@inproceedings{9802620a11c6427ca9b402e857d18a34,
title = "Robust estimation of rotations from relative measurements by maximum likelihood",
abstract = "We estimate unknown rotation matrices Ri from a set of measurements of relative rotations RiRTj . Measurements are strongly affected by noise such that a small fraction of them are well concentrated around the true relative rotations while the majority of measurements are outliers bearing little or no information. We propose a maximum likelihood estimator (MLE) that explicitly acknowledges this noise model, yielding a robust estimation algorithm. The MLE is computed via Riemannian trust-region optimization using the Manopt toolbox. Comparisons of the MLE with Cram{\'e}r-Rao bounds suggest the estimator is asymptotically efficient.",
author = "Nicolas Boumal and Amit Singer and Absil, {P. A.}",
year = "2013",
doi = "10.1109/CDC.2013.6760038",
language = "English (US)",
isbn = "9781467357173",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1156--1161",
booktitle = "2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013",
address = "United States",
note = "52nd IEEE Conference on Decision and Control, CDC 2013 ; Conference date: 10-12-2013 Through 13-12-2013",
}