths based on a particle model

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1923763A1 (fr) * 2006-11-16 2008-05-21 Samsung Electronics Co., Ltd. Procédé, appareil, et support pour évaluer la pose d'un robot mobile utilisant un filtre à particules
US8504292B1 (en) * 2011-05-05 2013-08-06 Bentley Systems, Incorporated Indoor localization based on ultrasound sensors
WO2012158441A1 (fr) * 2011-05-13 2012-11-22 Google Inc. Localisation en intérieur de dispositifs mobiles
US8548738B1 (en) * 2011-07-08 2013-10-01 Google Inc. Constructing paths based on a particle model
US20130053056A1 (en) * 2011-08-29 2013-02-28 Qualcomm Incorporated Facilitating mobile device positioning
WO2013055959A1 (fr) * 2011-10-11 2013-04-18 Qualcomm Incorporated Système et/ou procédé pour navigation de piéton

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
ANSHUL RAI ET AL: "Zee", MOBILE COMPUTING AND NETWORKING, ACM, 2 PENN PLAZA, SUITE 701 NEW YORK NY 10121-0701 USA, 22 August 2012 (2012-08-22), pages 293 - 304, XP058009223, ISBN: 978-1-4503-1159-5, DOI: 10.1145/2348543.2348580 *
MORITZ KESSEL ET AL: "Automated WLAN calibration with a backtracking particle filter", INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN), 2012 INTERNATIONAL CONFERENCE ON, IEEE, 13 November 2012 (2012-11-13), pages 1 - 10, XP032313213, ISBN: 978-1-4673-1955-3, DOI: 10.1109/IPIN.2012.6418907 *

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105865448A (zh) * 2016-03-18 2016-08-17 常州大学 一种基于imu的室内定位方法
US9817102B1 (en) 2016-06-10 2017-11-14 Apple Inc. De-weighting of outlier signals in a radio frequency navigation system
US10214180B2 (en) 2016-06-24 2019-02-26 Emerson Electric Co. Systems and methods for machine sensing and communication
US10576930B2 (en) 2016-06-24 2020-03-03 Emerson Electric Co. Systems and methods for machine sensing and communication
CN108072371A (zh) * 2016-11-18 2018-05-25 富士通株式会社 定位方法、定位装置和电子设备
CN108072371B (zh) * 2016-11-18 2021-05-11 富士通株式会社 定位方法、定位装置和电子设备
WO2021009499A1 (fr) 2019-07-17 2021-01-21 Waymap Limited Appareil et procédés associés permettant d'estimer une longueur de pas
CN116866842A (zh) * 2023-09-05 2023-10-10 成都健康医联信息产业有限公司 一种传染病跨时空目标追踪预警方法、系统、终端及介质
CN116866842B (zh) * 2023-09-05 2023-11-21 成都健康医联信息产业有限公司 一种传染病跨时空目标追踪预警方法、系统、终端及介质

Also Published As

Publication number Publication date
GB201321129D0 (en) 2014-01-15
GB2520751A (en) 2015-06-03

Similar Documents

Publication Publication Date Title
Renaudin et al. Evaluating indoor positioning systems in a shopping mall: The lessons learned from the IPIN 2018 competition
US10126134B2 (en) Method and system for estimating uncertainty for offline map information aided enhanced portable navigation
EP3019827B1 (fr) Découverte d'emplacement en intérieur à l'aide d'anomalies de champ magnétique
Chung et al. Indoor location sensing using geo-magnetism
WO2015079260A1 (fr) Appareil de recherche d'emplacement et procédés associés
US9116000B2 (en) Map-assisted sensor-based positioning of mobile devices
JP6783751B2 (ja) 地図情報支援の質を高められたポータブルナビゲーションを使用する方法および装置
Shin et al. Unsupervised construction of an indoor floor plan using a smartphone
US9113310B2 (en) Systems and methods for simultaneously and automatically creating databases of wifi signal information
US8977494B2 (en) Method and apparatus for identification of points of interest within a predefined area
JP5838758B2 (ja) キャリブレーション方法、情報処理装置及びキャリブレーションプログラム
Potortì et al. Off-line evaluation of indoor positioning systems in different scenarios: The experiences from IPIN 2020 competition
US9462423B1 (en) Qualitative and quantitative sensor fusion for indoor navigation
Liu et al. Indoor localization using smartphone inertial sensors
Liu et al. Indoor localization on mobile phone platforms using embedded inertial sensors
US20150063135A1 (en) Client, server, and wireless signal map creating system using the same
KR101523147B1 (ko) 실내 측위 장치 및 방법
Alakhras et al. Location fixing and fingerprint matching fingerprint map construction for indoor localization
Kessel et al. Automated WLAN calibration with a backtracking particle filter
Bao et al. An indoor navigation and localization system
WO2023067363A1 (fr) Appareil de recherche d'emplacement et procédés associés
Ho-Sy et al. A hybrid algorithm based on wifi for robust and effective indoor positioning
Khan Design and development of indoor positioning system for portable devices
Hafner et al. Indoor positioning based on bayes filtering using map information
Leppäkoski Novel Methods for Personal Indoor Positioning

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 14806709

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 14806709

Country of ref document: EP

Kind code of ref document: A1