Outlier Robust ICP for Minimizing Fractional RMSD
<|reference_start|>Outlier Robust ICP for Minimizing Fractional RMSD: 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.<|reference_end|>
@article{phillips2006outlier,
    title={Outlier Robust ICP for Minimizing Fractional RMSD},
    author={Jeff M. Phillips and Ran Liu and Carlo Tomasi},
    journal={arXiv preprint arXiv:cs/0606098},
    year={2006},
    number={Duke University Technical Report: CS-2006-05},
    archivePrefix={arXiv},
    eprint={cs/0606098},
    primaryClass={cs.GR cs.CG}
}