Registration of 3D point cloud data to 2D electro-optical image data
First Claim
1. A method for registration of a plurality of images, comprising:
- acquiring for a common scene a two dimensional image data and a three-dimensional image data;
cropping a three-dimensional volume of point cloud data comprising said three-dimensional image data to remove a portion of said point cloud data comprising a ground surface within said scene;
dividing said three-dimensional volume into a plurality of m sub-volumes, where m is greater than or equal to one;
edge-enhancing said two-dimensional image data;
for each qualifying sub-volume, creating a filtered density image, calculating a two-dimensional correlation surface based on said filtered density image and said two-dimensional image data that has been edge enhanced, finding a peak of the two-dimensional correlation surface, determining a corresponding location of said peak within the two-dimensional image, define a correspondence point set; and
storing said correspondence point set in a point set list;
finding a transformation that minimizes the error between a plurality of said correspondence point sets contained in said point set list; and
applying the transformation to said points in a target data selected from group consisting of the three-dimensional image data and said two-dimensional image data.
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Abstract
Method and system for registration of a two dimensional image data set and a three-dimensional image comprising point cloud data. The method begins by cropping a three-dimensional volume of point cloud data comprising a three-dimensional image data to remove a portion of the point cloud data comprising a ground surface within a scene, and dividing the three-dimensional volume into a plurality of m sub-volumes. Thereafter, the method continues by edge-enhancing a two-dimensional image data. Then, for each qualifying sub-volume, creating a filtered density image, calculating a two-dimensional correlation surface based on the filtered density image and the two-dimensional image data that has been edge enhanced, finding a peak of the two-dimensional correlation surface, determining a corresponding location of the peak within the two-dimensional image, defining a correspondence point set; and storing the correspondence point set in a point set list. Finally, a transformation is determined that minimizes the error between a plurality of the correspondence point sets contained in the point set list.
115 Citations
21 Claims
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1. A method for registration of a plurality of images, comprising:
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acquiring for a common scene a two dimensional image data and a three-dimensional image data; cropping a three-dimensional volume of point cloud data comprising said three-dimensional image data to remove a portion of said point cloud data comprising a ground surface within said scene; dividing said three-dimensional volume into a plurality of m sub-volumes, where m is greater than or equal to one; edge-enhancing said two-dimensional image data; for each qualifying sub-volume, creating a filtered density image, calculating a two-dimensional correlation surface based on said filtered density image and said two-dimensional image data that has been edge enhanced, finding a peak of the two-dimensional correlation surface, determining a corresponding location of said peak within the two-dimensional image, define a correspondence point set; and
storing said correspondence point set in a point set list;finding a transformation that minimizes the error between a plurality of said correspondence point sets contained in said point set list; and applying the transformation to said points in a target data selected from group consisting of the three-dimensional image data and said two-dimensional image data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for registration of a plurality of images, comprising processing means programmed with a set of instructions for performing a series of steps including:
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cropping a three-dimensional volume of point cloud data comprising a three-dimensional image data to remove a portion of said point cloud data comprising a ground surface within a scene; dividing said three-dimensional volume into a plurality of m sub-volumes, where m is greater than or equal to one; edge-enhancing a two-dimensional image data; for each qualifying sub-volume, creating a filtered density image, calculating a two-dimensional correlation surface based on said filtered density image and said two-dimensional image data that has been edge enhanced, finding a peak of the two-dimensional correlation surface, determining a corresponding location of said peak within the two-dimensional image, defining a correspondence point set; and
storing said correspondence point set in a point set list;finding a transformation that minimizes the error between a plurality of said correspondence point sets contained in said point set list; and applying the transformation to said points in a target data selected from group consisting of the three-dimensional image data and said two-dimensional image data. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A computer program embodied on a non-transitory computer-readable medium for performing a series of steps comprising:
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cropping a three-dimensional volume of point cloud data comprising a three-dimensional image data to remove a portion of said point cloud data comprising a ground surface within a scene; dividing said three-dimensional volume into a plurality of m sub-volumes, where m is greater than or equal to one; edge-enhancing said two-dimensional image data; for each qualifying sub-volume, creating a filtered density image, calculating a two-dimensional correlation surface based on said filtered density image and said two-dimensional image data that has been edge enhanced, finding a peak of the two-dimensional correlation surface, determining a corresponding location of said peak within the two-dimensional image, define a correspondence point set; and
storing said correspondence point set in a point set list;finding a transformation that minimizes the error between a plurality of said correspondence point sets contained in said point set list; and applying the transformation to said points in a target data selected from group consisting of the three-dimensional image data and said two-dimensional image data.
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Specification