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 potential unstable transformations.

@inproceedings{Gelfand:2003:GSS,
   author = "Natasha Gelfand and Leslie Ikemoto and Szymon Rusinkiewicz and Marc
      Levoy",
   title = "Geometrically Stable Sampling for the {ICP} Algorithm",
   booktitle = "Fourth International Conference on 3D Digital Imaging and Modeling
      (3DIM)",
   year = "2003",
   month = oct
}