Techniques for processing perceived routability constraints that may or may not affect movement of a mobile device within an indoor environment

• US 9,014,716 B2
• Filed: 08/06/2012
• Issued: 04/21/2015
• Est. Priority Date: 04/10/2012
• Status: Expired due to Fees

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Pakzad, Payam, Khorashadi, Behrooz, Chao, Hui
• Primary Examiner(s): VO, NGUYEN THANH
• Application Number:
  US13/568,026
• Publication Number:
  US 20130267178A1
• Time in Patent Office: 988 Days
• Field of Search: 455/456.1, 455/566, 342/450, 342/451, 342/452, 701/431, 701/434, 701/400, 701/409, 701/433
• US Class Current: 455/456.1
• CPC Class Codes: G01C 21/206 specially adapted for indoor navigation
• CPC Class Codes: H04W 24/10 Scheduling measurement reports ; Arrangements for measurement reports
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/33 for indoor environments, e.g. buildings

Abstract

Techniques are provided which may be implemented using various methods, apparatuses and/or articles of manufacture to provide or otherwise support mobile device positioning. The mobile device positioning may be based, at least in part, on one or more perceived routability constraints that may or may not affect actual movement of a mobile device within an indoor environment.

Claims

1. A method for supporting mobile device positioning on a mobile device, the method comprising:

receiving an electronic map for an indoor environment, wherein at least a portion of said electronic map comprises displayable image data;

selecting at least one perceived routability constraint based, at least in part, on said displayable image data;

setting a measure of confidence for said at least one perceived routability constraint based, at least in part, on said displayable image data, wherein the measure of confidence corresponds to an estimated likelihood that said at least one perceived routability constraint represents an actual physical obstacle to navigation; and

estimating a trajectory of said mobile device within said indoor environment based, at least in part, on said measure of confidence.

2. The method as recited in claim 1, wherein at least a portion of said displayable image data comprises vector-based image data.

3. The method as recited in claim 1, wherein at least a portion of said displayable image data comprises raster-based image data.

4. The method as recited in claim 1, wherein at least a portion of said displayable image data represents a plurality of pixels corresponding to at least a portion of a line or an object transition, and wherein selecting said at least one perceived routability constraint further comprises:

selecting at least said plurality of pixels as said at least one perceived routability constraint based, at least in part, on one or more threshold values.

5. The method as recited in claim 4, wherein at least one of said one or more threshold values is based, at least in part, on at least one of:

a minimum pixilated line measurement;

a minimum pixilated object transition measurement;

or

a maximum pixilated noise measurement.

6. The method as recited in claim 1, wherein said at least one perceived routability constraint corresponds to at least a portion of a line or an object transition identified within said displayable image data, and wherein setting said measure of confidence for said at least one perceived routability constraint further comprises:

associating a weight value with said at least said portion of said line or said object transition identified within said displayable image data, said weight value corresponding to the estimated likelihood that said at least one perceived routability constraint represents an actual physical obstacle to navigation.

7. The method as recited in claim 6, and further comprising:

affecting a propagation of at least one particle within a particle filtering model based, at least in part, on said weight value associated with said at least said portion of said line or said object transition.

8. The method as recited in claim 1, wherein said at least one perceived routability constraint corresponds to at least one edge identified within a routability graph corresponding to at least a portion of said displayable image data, and wherein setting said measure of confidence for said at least one perceived routability constraint further comprises:

associating a weight value with said at least one edge, said weight value corresponding to the estimated likelihood that said at least one perceived routability constraint represents an actual physical obstacle to navigation, wherein the actual physical obstacle is capable of affecting said trajectory of said mobile device along said at least one edge within a corresponding region of said indoor environment.

9. The method as recited in claim 8, and further comprising:

affecting a propagation of at least one particle within a particle filtering model based, at least in part, on said weight value associated with said at least one edge.

10. The method as recited in claim 1, and further comprising:

affecting said at least said measure of confidence based, at least in part, on subsequently received positioning data for at least one of said mobile device or one or more other mobile devices as result of movement therein within said indoor environment.

11. The method as recited in claim 1, and further comprising:

affecting said at least said measure of confidence based, at least in part, on an age of said electronic map.

12. The method as recited in claim 1, and further comprising:

presenting estimated positioning information to a user of said mobile device, said estimated positioning information being based, at least in part, on said trajectory of said mobile device within said indoor environment.

13. The method as recited in claim 12, wherein said estimated positioning information is based, at least in part, on said electronic map.

14. An apparatus for use in a mobile device, the apparatus comprising:

means for receiving an electronic map for an indoor environment, wherein at least a portion of said electronic map comprises displayable image data;

means for selecting at least one perceived routability constraint based, at least in part, on said displayable image data;

means for setting a measure of confidence for said at least one perceived routability constraint based, at least in part, on said displayable image data, wherein the measure of confidence corresponds to an estimated likelihood that said at least one perceived routability constraint represents an actual physical obstacle to navigation;

means for estimating a trajectory of said mobile device within said indoor environment based, at least in part, on said measure of confidence; and

means for presenting estimated positioning information to a user of said mobile device, said estimated positioning information being based, at least in part, on said trajectory of said mobile device within said indoor environment.

15. The apparatus as recited in claim 14, wherein at least a portion of said displayable image data represents a plurality of pixels corresponding to at least a portion of a line or an object transition, and further comprising:

means for selecting at least said plurality of pixels as said at least one perceived routability constraint based, at least in part, on one or more threshold values.

16. The apparatus as recited in claim 15, wherein at least one of said one or more threshold values is based, at least in part, on at least one of:

a minimum pixilated line measurement;

a minimum pixilated object transition measurement;

or

a maximum pixilated noise measurement.

17. The apparatus as recited in claim 14, wherein said at least one perceived routability constraint corresponds to at least a portion of a line or an object transition identified within said displayable image data, and wherein the means for setting the measure of confidence for said at least one perceived routability constraint further comprises:

means for associating a weight value with said at least said portion of said line or said object transition identified within said displayable image data, said weight value corresponding to the estimated likelihood that said at least one perceived routability constraint represents an actual physical obstacle to navigation.

18. The apparatus as recited in claim 17, and further comprising:

means for affecting a propagation of at least one particle within a particle filtering model based, at least in part, on said weight value associated with said at least said portion of said line or said object transition.

19. The apparatus as recited in claim 14, wherein said at least one perceived routability constraint corresponds to at least one edge identified within a routability graph corresponding to at least a portion of said displayable image data, and wherein the means for setting said measure of confidence for said at least one perceived routability constraint further comprises:

means for associating a weight value with said at least one edge, said weight value corresponding to the estimated likelihood that said at least one perceived routability constraint represents an actual physical obstacle to navigation, wherein the actual physical obstacle is capable of affecting said trajectory of said mobile device along said at least one edge within a corresponding region of said indoor environment.

20. The apparatus as recited in claim 19, and further comprising:

means for affecting a propagation of at least one particle within a particle filtering model based, at least in part, on said weight value associated with said at least one edge.

21. The apparatus as recited in claim 14, and further comprising:

means for affecting said at least said measure of confidence based, at least in part, on subsequently received positioning data for at least one of said mobile device or one or more other mobile devices as result of movement therein within said indoor environment.

22. The apparatus as recited in claim 14, and further comprising:

means for affecting said at least said measure of confidence based, at least in part, on an age of said electronic map.

23. A mobile device comprising:

memory;

an output unit; and

one or more processing units to;

access an electronic map for an indoor environment via said memory, wherein at least a portion of said electronic map comprises displayable image data;

select at least one perceived routability constraint based, at least in part, on said displayable image data;

set a measure of confidence for said at least one perceived routability constraint based, at least in part, on said displayable image data, wherein the measure of confidence corresponds to an estimated likelihood that said at least one perceived routability constraint represents an actual physical obstacle to navigation;

estimate a trajectory of said mobile device within said indoor environment based, at least in part, on said measure of confidence; and

initiate presentation of estimated positioning information to a user of said mobile device via said output unit, said estimated positioning information being based, at least in part, on said trajectory of said mobile device within said indoor environment.

24. The mobile device as recited in claim 23, wherein at least a portion of said displayable image data represents a plurality of pixels corresponding to at least a portion of a line or an object transition, and said one or more processing units to further:

select at least said plurality of pixels as said at least one perceived routability constraint based, at least in part, on one or more threshold values.

25. The mobile device as recited in claim 24, wherein at least one of said one or more threshold values is based, at least in part, on at least one of:

a minimum pixilated line measurement;

a minimum pixilated object transition measurement;

or

a maximum pixilated noise measurement.

26. The mobile device as recited in claim 23, wherein said at least one perceived routability constraint corresponds to at least a portion of a line or an object transition identified within said displayable image data, and wherein said measure of confidence for said at least one perceived routability constraint is determined, at least in part, on an association of a weight value with said at least said portion of said line or said object transition identified within said displayable image data, said weight value corresponding to the estimated likelihood that said at least one perceived routability constraint represents an actual physical obstacle to navigation.

27. The mobile device as recited in claim 26, said one or more processing units to further:

affect a propagation of at least one particle within a particle filtering model based, at least in part, on said weight value associated with said at least said portion of said line or said object transition.

28. The mobile device as recited in claim 23, wherein said at least one perceived routability constraint corresponds to at least one edge identified within a routability graph corresponding to at least a portion of said displayable image data, and wherein said measure of confidence for said at least one perceived routability constraint is determined, at least in part, on an association of a weight value with said at least one edge, said weight value corresponding to the estimated likelihood that said at least one perceived routability constraint represents an actual physical obstacle to navigation, wherein the actual physical obstacle is capable of affecting a trajectory of said mobile device along said at least one edge within a corresponding region of said indoor environment.

29. The mobile device as recited in claim 28, said one or more processing units to further:

affect a propagation of at least one particle within a particle filtering model based, at least in part, on said weight value associated with said at least one edge.

30. The mobile device as recited in claim 23, said one or more processing units to further:

affect said at least said measure of confidence based, at least in part, on subsequently received positioning data for at least one of said mobile device or one or more other mobile devices as result of movement therein within said indoor environment.

31. The mobile device as recited in claim 23, said one or more processing units to further:

affect said at least said measure of confidence based, at least in part, on an age of said electronic map.

32. An article for use in a computing device to support mobile device positioning, the article comprising:

a non-transitory computer readable medium having computer implementable instructions stored therein that are executable by one or more processing units of a computing platform of a mobile device to;

receive an electronic map for an indoor environment, wherein at least a portion of said electronic map comprises displayable image data;

select at least one perceived routability constraint based, at least in part, on said displayable image data;

set a measure of confidence for said at least one perceived routability constraint based, at least in part, on said displayable image data, wherein the measure of confidence corresponds to an estimated likelihood that said at least one perceived routability constraint represents an actual physical obstacle to navigation;

estimate a trajectory of said mobile device within said indoor environment based, at least in part, on said measure of confidence; and

initiate presentation of estimated positioning information to a user of said mobile device, said estimated positioning information being based, at least in part, on said trajectory of said mobile device within said indoor environment.

33. The article as recited in claim 32, wherein at least a portion of said displayable image data represents a plurality of pixels corresponding to at least a portion of a line or an object transition, and said computer implementable instructions are further executable by said one or more processing units to:

select at least said plurality of pixels as said at least one perceived routability constraint based, at least in part, on one or more threshold values.

34. The article as recited in claim 33, wherein at least one of said one or more threshold values is based, at least in part, on at least one of:

a minimum pixilated line measurement;

a minimum pixilated object transition measurement;

or

a maximum pixilated noise measurement.

35. The article as recited in claim 32, wherein said at least one perceived routability constraint corresponds to at least a portion of a line or an object transition identified within said displayable image data, and wherein said measure of confidence for said at least one perceived routability constraint is determined, at least in part, on an association of a weight value with said at least said portion of said line or said object transition identified within said displayable image data, said weight value corresponding to the estimated likelihood that said at least one perceived routability constraint represents an actual physical obstacle to navigation.

36. The article as recited in claim 35, said computer implementable instructions are further executable by said one or more processing units to:

affect a propagation of at least one particle within a particle filtering model based, at least in part, on said weight value associated with said at least said portion of said line or said object transition.

37. The article as recited in claim 32, wherein said at least one perceived routability constraint corresponds to at least one edge identified within a routability graph corresponding to at least a portion of said displayable image data, and wherein said measure of confidence for said at least one perceived routability constraint is determined, at least in part, on an association of a weight value with said at least one edge, said weight value corresponding to the estimated likelihood that said at least one perceived routability constraint represents an actual physical obstacle to navigation, wherein the actual physical obstacle is capable of affecting a trajectory of said mobile device along said at least one edge within a corresponding region of said indoor environment.

38. The article as recited in claim 37, said computer implementable instructions are further executable by said one or more processing units to:

affect a propagation of at least one particle within a particle filtering model based, at least in part, on said weight value associated with said at least one edge.

39. The article as recited in claim 32, and said computer implementable instructions are further executable by said one or more processing units to:

affect said at least said measure of confidence based, at least in part, on subsequently received positioning data for at least one of said mobile device or one or more other mobile devices as result of movement therein within said indoor environment.

40. The article as recited in claim 32, said computer implementable instructions are further executable by said one or more processing units to:

affect said at least said measure of confidence based, at least in part, on an age of said electronic map.