@inproceedings{bc211b823fe84a4d836e83288c143d43,
title = "Geometrically stable sampling for the ICP algorithm",
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.",
keywords = "Convergence, Error correction, Frequency, Geometry, Iterative algorithms, Iterative closest point algorithm, Iterative methods, Sampling methods, Stability, Uncertainty",
author = "N. Gelfand and L. Ikemoto and S. Rusinkiewicz and M. Levoy",
note = "Publisher Copyright: {\textcopyright} 2003 IEEE.; 4th International Conference on 3-D Digital Imaging and Modeling, 3DIM 2003 ; Conference date: 06-10-2003 Through 10-10-2003",
year = "2003",
doi = "10.1109/IM.2003.1240258",
language = "English (US)",
series = "Proceedings of International Conference on 3-D Digital Imaging and Modeling, 3DIM",
publisher = "IEEE Computer Society",
pages = "260--267",
booktitle = "Proceedings - 4th International Conference on 3-D Digital Imaging and Modeling, 3DIM 2003",
address = "United States",
}