Determining position of a device in three-dimensional space and corresponding calibration techniques

• US 10,694,325 B2
• Filed: 12/15/2017
• Issued: 06/23/2020
• Est. Priority Date: 12/31/2016
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google LLC (Alphabet Inc.)
• Inventors: Robertson, Patrick, Williams, Brian, Puyol, Maria Jesus Garcia, Le Grand, Etienne, Khider, Mohammed, Bruno, Luigi
• Primary Examiner(s): Behnamian, Shahriar
• Application Number:
  US15/844,384
• Publication Number:
  US 20180255431A1
• Time in Patent Office: 921 Days
• Field of Search: 4554561-4566, 455457
• CPC Class Codes: G01C 21/005 with correlation of navigation data from several sources, e.g. map or contour matching G01C21/30 takes precedence
• CPC Class Codes: G01C 21/08 involving use of the magnetic field of the earth
• CPC Class Codes: G01C 21/206 specially adapted for indoor navigation
• CPC Class Codes: G01C 5/06 by using barometric means
• CPC Class Codes: G01S 11/06 using intensity measurements
• CPC Class Codes: G01S 19/45 by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
• CPC Class Codes: G01S 19/46 the supplementary measurement being of a radio-wave signal type
• CPC Class Codes: G01S 5/01 Determining conditions which influence positioning, e.g. radio environment, state of motion or energy consumption
• CPC Class Codes: G01S 5/011 Identifying the radio environment
• CPC Class Codes: G01S 5/013 Identifying areas in a building
• CPC Class Codes: G01S 5/016 between areas within a building
• CPC Class Codes: G01S 5/0252 Radio frequency fingerprinting
• CPC Class Codes: G01S 5/02522 The radio-map containing measured values of non-radio values
• CPC Class Codes: G01S 5/02585 at least one of the measurements being a non-radio measurement
• CPC Class Codes: H04B 17/27 for locating or positioning the transmitter
• CPC Class Codes: H04B 17/318 Received signal strength
• CPC Class Codes: H04W 4/023 using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
• CPC Class Codes: H04W 84/12 WLAN [Wireless Local Area Networks]

Abstract

Positioning mobile devices in a three-dimensional space includes receiving multiple traces, each trace corresponding to a sequence of atmospheric pressure readings from a respective mobile device, receiving indications of signals received by the mobile devices from signal sources concurrently with the atmospheric pressure readings, generate similarity metrics for the multiple traces using the indications of other signals received by the mobile devices, the similarity metrics being indicative of associations between the signal sources and the atmospheric pressure readings, and determine estimated changes in elevation over time for the multiple traces using the generated similarity metrics.

Claims

1. A method of positioning mobile devices in a three-dimensional space, the method comprising:

receiving, by one or more processors, multiple traces, each trace corresponding to a sequence of atmospheric pressure readings from a respective mobile device;

receiving, by the one or more processors, indications of signals received by the mobile devices from signal sources concurrently with the atmospheric pressure readings;

generating, by the one or more processors, similarity metrics for the multiple traces using the indications of other signals received by the mobile devices, the similarity metrics being indicative of associations between the signal sources and the atmospheric pressure readings, each of the similarity metrics corresponding to a segment with multiple atmospheric pressure readings of a first one of the traces and a segment with multiple atmospheric pressure readings of a second one of the traces to provide a quantitative indication of how similar the first and the second traces are; and

determining, by the one or more processors, estimated changes in elevation over time for the multiple traces using the generated similarity metrics.

2. The method of claim 1, further comprising splitting at least some of the multiple traces into respective multiple segments, each segment corresponding to atmospheric pressure readings that differ by less than a certain fixed amount, wherein average atmospheric pressure readings at different segments differ from each other by more than the fixed amount.

3. The method of claim 2, wherein splitting the at least some of the multiple traces into respective multiple segments includes associating a portion of a trace with a single segment only when the portion of the trace is within a time window of a certain fixed duration.

4. The method of claim 2, wherein splitting the at least some of the multiple traces into respective multiple segments includes estimating a number of floors in a building.

5. The method of claim 2, wherein splitting the at least some of the multiple traces into respective multiple segments includes estimating vertical separation between floors.

6. The method of claim 1, wherein generating the similarity metrics includes:

correlating receive signal strength measurements between several of the traces along a time window of a fixed duration to generate a correlation metric, and

comparing the correlation metric to a similarity threshold value to determine whether the correlated traces traverse a same geographic area.

7. The method of claim 1, wherein generating the similarity metrics includes:

determining that receive signal strength measurements from several of the traces exceed a signal strength threshold, and that the signal strength measurements correspond to a common wireless signal source.

8. The method of claim 1, wherein determining the estimated changes in elevation includes:

receiving weather data for a time interval to which the traces correspond; and

modeling an impact of the weather data on the estimated changes in elevation over time as a smooth function of time.

9. The method of claim 1, further comprising:

detecting, in at least some of the multiple trace, horizontal and/or vertical displacement characteristic of ascending stairs; and

estimating a location of a stairwell using the detected displacement.

10. The method of claim 1, further comprising:

detecting, in at least some of the multiple trace, horizontal and/or vertical displacement characteristic of riding on an escalator; and

estimating a location of an escalator using the detected displacement.

11. The method of claim 1, further comprising:

detecting, in at least some of the multiple trace, horizontal and/or vertical displacement characteristic of riding in an elevator; and

estimating a location of an elevator using the detected displacement.

12. The method of claim 1, wherein receiving the indications of signals received by the mobile devices from signal sources concurrently with the atmospheric pressure readings includes receiving indications of one or more of:

signals from wireless local-area network (WLAN) access points,

signals from wireless personal area networks (WPAN) devices,

near-field communication (NFC) signals, or

magnetic field readings.

13. A computing system for positioning mobile devices in a three-dimensional space, the system comprising:

one or more processors; and

a non-transitory computer-readable medium storing thereon instructions that, when executed by the one or more processors, cause the system to;

receive multiple traces, each trace corresponding to a sequence of atmospheric pressure readings from a respective mobile device,

receive indications of signals received by the mobile devices from signal sources concurrently with the atmospheric pressure readings,

generate similarity metrics for the multiple traces using the indications of other signals received by the mobile devices, the similarity metrics being indicative of associations between the signal sources and the atmospheric pressure readings, each of the similarity metrics corresponding to a segment with multiple atmospheric pressure readings of a first one of the traces and a segment with multiple atmospheric pressure readings of a second one of the traces to provide a quantitative indication of how similar the first and the second traces are; and

determine estimated changes in elevation over time for the multiple traces using the generated similarity metrics.

14. The computing system of claim 13, wherein the instructions further cause the system to split at least some of the multiple traces into respective multiple segments, each segment corresponding to atmospheric pressure readings that differ by less than a certain fixed amount, wherein average atmospheric pressure readings at different segments differ from each other by more than the fixed amount.

15. The computing system of claim 14, wherein to split the at least some of the multiple traces into respective multiple segments, the instructions further cause the system to associate a portion of a trace with a single segment only when the portion of the trace is within a time window of a certain fixed duration.

16. The computing system of claim 14, wherein to split the at least some of the multiple traces into respective multiple segments, the instructions further cause the system to estimate a number of floors in a building.

17. The computing system of claim 14, wherein to split the at least some of the multiple traces into respective multiple segments, the instructions further cause the system to estimate vertical separation between floors.

18. The computing system of claim 13, wherein to generate the similarity metrics, the instructions further cause the system to:

correlate receive signal strength measurements between several of the traces along a time window of a fixed duration to generate a correlation metric, and

compare the correlation metric to a similarity threshold value to determine whether the correlated traces traverse a same geographic area.

19. The computing system of claim 13, wherein to generate the similarity metrics, the instructions further cause the system to determine that receive signal strength measurements from several of the traces exceed a signal strength threshold, and that the signal strength measurements correspond to a common wireless signal source.

20. The computing system of claim 13, wherein to determine the estimated changes in elevation, the instructions cause the system to:

receive weather data for a time interval to which the traces correspond, and

model an impact of the weather data on the estimated changes in elevation over time as a smooth function of time.