SYSTEMS AND METHODS FOR DETECTING CRACKS IN TERRAIN SURFACES USING MOBILE LIDAR DATA

• US 20130046471A1
• Filed: 08/18/2011
• Published: 02/21/2013
• Est. Priority Date: 08/18/2011
• Status: Abandoned Application

• Current Assignee: Harris Corporation (L3Harris Technologies, Inc.)
• Original Assignee: Harris Corporation (L3Harris Technologies, Inc.)
• Inventors: Rahmes, Mark, McGonagle, Michael, Yates, J. H., Lemieux, George
• Application Number:
  US13/212,253
• Publication Number:
  US 20130046471A1
• Time in Patent Office: Days
• US Class Current: 702/5
• CPC Class Codes: G01S 17/88 Lidar systems specially adapted for specific applications
• CPC Class Codes: G01S 17/89 for mapping or imaging
• CPC Class Codes: G01S 7/4802 using analysis of echo signal for target characterisation; Target signature; Target cross-section
• CPC Class Codes: G01S 7/4808 Evaluating distance, position or velocity data

Abstract

Systems (100) and methods (300) for automatically generating a quality metric for a specified surface area of a terrain (104). The methods involve acquiring mobile LIDAR data defining a geometry of the specified surface area of the terrain. The mobile LIDAR data is acquired by LIDAR equipment (106) disposed on a vehicle (102) traveling along the terrain. The methods also involve automatically determining a quality metric defining a quality of the specified surface area of the terrain using the mobile LIDAR data.

Claims

1. A method for automatically generating a quality metric for a specified surface area of a terrain, comprising:

acquiring mobile LIDAR data defining a geometry of said specified surface area of said terrain, said mobile LIDAR data being acquired by LIDAR equipment disposed on a vehicle traveling along said terrain; and

automatically determining, by at least one electronic circuit communicatively coupled to said LIDAR equipment, a quality metric defining a quality of said specified surface area of said terrain using said mobile LIDAR data.

2. The method according to claim 1, further comprising performing, by said electronic circuit, a binarization process using said mobile LIDAR data to obtain Black-And-White (“

BAW”

) LIDAR data comprising black pixels and white pixels, said black pixels defining said cracks.

3. The method according to claim 2, wherein said binarization process comprises determining propagation directions of said cracks, aligning a steerable filter to said propagation directions, and using said steerable filter to convert said mobile LIDAR data to said BAW LIDAR data.

4. The method according to claim 2, further comprising determining, by said electronic circuit, at least one of an average width of cracks defined by said BAW LIDAR data, a total number of pores defined by said BAW LIDAR data, and a total number of spurs defined by said BAW LIDAR data.

5. The method according to claim 2, further comprising filling, by said electronic circuit, at least one pore defined by said BAW LIDAR data.

6. The method according to claim 2, further comprising removing, by said electronic circuit, at least one spur defined by said BAW LIDAR data.

7. The method according to claim 2, further comprising performing, by said electronic circuit, operations to connect cracks having endings that are spaced a certain distance apart from each other.

8. The method according to claim 2, further comprising processing, by said electronic circuit, said BAW LIDAR data to obtain first modified BAW LIDAR data defining cracks with widths of one pixel.

9. The method according to claim 8, further comprising processing, by said electronic circuit, said first modified BAW LIDAR data to reduce a pixel-wide noise thereof so as to obtain second modified BAW LIDAR data with smoothed cracks.

10. The method according to claim 9, further comprising identifying, by said electronic circuit, black pixels of said second modified BAW LIDAR data defining cracks that constitute minutiae and determining, by said electronic circuit, locations of said minutiae.

11. The method according to claim 10, further comprising determining, by said electronic circuit, at least one of a total number of said minutiae, a density of said minutiae, a total number of cracks defined by said second modified BAW LIDAR data, and an average length of said cracks defined by said second modified BAW LIDAR data.

12. The method according to claim 1, wherein said quality metric is determined by comparing a threshold value to a quality measure.

13. The method according to claim 12, wherein the quality measure comprises a total number of cracks defined by data, a total number of pores defined by said data, a total number of spurs defined by said data, a total number of crack connections made, a total number of spurs removed, an average length of said cracks, an average width of said cracks, a total number of minutiae, a density of said minutiae, a depth of said cracks, or a ridge flow disturbance.

14. The method according to claim 1, further comprising determining a maintenance plan for said terrain based on said quality metric and a plurality of other quality metrics.

15. The method according to claim 1, further comprising superimposing said mobile LIDAR data on a map, virtual model or image.

16. A system, comprising:

LIDAR equipment configured to acquire mobile LIDAR data defining a geometry of a specified surface area of a terrain; and

at least one electronic circuit communicatively coupled to said LIDAR equipment and configured to automatically determine a quality metric defining a quality of said specified surface area of said terrain using said mobile LIDAR data.

17. The system according to claim 16, wherein said electronic circuit is further configured to perform a binarization process using said mobile LIDAR data to obtain Black-And-White (“

BAW”

) LIDAR data comprising black pixels and white pixels, said black pixels defining said cracks.

18. The system according to claim 17, wherein said binarization process comprises determining propagation directions of said cracks, aligning a steerable filter to said propagation directions, and using said steerable filter to convert said mobile LIDAR data to said BAW LIDAR data.

19. The system according to claim 17, wherein said electronic circuit is further configured to determine at least one of an average width of cracks defined by said BAW LIDAR data, a total number of pores defined by said BAW LIDAR data and a total number of spurs defined by said BAW LIDAR data.

20. The system according to claim 17, wherein said electronic circuit is further configured to fill at least one pore defined by said BAW LIDAR data.

21. The system according to claim 17, wherein is said electronic circuit is further configured to remove at least one spur defined by said BAW LIDAR data.

22. The system according to claim 17, wherein said electronic circuit is further configured to perform operations to connect cracks having endings that are spaced a certain distance apart from each other.

23. The system according to claim 17, wherein said electronic circuit is further configured to process said BAW LIDAR data to obtain first modified BAW LIDAR data defining cracks with widths of one pixel.

24. The system according to claim 23, wherein said electronic circuit is further configured to process said first modified BAW LIDAR data to reduce a pixel-wide noise thereof so as to obtain second modified BAW LIDAR data with smoothed cracks.

25. The system according to claim 24, wherein said electronic circuit is further configured to identify black pixels of said second modified BAW LIDAR data defining cracks that constitute minutiae and to determine locations of said minutiae.

26. The system according to claim 25, wherein said electronic circuit is further configured to determine at least one of a total number of said minutiae, a density of said minutiae, a total number of cracks defined by said second modified BAW LIDAR data, and an average length of said cracks defined by said second modified BAW LIDAR data.

27. The system according to claim 16, wherein said quality metric is determined by comparing a threshold value to a quality measure.

28. The system according to claim 27, wherein said quality measure comprises a total number of cracks defined by data, a total number of pores defined by said data, a total number of spurs defined by said data, a total number of crack connections made, a total number of spurs removed, an average length of said cracks, an average width of said cracks, a total number of minutiae, a density of said minutiae, a depth of said cracks, or a ridge flow disturbance.

29. The system according to claim 16, wherein said electronic circuit is further configured to determine a maintenance plan for said terrain based on said quality metric and a plurality of other quality metrics.

30. The system according to claim 16, wherein said electronic circuit is further configured to superimpose said mobile LIDAR data on a map, virtual model or image.