REGISTRATION OF 3D POINT CLOUD DATA USING EIGENANALYSIS

• US 20090232355A1
• Filed: 03/12/2008
• Published: 09/17/2009
• Est. Priority Date: 03/12/2008
• Status: Abandoned Application

• Current Assignee: Harris Corporation (L3Harris Technologies, Inc.)
• Original Assignee: Harris Corporation (L3Harris Technologies, Inc.)
• Inventors: Minear, Kathleen, Blask, Steven G., Gluvna, Katie
• Application Number:
  US12/047,066
• Publication Number:
  US 20090232355A1
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06T 2207/10032 Satellite or aerial image; Remote sensing
• CPC Class Codes: G06T 7/33 using feature-based methods
• CPC Class Codes: G06T 7/35 using statistical methods
• CPC Class Codes: G06V 10/7515 Shifting the patterns to accommodate for positional errors
• CPC Class Codes: G06V 20/64 Three-dimensional objects

Abstract

Method (300) for registration of n frames 3D point cloud data. Frame pairs (200i, 200j) are selected from among the n frames and sub-volumes (702) within each frame are defined. Qualifying sub-volumes are identified in which the 3D point cloud data has a blob-like structure. A location of a centroid associated with each of the blob-like objects is also determined. Correspondence points between frame pairs are determined using the locations of the centroids in corresponding sub-volumes of different frames. Thereafter, the correspondence points are used to simultaneously calculate for all n frames, global translation and rotation vectors for registering all points in each frame. Data points in the n frames are then transformed using the global translation and rotation vectors to provide a set of n coarsely adjusted frames.

Claims

1. A method for registration of a plurality of frames of three dimensional (3D) point cloud data concerning a target of interest, comprising:

acquiring a plurality of n frames, each containing 3D point cloud data collected for a selected geographic location;

defining a plurality of frame pairs from among said plurality of n frames, said frame pairs comprising both adjacent and non-adjacent frames in a series of said frames;

defining a plurality of sub-volumes within each said frame of said plurality of frames;

identifying qualifying ones of said plurality of sub-volumes in which the 3D point cloud data has a blob-like structure;

determining a location of a centroid associated with each of said blob-like objects;

using the locations of said centroids in corresponding sub-volumes of different frames to determine centroid correspondence points between frame pairs;

using said centroid correspondence points to simultaneously calculate for all n frames, global values of R_jT_j for coarse registration of each frame, where R_j is the rotation vector necessary for aligning or registering all points in each frame j to frame i, and T_j is the translation vector for aligning or registering all points in frame j with frame i;

transforming all data points in said n frames using said global values of R_jT_j to provide a set of n coarsely adjusted frames.

2. The method according to claim 1, wherein said identifying step further comprises performing an Eigen analysis for each of said sub-volumes to determine if it contains a blob-like structure.

3. The method according to claim 1, wherein said identifying step further comprises determining whether said sub-volume contains at least a predetermined number of data points.

4. The method according to claim 1, further comprising, exclusively defining said plurality of sub-volumes within a horizontal slice of the 3D point cloud data.

5. The method according to claim 1, further comprising noise filtering each of said n frames to remove noise.

6. The method according to claim 1, wherein said step of determining centroid correspondence points further comprises identifying a location of a first centroid in a qualifying sub-volume of a first frame of a frame pair, which most closely matches the location of a second centroid from the qualifying sub-volume of a second frame of a frame pair.

7. The method according to claim 6, wherein said step of determining centroid correspondence points is performed by using a K-D tree search method.

8. The method according to claim 1, further comprising processing all said coarsely adjusted frames in a further registration step to provide a more precise registration of the 3D point cloud data in all frames.

9. The method according to claim 8, further comprising identifying correspondence points as between frames comprising each frame pair,

10. The method according to claim 9, wherein said identifying correspondence points step further comprises identifying data points in a qualifying sub-volume of a first frame of a frame pair, which most closely matches the location of a second data point from the qualifying sub-volume of a second frame of a frame pair.

11. The method according to claim 10, wherein said step of identifying correspondence points is performed using a K-D tree search method.

12. The method according to claim 10 further comprising using said correspondence points to simultaneously calculate for all n frames, global values of R_jT_j for fine registration of each frame, where R_j is the rotation vector necessary for aligning or registering all points in each frame j to frame i, and T_j is the translation vector for aligning or registering all points in frame j with frame i.

13. The method according to claim 12, further comprising transforming all data points in said n frames using said global values of R_jT_j to provide a set of n finely adjusted frames.

14. The method according to claim 13, further comprising repeating said steps of identifying correspondence points, simultaneously calculating global values of R_jT_j for fine registration of each frame, and transforming step until at least one optimization parameter has been satisfied.

15. A method for registration of a plurality of frames of three dimensional (3D) point cloud data concerning a target of interest, comprising:

selecting a plurality of frame pairs from among said plurality of n frames containing 3D point cloud data for a scene;

defining a plurality of sub-volumes within each said frame of said plurality of frames;

identifying qualifying ones of said plurality of sub-volumes in which the 3D point cloud data comprises a pre-defined blob-like object;

determining a location of a centroid associated with each of said blob-like objects;

using the locations of said centroids in corresponding sub-volumes of different frames to determine centroid correspondence points between frame pairs;

using said centroid correspondence points to simultaneously calculate for all n frames, global values of R_jT_j for coarse registration of each frame, where R_j is the rotation vector necessary for aligning or registering all points in each frame j to frame i, and T_j is the translation vector for aligning or registering all points in frame j with frame i.

16. The method according to claim 15, further comprising transforming all data points in said n frames using said global values of R_jT_j to provide a set of n coarsely adjusted frames.

17. The method according to claim 16, wherein said identifying step further comprises performing an Eigen analysis for each of said sub-volumes to determine if it contains said pre-defined blob-like object.

18. The method according to claim 15, wherein said step of determining centroid correspondence points further comprises identifying a location of a first centroid in a qualifying sub-volume of a first frame of a frame pair, which most closely matches the location of a second centroid from the qualifying sub-volume of a second frame of a frame pair.

19. The method according to claim 15, further comprising processing all said coarsely adjusted frames in a further registration step to provide a more precise registration of the 3D point cloud data in all frames.

20. The method according to claim 19, further comprising identifying correspondence points as between frames comprising each frame pair,

21. The method according to claim 20, wherein said identifying correspondence points step further comprises identifying data points in a qualifying sub-volume of a first frame of a frame pair, which most closely matches the location of a second data point from the qualifying sub-volume of a second frame of a frame pair.

22. The method according to claim 21, wherein said step of identifying correspondence points is performed using a K-D tree search method.

23. The method according to claim 21 further comprising using said correspondence points to simultaneously calculate for all n frames, global values of R_jT_j for fine registration of each frame, where R_j is the rotation vector necessary for aligning or registering all points in each frame j to frame i, and T_j is the translation vector for aligning or registering all points in frame j with frame i.

24. The method according to claim 15, further comprising noise filtering each of said n frames to remove noise.

25. A method for registration of a plurality of frames of three dimensional (3D) point cloud data concerning a target of interest, comprising:

acquiring a plurality of n frames, each containing 3D point cloud data collected for a selected geographic location;

performing filtering on each of said n frames to remove noise;

defining a plurality of frame pairs from among said plurality of n frames, said frame pairs comprising both adjacent and non-adjacent frames in a series of said frames;

defining a plurality of sub-volumes within each said frame of said plurality of frames;

identifying qualifying ones of said plurality of sub-volumes in which the 3D point cloud data has a blob-like structure;

determining a location of a centroid associated with each of said blob-like objects;

using the locations of said centroids in corresponding sub-volumes of different frames to determine centroid correspondence points between frame pairs;

using said centroid correspondence points to simultaneously calculate for all n frames, global values of R_jT_j for coarse registration of each frame, where R_j is the rotation vector necessary for aligning or registering all points in each frame j to frame i, and T_j is the translation vector for aligning or registering all points in frame j with frame i;

transforming all data points in said n frames using said global values of R_jT_j to provide a set of n coarsely adjusted frames.