LIDAR-ASSISTED MULTI-IMAGE MATCHING FOR 3-D MODEL AND SENSOR POSE REFINEMENT

• US 20100204964A1
• Filed: 09/21/2009
• Published: 08/12/2010
• Est. Priority Date: 02/09/2009
• Status: Abandoned Application

• Current Assignee: Utah State University
• Original Assignee: Utah State University
• Inventors: Israelsen, Paul, Pack, Robert Taylor
• Application Number:
  US12/563,894
• Publication Number:
  US 20100204964A1
• Time in Patent Office: Days
• US Class Current: 703/1
• CPC Class Codes: G01S 17/89 for mapping or imaging
• CPC Class Codes: G06T 2207/10004 Still image; Photographic image
• CPC Class Codes: G06T 2207/10028 Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 2207/30181 Earth observation
• CPC Class Codes: G06T 7/521 from laser ranging, e.g. using interferometry; from the projection of structured light

Abstract

A 3-D model of a subject matter may be constructed from a plurality of lidar shots and overlapping EO images of the subject matter. Each of the lidar shots may be mapped to image patches within two or more of the EO images using navigation data associated with the lidar shots and EO images. Each of the back-projected lidar points may be set as a centroid of an image patch (collection of pixels) within an EO image. With the aid of the lidar centroids, the image patches in overlapping EO images may be correlated and an image-based pixel-to-pixel coordinate association therebetween may be calculated. Using this refined pixel-to-pixel coordinate association, a 3-D model of the subject matter may be constructed and refined using photogrammetry techniques. Videogrammetry techniques, such as optical flow techniques, may be applied if a sufficient amount of EO imagery data is available.

Claims

1. A computer-readable storage medium comprising executable instructions to cause a computing device to perform a method for constructing a model of a subject matter, the method comprising:

accessing modeling data comprising a plurality of overlapping EO images of the subject matter;

a plurality of lidar shots of the subject matter, and correlation data associated with each of the EO images and lidar shots;

projecting each of the lidar shots onto two or more of the EO images using the correlation data;

calculating pixel-to-pixel coordinate associations between the overlapping EO images, wherein the pixel-to-pixel coordinate associations are calculated using the lidar shot projections; and

constructing a 3-D model of the subject matter using the pixel-to-pixel coordinate associations.

2. The computer-readable storage medium of claim 1, wherein projecting a lidar shot onto two or more EO images comprises back projecting a footprint of the lidar shot onto respective image patches within the two or more EO images.

3. The computer-readable storage medium of claim 2, wherein the correlation data of the lidar shot comprises navigation data indicative of a location of a lidar when the lidar shot was acquired, and wherein the lidar shot is back projected onto the two or more EO images using the navigation data.

4. The computer-readable storage medium of claim 3, wherein the correlation data of the lidar shot further comprises orientation data indicative of an orientation of the lidar at the time the lidar shot was acquired, and wherein the lidar shot is back projected onto the two or more EO images using the navigation data and the orientation data.

5. The computer-readable storage medium of claim 2, the method further comprising, setting the lidar shot projections within the two or more EO images as centroids of respective bounding primitives within each of the two or more EO images.

6. The computer-readable storage medium of claim 5, wherein the bounding primitives are Voroni cells.

7. The computer-readable storage medium of claim 5, wherein calculating a pixel-to-pixel coordinate association between the two or more EO images comprises correlating the centroids of the bounding primitives within the two or more EO images.

8. The computer-readable storage medium of claim 7, wherein the centroids of the bounding primitives are correlated using an image processing technique, the method further comprising seeding the centroid correlation image processing technique using the bounding primitives.

9. The computer-readable storage medium of claim 7, wherein the pixel-to-pixel coordinate associations between the two or more EO images are calculated using an image processing technique, the method further comprising seeding the pixel-to-pixel coordinate association image processing technique using the correlated bounding primitive centroid locations within the two or more EO images.

10. The computer-readable storage medium of claim 2, wherein constructing a 3-D model of the subject matter using the pixel-to-pixel coordinate associations comprises photogrammetrically calculating a 3-D point for each associated pair of pixels in the pixel-to-pixel coordinate associations.

11. The computer-readable storage medium of claim 1, wherein a lidar shot is projected within three or more overlapping EO images, the method further comprising:

photogrammetrically calculating a 3-D point using each of the two or more pixel-to-pixel coordinate associations; and

calculating the 3-D point by minimizing an error metric between the two or more 3-D points photogrammetrically calculated using the two or more pixel-to-pixel coordinate associations.

12. The computer-readable storage medium of claim 1, further comprising calculating point motion vectors for each of the pixel-to-pixel coordinate associations, and wherein the 3-D model of the subject matter is constructed using the point motion vectors.

13. The computer-readable storage medium of claim 12, wherein the point motion vectors are calculated using one selected from the group consisting of an optical flow technique, phase correlation, a block correlation, and a gradient constraint-based registration.

14. The computer-readable storage medium of claim 12, further comprising filtering the point motion vectors, wherein the 3-D model of the subject matter is constructed using the filtered point motion vectors.

15. The computer-readable storage medium of claim 12, wherein a particular portion of the subject matter is captured within 3 or more overlapping EO images.

16. The computer-readable storage medium of claim 1, wherein the EO images in the modeling data are captured using a video camera.

17. The computer-readable storage medium of claim 1, wherein the correlation data comprises navigation data, and wherein the navigation data comprises data indicative of a position and orientation of a lidar as each of the lidar shots were captured, and wherein the navigation data further comprises data indicative of a position and orientation of an EO imaging device as each of the EO images were captured.

18. The computer-readable storage medium of claim 17, the method further comprising estimating a position and orientation of the lidar as each of the lidar shots were captured using the navigation data.

19. The computer-readable storage medium of claim 17, further comprising refining the navigation data using the lidar shots and the estimates of the lidar position and orientation as each of the lidar shots were captured.

20. The computer-readable storage medium of claim 19, wherein the refining comprises applying a point cloud matching technique to the lidar shots and the lidar position and orientation estimates.

21. The computer-readable storage medium of claim 17, further comprising estimating a position and orientation of the EO imaging device as each of the EO images were acquired using the refined navigation data, and wherein each of the lidar shots are projected onto two or more EO images based on the refined navigation data.

22. The computer-readable storage medium of claim 17, wherein a lidar shot projects into two or more EO images, the method further comprising:

setting the projection of a lidar shot as the centroid of a bounding primitive within the two or more EO images into which the lidar shot projects;

seeding a centroid correlation image processing technique using the lidar shot projections;

correlating the centroids of the bounding primitives within the two or more EO images; and

refining the navigation data using the correlated centroid positions within the two or more EO images.

23. The computer-readable storage medium of claim 22, wherein refining the navigation data using the correlated centroid positions comprises correcting the navigation data of the lidar shot to conform to the correlated centroid positions within the two or more EO images.

24. The computer-readable storage medium of claim 21, the method further comprising:

incorporating the refined navigation data into the 3-D model of the subject matter; and

refining the navigation by conforming the navigation data to the 3-D model.

25. The computer-readable storage medium of claim 24, wherein refining the navigation data comprises conforming the navigation and orientation data indicative of a position and orientation of the lidar as each of the lidar shots were acquired to conform to the lidar shot projections within the 3-D model.

26. The computer-readable storage medium of claim 1, the method further comprising calculating a color value for each of the 3-D points in the 3-D model, and wherein a color value of a 3-D point comprises a combination of color values of the EO image pixels used to calculate position of the 3-D point.

27. The computer-readable storage medium of claim 26, wherein the color of the 3-D point comprises a weighted average of the color values of the EO image pixels of one or more pixel-to-pixel coordinate associations used to calculate the 3-D point.

28. The computer-readable storage medium of claim 26, wherein the color of the 3-D point comprises a weighted average of the color values of the EO image pixels within a motion vector used to calculate the 3-D point.

29. The computer-readable storage medium of claim 26, the method further comprising generating a texture atlas to map color values from a plurality of EO images to each of the 3-D points of the 3-D model.

30. The computer-readable storage medium of claim 26, the method further comprising generating one or more textured primitives to provide color values to one or more of the 3-D points of the 3-D model, and wherein the textured primitives include one of a splat, a textured plat, and a textured polygon.

31. A method for constructing a model of a subject matter using a computing device comprising a processor, the method comprising:

acquiring modeling data comprising a plurality of overlapping EO images of the subject matter captured using an EO imaging device, a plurality of lidar shots of the subject matter captured using a lidar, and correlation data;

the computing device estimating a pose of the EO imaging device as each EO image was acquired and estimating a pose of the lidar as each of the lidar shots was acquired;

the computing device projecting each of the lidar shots onto two or more of the EO images using the pose estimates;

the computing device defining bounding primitives for each of the lidar shot projections on the image plane;

calculating pixel-to-pixel coordinate associations between the overlapping EO images, wherein the pixel-to-pixel coordinate associations are calculated using an image processing technique seeded using the bounding primitives; and

constructing a 3-D model of the subject matter using the pixel-to-pixel coordinate associations.

32. A system for constructing a 3-D model of a subject matter using modeling data comprising a plurality of EO images of the subject matter, a plurality of lidar shots of the subject matter, and correlation data associated with the EO images and the lidar shots, comprising:

a computing device comprising a processor;

a correlation module operable on the processor and configured to project each of the plurality of lidar shots onto two or more EO images and to set each of the lidar shot projections as centroids of respective bounding primitives within the respective EO images;

an image processing module operable on the processor and communicatively coupled to the correlation module, the image processing module configured to calculate pixel-to-pixel coordinate associations between the overlapping EO images, wherein the pixel-to-pixel coordinate associations are calculated using an image processing technique seeded using the bounding primitives; and

a modeling module operable on the processor and communicatively coupled to the image processing module, the modeling module configured to construct a 3-D model of the subject matter using the pixel-to-pixel coordinate associations.