Robust estimation of rotations from relative measurements by maximum likelihood

Nicolas Boumal, Amit Singer, P. A. Absil

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Scopus citations

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 Cramér-Rao bounds suggest the estimator is asymptotically efficient.

Original languageEnglish (US)
Title of host publication2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1156-1161
Number of pages6
ISBN (Print)9781467357173
DOIs
StatePublished - Jan 1 2013
Event52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy
Duration: Dec 10 2013Dec 13 2013

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other52nd IEEE Conference on Decision and Control, CDC 2013
CountryItaly
CityFlorence
Period12/10/1312/13/13

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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    Boumal, N., Singer, A., & Absil, P. A. (2013). Robust estimation of rotations from relative measurements by maximum likelihood. In 2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013 (pp. 1156-1161). [6760038] (Proceedings of the IEEE Conference on Decision and Control). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2013.6760038