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Published Articles >> Table of Contents >> Abstract
Fourth International Conference on 3-D Digital Imaging and Modeling (3DIM '03)
p. 260
Geometrically Stable Sampling for the ICP Algorithm
Natasha Gelfand, Stanford University
Szymon Rusinkiewicz, Princeton University
Leslie Ikemoto, Stanford University
Marc Levoy, Stanford University
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IM.2003.1240258
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| Abstract |
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The Iterative Closest Point (ICP) algorithm is a widely used
method for aligning three-dimensional point sets. The quality
of alignment obtained by this algorithm depends heavily
on choosing good pairs of corresponding points in the two
datasets. If too many points are chosen from featureless regions
of the data, the algorithm converges slowly, finds the
wrong pose, or even diverges, especially in the presence of
noise or miscalibration in the input data. In this paper, we
describe a method for detecting uncertainty in pose, and we
propose a point selection strategy for ICP that minimizes
this uncertainty by choosing samples that constrain potentially
unstable transformations.
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 Additional Information
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Citation:
Natasha Gelfand, Szymon Rusinkiewicz, Leslie Ikemoto, Marc Levoy,
"Geometrically Stable Sampling for the ICP Algorithm,"
3dim,
p. 260,
Fourth International Conference on 3-D Digital Imaging and Modeling (3DIM '03),
2003
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