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

• US 9,107,044 B2
• Filed: 02/13/2015
• Issued: 08/11/2015
• 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:
  US14/622,799
• Publication Number:
  US 20150163641A1
• Time in Patent Office: 179 Days
• Field of Search: 455/456.1, 455/566, 342/450, 342/451, 342/452, 701/400, 701/409, 701/431, 701/433, 701/434
• US Class Current: 1/1
• 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 computing device external to said 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

transmitting at least said measure of confidence to said mobile device for use by said mobile device in estimating a trajectory of said mobile device within said indoor environment.

2. 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 further comprising:

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.

3. The method as recited in claim 2, wherein at least one of said one or more threshold values is based, at least in part, on:

a minimum pixilated line measurement;

a minimum pixilated object transition measurement;

or

a maximum pixilated noise measurement, or

any combination thereof.

4. 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 said setting said measure of confidence 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.

5. The method as recited in claim 4, 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.

6. 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 said setting said measure of confidence 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 a trajectory of said mobile device along said at least one edge within a corresponding region of said indoor environment.

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 one edge.

8. 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.

9. 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.

10. A computing device for use in supporting mobile device positioning, the computing device comprising:

memory;

a communication interface; 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; and

initiate transmission, via said communication interface, of at least said measure of confidence to said mobile device for use in estimating a trajectory of said mobile device within said indoor environment.

11. The computing device as recited in claim 10, 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.

12. The computing device as recited in claim 10, 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.

13. The computing device as recited in claim 12, 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.

14. The computing device as recited in claim 10, 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.

15. The computing device as recited in claim 14, 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.

16. The computing device as recited in claim 10, 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.

17. The computing device as recited in claim 10, 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.

18. An article for use in a computing platform external to a mobile 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 the computing platform 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; and

initiate transmission of at least said measure of confidence to said mobile device for use in estimating a trajectory of said mobile device within said indoor environment.

19. The article as recited in claim 18, 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.

20. The article as recited in claim 18, 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.