REGISTRATION OF 3D POINT CLOUD DATA BY CREATION OF FILTERED DENSITY IMAGES
First Claim
1. A method for registration of a plurality of frames of three dimensional (3D) point cloud data concerning a target of interest, comprising:
- acquiring at least a first frame and a second frame, each containing 3D point cloud data collected for a selected object;
creating a density image for each of said first frame and said second frame respectively by projecting said 3D point cloud data from each of said first frame and said second frame to a two dimensional (2D) plane;
using said density images obtained from said first frame and said second frame to determine at least one translation vector;
performing a coarse registration of said 3D point cloud data in at least one of said XY plane and said Z plane using said at least one translation vector.
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Accused Products
Abstract
Method (300) for registration of two or more of frames of three dimensional (3D) point cloud data (200-i, 200-j). A density image for each of the first frame (frame i) and the second frame (frame j) is used to obtain the translation between the images and thus image-to-image point correspondence. Correspondence for each adjacent frame is determined using correlation of the ‘filtered density’ images. The translation vector or vectors are used to perform a coarse registration of the 3D point cloud data in one or more of the XY plane and the Z direction. The method also includes a fine registration process applied to the 3D point cloud data (200-i, 200-j). Corresponding transformations between frames (not just adjacent frames) are accumulated and used in a ‘global’ optimization routine that seeks to find the best translation, rotation, and scale parameters that satisfy all frame displacements.
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Citations
25 Claims
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1. A method for registration of a plurality of frames of three dimensional (3D) point cloud data concerning a target of interest, comprising:
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acquiring at least a first frame and a second frame, each containing 3D point cloud data collected for a selected object; creating a density image for each of said first frame and said second frame respectively by projecting said 3D point cloud data from each of said first frame and said second frame to a two dimensional (2D) plane; using said density images obtained from said first frame and said second frame to determine at least one translation vector; performing a coarse registration of said 3D point cloud data in at least one of said XY plane and said Z plane using said at least one translation vector. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A method for registration of a plurality of frames of three dimensional (3D) point cloud data concerning a target of interest, comprising:
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acquiring at least a first frame and a second frame, each containing 3D point cloud data collected for a selected object; creating a density image for each of said first frame and said second frame respectively by projecting said 3D point cloud data from each of said first frame and said second frame to a two dimensional (2D) plane; using said density images obtained from said first frame and said second frame to determine at least one translation vector; performing a coarse registration of said 3D point cloud data in at least one of said XY plane and said Z plane using said at least one translation vector; selecting said density images for each of said first frame and said second frame to be XY density images formed by setting to zero a z coordinate value of each data point in a 3D point cloud contained in said first and second frame; and selecting said density images for said first frame and said second frame to be XZ density images formed by setting to zero a y coordinate value of each data point in a 3D point cloud contained in said first and second frame.
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20. A method for registration of a plurality of frames of three dimensional (3D) point cloud data concerning a target of interest, comprising:
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acquiring at least a first frame and a second frame, each containing 3D point cloud data collected for a selected object; creating a density image for each of said first frame and said second frame respectively by projecting said 3D point cloud data from each of said first frame and said second frame to a two dimensional (2D) plane; using said density images obtained from said first frame and said second frame to determine at least one translation vector; performing a coarse registration of said 3D point cloud data in at least one of said XY plane and said Z plane using said at least one translation vector; selecting said density images for each of said first frame and said second frame to be XY density images formed by setting to zero a z coordinate value of each data point in a 3D point cloud contained in said first and second frame; selecting said density images for said first frame and said second frame to be XZ density images formed by setting to zero a y coordinate value of each data point in a 3D point cloud contained in said first and second frame; filtering each of said density images to obtain a filtered density image for each of said first frame and said second frame, prior to determining said translation vector. - View Dependent Claims (21, 22, 23, 24, 25)
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Specification