Geometrically stable sampling for the ICP algorithm

N. Gelfand, L. Ikemoto, S. Rusinkiewicz, M. Levoy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

260 Scopus citations

Abstract

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. 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.

Original languageAmerican English
Title of host publicationProceedings - 4th International Conference on 3-D Digital Imaging and Modeling, 3DIM 2003
PublisherIEEE Computer Society
Pages260-267
Number of pages8
ISBN (Electronic)0769519911
DOIs
StatePublished - 2003
Event4th International Conference on 3-D Digital Imaging and Modeling, 3DIM 2003 - Banff, Canada
Duration: Oct 6 2003Oct 10 2003

Publication series

NameProceedings of International Conference on 3-D Digital Imaging and Modeling, 3DIM
Volume2003-January

Other

Other4th International Conference on 3-D Digital Imaging and Modeling, 3DIM 2003
Country/TerritoryCanada
CityBanff
Period10/6/0310/10/03

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Keywords

  • Convergence
  • Error correction
  • Frequency
  • Geometry
  • Iterative algorithms
  • Iterative closest point algorithm
  • Iterative methods
  • Sampling methods
  • Stability
  • Uncertainty

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