Map-assisted sensor-based positioning of mobile devices

• US 9,116,000 B2
• Filed: 10/22/2012
• Issued: 08/25/2015
• Est. Priority Date: 10/22/2012
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Pakzad, Payam, Poduri, Sameera
• Primary Examiner(s): Cheung, Mary
• Assistant Examiner(s): Sweeney, Brian P
• Application Number:
  US13/657,428
• Publication Number:
  US 20140114561A1
• Time in Patent Office: 1,037 Days
• Field of Search: 701/410
• US Class Current: 1/1
• CPC Class Codes: G01C 21/206 specially adapted for indoor navigation
• CPC Class Codes: G01C 22/006 Pedometers
• CPC Class Codes: H04W 4/029 Location-based management or tracking services

Abstract

Various methods, apparatuses and/or articles of manufacture are provided which may be implemented to estimate a trajectory of a mobile device within an indoor environment. In some embodiments, the trajectory may be estimated without the use of any signal-based positioning information. For example, a mobile device may estimate such a trajectory based, at least in part, on one or more sensor measurements obtained at the mobile device, and further affect the estimated trajectory based, at least in part, on one or more objects identified in an electronic map of the indoor environment.

Claims

1. A method comprising, with at least one computing platform:

estimating a trajectory of a mobile device within an indoor environment based, at least in part, on one or more sensor measurements obtained at said mobile device;

obtaining at least one of one or more locations of one or more waypoints based, at least in part, on at least one input received from a user interface of said mobile device, said locations of said one or more waypoints being defined based, at least in part, on one or more objects identified in an electronic map of said indoor environment; and

affecting said estimated trajectory based, at least in part, on said one or more locations of said one or more waypoints.

2. The method as recited in claim 1, wherein said estimating said trajectory further comprises:

detecting an occurrence of at least one action associated with a motion mode based, at least in part, on at least one of said one or more sensor measurements obtained from at least one accelerometer at said mobile device; and

based, at least in part, on said occurrence of said at least one action, estimating at least one of;

a distance traveled by said mobile device during a period of time;

or a velocity of said mobile device during said period of time.

3. The method as recited in claim 2, wherein said motion mode comprises a bipedal movement mode, said at least one action comprises at least one stepping action, and further comprising:

estimating at least one of;

said distance traveled;

or said velocity, based, at least in part, on at least one of;

an estimated stride length;

or an estimated stride frequency, of said at least one stepping action.

4. The method as recited in claim 3, wherein estimating at least one of:

said distance traveled;

or said velocity further comprises;

integrating a step count.

5. The method as recited in claim 1, wherein said estimating said trajectory further comprises:

determining whether a change in an orientation of said mobile device has or has not occurred based, at least in part, on at least one of said one or more sensor measurements obtained from at least one of;

a gyroscope at said mobile device;

or a magnetometer at said mobile device; and

estimating a current heading of said mobile device based, at least in part, on said determination of whether said change in said orientation has or has not occurred.

6. The method as recited in claim 5, wherein said estimating said current heading of said mobile device further comprises:

estimating said current heading of said mobile device based, at least in part, on at least one previously estimated heading.

7. The method as recited in claim 1, and further comprising, with said at least one computing platform:

determining at least one of said one or more locations of said one or more waypoints based, at least in part, on said electronic map.

8. The method as recited in claim 1, wherein said affecting said estimated trajectory further comprises:

correcting a drift in said estimated trajectory, at least in part, using a particle filter.

9. The method as recited in claim 8, wherein said particle filter maintains a particle state based, at least in part, on at least one of:

an estimated position of said mobile device on said electronic map;

an estimated heading of said mobile device;

an estimated distance traveled by said mobile device during a period of time;

or an estimated velocity of said mobile device during said period of time.

10. The method as recited in claim 9, wherein said estimated heading is associated with a heading error that is based, at least in part, on an estimated turn characteristic.

11. The method as recited in claim 8, wherein said particle filter maintains a particle state based, at least in part, on deleting propagated particles that stray from said estimated trajectory by more than a threshold measurement.

12. The method as recited in claim 8, wherein said particle filter propagates particles based, at least in part, on at least one of said one or more sensor measurements obtained at said mobile device.

13. The method as recited in claim 1, wherein said affecting said estimated trajectory further comprises:

correcting a drift in said estimated trajectory, at least in part, using a discrete turn assignment that is based, at least in part, on a cost function.

14. The method as recited in claim 13, wherein said cost function takes into account at least one of:

a cost corresponding to a difference in an orientation of said mobile device compared to said estimated trajectory during a period of time;

or a cost corresponding to a distance between consecutive waypoints on said estimated trajectory to an estimated distance traveled by said mobile device during said period of time.

15. The method as recited in claim 13, wherein said cost function is computed using a dynamic function to minimize a matching cost.

16. The method as recited in claim 1, wherein said at least one computing platform is provided in said mobile device.

17. The method as recited in claim 16, and further comprising, with said at least one computing platform:

estimating a location of said mobile device based, at least in part, on said estimated trajectory and said electronic map.

18. The method as recited in claim 17, and further comprising, with said at least one computing platform:

initiating presentation of said estimated location of said mobile device via at least one user output device of said mobile device.

19. The method as recited in claim 1, wherein said estimating comprises estimating said trajectory from a plurality of potential trajectories in said indoor environment based, at least in part, on said electronic map of said indoor environment.

20. The method as recited in claim 1, wherein the estimating comprises estimating the trajectory of the mobile device without the use of any signal based positioning information.

21. The method of claim 1, wherein obtaining the at least one of said one or more locations of waypoints further comprises accepting user inputs with regarding to a display of information within a displayed rendering of said electronic map.

22. The method of claim 1, wherein the at least one input is provided in response to presentation of a list of people, points of interest or a visual representation of all or a portion of a floor plan.

23. The method of claim 1, and further comprising accepting said at least one input by way of a touchscreen, keypad, microphone or camera.

24. An apparatus comprising:

means for estimating a trajectory of a mobile device within an indoor environment based, at least in part, on one or more sensor measurements obtained at said mobile device;

means for obtaining at least one of one or more locations of one or more waypoints based, at least in part, on at least one input received from a user interface of said mobile device, said locations of said one or more waypoints being defined based, at least in part, on one or more objects identified in an electronic map of said indoor environment; and

means for affecting said estimated trajectory based, at least in part, on said one or more locations of said one or more waypoints.

25. A computing platform comprising:

memory; and

one or more processing units to;

estimate a trajectory of a mobile device within an indoor environment based, at least in part, on one or more sensor measurements obtained at said mobile device;

obtain at least one of one or more locations of one or more waypoints based, at least in part, on at least one input received from a user interface of said mobile device, said locations of said one or more waypoints being defined based, at least in part, on one or more objects identified in an electronic map of said indoor environment accessible via said memory; and

affect said estimated trajectory based, at least in part, on said one or more locations of said one or more waypoints.

26. The computing platform as recited in claim 25, said one or more processing units to further:

detect an occurrence of at least one action associated with a motion mode based, at least in part, on at least one of said one or more sensor measurements obtained from at least one accelerometer at said mobile device; and

based, at least in part, on said occurrence of said at least one action, estimate at least one of;

a distance traveled by said mobile device during a period of time;

or a velocity of said mobile device during said period of time.

27. The computing platform as recited in claim 26, wherein said motion mode comprises a bipedal movement mode, said at least one action comprises at least one stepping action, and said one or more processing units to further:

estimate at least one of;

said distance traveled;

or said velocity, based, at least in part, on at least one of;

an estimated stride length;

or an estimated stride frequency, of said at least one stepping action.

28. The computing platform as recited in claim 27, said one or more processing units to further estimate at least one of:

said distance traveled;

or said velocity by further;

integrating a step count.

29. The computing platform as recited in claim 25, said one or more processing units to further:

determine whether a change in an orientation of said mobile device has or has not occurred based, at least in part, on at least one of said one or more sensor measurements obtained from at least one of;

a gyroscope at said mobile device;

or a magnetometer at said mobile device; and

estimate a current heading of said mobile device based, at least in part, on said determination of whether said change in said orientation has or has not occurred.

30. The computing platform as recited in claim 29, said one or more processing units to further:

estimate said current heading of said mobile device based, at least in part, on at least one previously estimated heading.

31. The computing platform as recited in claim 25, said one or more processing units to further affect said estimated trajectory, based at least in part, by correcting a drift in said estimated trajectory, at least in part, using a particle filter.

32. The computing platform as recited in claim 31, wherein said particle filter maintains a particle state based, at least in part, on at least one of:

an estimated position of said mobile device on said electronic map;

an estimated heading of said mobile device;

an estimated distance traveled by said mobile device during a period of time;

or an estimated velocity of said mobile device during said period of time.

33. The computing platform as recited in claim 32, wherein said estimated heading is associated with a heading error that is based, at least in part, on an estimated turn characteristic.

34. The computing platform as recited in claim 31, wherein said particle filter maintains a particle state based, at least in part, on deleting propagated particles that stray from said estimated trajectory by more than a threshold measurement.

35. The computing platform as recited in claim 31, wherein said particle filter propagates particles based, at least in part, on at least one of said one or more sensor measurements obtained at said mobile device.

36. The computing platform as recited in claim 25 said one or more processing units to affect said estimated trajectory, at least in part, by correcting a drift in said estimated trajectory, at least in part, using a discrete turn assignment that is based, at least in part, on a cost function.

37. The computing platform as recited in claim 36, wherein said cost function takes into account at least one of:

a cost corresponding to a difference in an orientation of said mobile device compared to said estimated trajectory during said period of time;

or a cost corresponding to a distance between consecutive waypoints on said estimated trajectory to said estimated distance traveled by said mobile device during said period of time.

38. The computing platform as recited in claim 36, wherein said cost function is computed using a dynamic function to minimize a matching cost.

39. The computing platform as recited in claim 25, wherein said computing platform is provided in said mobile device.

40. The computing platform as recited in claim 39, said one or more processing units to further:

estimate a location of said mobile device based, at least in part, on said estimated trajectory and said electronic map.

41. The computing platform as recited in claim 40, wherein said mobile device further comprises at least one user output device, and said one or more processing units to further initiate presentation of said estimated location of said mobile device via said at least one user output device.

42. A non-transitory computer readable medium having stored thereon computer implementable instructions executable by one or more processing units in a computing platform to:

estimate a trajectory of a mobile device within an indoor environment based, at least in part, on one or more sensor measurements obtained at said mobile device;

obtain at least one of one or more locations of one or more waypoints based, at least in part, on at least one input received from a user interface of said mobile device, said locations of said one or more waypoints being defined based, at least in part, on one or more objects identified in an electronic map of said indoor environment accessible via a memory; and

affect said estimated trajectory based, at least in part, on said one or more locations of said one or more waypoints.