  We describe a variation of the iterative closest point (ICP) algorithm for
aligning two point sets under a set of transformations. Our algorithm is
superior to previous algorithms because (1) in determining the optimal
alignment, it identifies and discards likely outliers in a statistically robust
manner, and (2) it is guaranteed to converge to a locally optimal solution. To
this end, we formalize a new distance measure, fractional root mean squared
distance (frmsd), which incorporates the fraction of inliers into the distance
function. We lay out a specific implementation, but our framework can easily
incorporate most techniques and heuristics from modern registration algorithms.
We experimentally validate our algorithm against previous techniques on 2 and 3
dimensional data exposed to a variety of outlier types.
