Systems and methods for non-obstacle area detection

• US 10,395,377 B2
• Filed: 01/16/2018
• Issued: 08/27/2019
• Est. Priority Date: 09/18/2015
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Dane, Gokce, Bhaskaran, Vasudev
• Primary Examiner(s): Allison, Andrae S
• Application Number:
  US15/872,672
• Publication Number:
  US 20180158193A1
• Time in Patent Office: 588 Days
• Field of Search: None
• CPC Class Codes: G06F 18/254 of classification results, e.g. of results related to same input data
• CPC Class Codes: G06T 2207/10028 Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 2207/30256 Lane; Road marking
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06T 7/50 Depth or shape recovery
• CPC Class Codes: G06T 7/507 from shading G06T7/586 takes precedence
• CPC Class Codes: G06V 20/56 exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 20/588 Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road

Abstract

A method performed by an electronic device is described. The method includes generating a depth map of a scene external to a vehicle. The method also includes performing first processing in a first direction of a depth map to determine a first non-obstacle estimation of the scene. The method also includes performing second processing in a second direction of the depth map to determine a second non-obstacle estimation of the scene. The method further includes combining the first non-obstacle estimation and the second non-obstacle estimation to determine a non-obstacle map of the scene. The combining includes combining comprises selectively using a first reliability map of the first processing and/or a second reliability map of the second processing The method additionally includes navigating the vehicle using the non-obstacle map.

Claims

1. A method performed by an electronic device, comprising:

performing a first processing of a depth map in a first direction;

generating a first reliability map based on the first processing;

performing a second processing of at least a portion of the depth map in a second direction that is different from the first direction;

generating a second reliability map based on the second processing; and

identifying at least one non-obstacle area based on the depth map, the first reliability map, and the second reliability map.

2. The method of claim 1, wherein performing the second processing of the at least a portion of the depth map comprises identifying unreliable regions of the depth map based on the first reliability map;

and wherein the at least a portion of the depth map corresponds to the unreliable regions that were identified.

3. The method of claim 1, wherein the first direction is horizontal and the second direction is vertical.

4. The method of claim 1, wherein performing the first processing of the depth map comprises dividing the depth map into linear segments in the first direction.

5. The method of claim 4, wherein the linear segments in the first direction are rows of pixels of the depth map.

6. The method of claim 4, wherein the linear segments in the first direction are columns of pixels of the depth map.

7. The method of claim 1, wherein the depth map comprises a depth map of a scene external to a vehicle.

8. The method of claim 7, further comprising navigating the vehicle based on the at least one non-obstacle area.

9. The method of claim 8, wherein navigating the vehicle comprises providing at least the non-obstacle area as input to an advanced driver assistance system (ADAS) to regulate at least one of speed or steering of the vehicle.

10. The method of claim 8, wherein navigating the vehicle comprises using the non-obstacle area to identify a region of interest that is used by at least one of an object detection algorithm or a lane detection algorithm.

11. An electronic device, comprising:

at least one processor;

a memory in communication with the at least one processor; and

instructions stored in the memory, the instructions executable by the at least one processor to;

perform a first processing of a depth map in a first direction;

generate a first reliability map based on the first processing;

perform a second processing of at least a portion of the depth map in a second direction that is different from the first direction;

generate a second reliability map based on the second processing; and

identify at least one non-obstacle area based on the depth map, the first reliability map, and the second reliability map.

12. The electronic device of claim 11, wherein the instructions executable by the at least one processor to perform the second processing of the at least a portion of the depth map comprise instructions executable by the at least one processor to identify unreliable regions of the depth map based on the first reliability map;

and wherein the at least a portion of the depth map corresponds to the unreliable regions that were identified.

13. The electronic device of claim 11, wherein the first direction is horizontal and the second direction is vertical.

14. The electronic device of claim 11, wherein the instructions executable by the at least one processor to perform the first processing of the depth map comprise instructions executable by the at least one processor to divide the depth map into linear segments in the first direction.

15. The electronic device of claim 14, wherein the linear segments in the first direction are rows of pixels of the depth map.

16. The electronic device of claim 14, wherein the linear segments in the first direction are columns of pixels of the depth map.

17. The electronic device of claim 11, wherein the depth map comprises a depth map of a scene external to a vehicle.

18. The electronic device of claim 17, wherein the instructions executable by the at least one processor are further executable to navigate the vehicle based on the at least one non-obstacle area.

19. The electronic device of claim 18, wherein the instructions executable by the at least one processor to navigate the vehicle comprise instructions executable by the at least one processor to provide at least the non-obstacle area as input to an advanced driver assistance system (ADAS) to regulate at least one of speed or steering of the vehicle.

20. The electronic device of claim 18, wherein the instructions executable by the at least one processor to navigate the vehicle comprise instructions executable by the at least one processor to use the non-obstacle area to identify a region of interest that is used by at least one of an object detection algorithm or a lane detection algorithm.

21. A computer-program product, comprising a non-transitory tangible computer-readable medium having instructions thereon, the instructions comprising:

code for causing an electronic device to perform a first processing of a depth map in a first direction;

code for causing an electronic device to generate a first reliability map based on the first processing;

code for causing an electronic device to perform a second processing of at least a portion of the depth map in a second direction that is different from the first direction;

code for causing an electronic device to generate a second reliability map based on the second processing; and

code for causing an electronic device to identify at least one non-obstacle area based on the depth map, the first reliability map, and the second reliability map.

22. The computer-program product of claim 21, wherein the code for causing the electronic device to perform the second processing of the at least a portion of the depth map comprises code for causing the electronic device to identify unreliable regions of the depth map based on the first reliability map;

and wherein the at least a portion of the depth map corresponds to the unreliable regions that were identified.

23. The computer-program product of claim 21, wherein the first direction is horizontal and the second direction is vertical.

24. The computer-program product of claim 21, wherein the code for causing the electronic device to perform the first processing of the depth map comprises code for causing the electronic device to divide the depth map into linear segments in the first direction.

25. The computer-program product of claim 24, wherein the linear segments in the first direction are rows of pixels of the depth map.

26. The computer-program product of claim 24, wherein the linear segments in the first direction are columns of pixels of the depth map.

27. The computer-program product of claim 21, wherein the depth map comprises a depth map of a scene external to a vehicle.

28. The computer-program product of claim 27, wherein the instructions further comprise further code for causing an electronic device to navigate the vehicle based on the at least one non-obstacle area.

29. The computer-program product of claim 28, wherein the code for causing an electronic device to navigate the vehicle comprises code for causing an electronic device to provide at least the non-obstacle area as input to an advanced driver assistance system (ADAS) to regulate at least one of speed or steering of the vehicle.

30. The computer-program product of claim 28, wherein the code for causing an electronic device to navigate the vehicle comprises code for causing an electronic device to use the non-obstacle area to identify a region of interest that is used by at least one of an object detection algorithm or a lane detection algorithm.