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2002

Guaranteed robust nonlinear estimation with application to robot localization

8 years 11 months ago
Guaranteed robust nonlinear estimation with application to robot localization
When reliable prior bounds on the acceptable errors between the data and corresponding model outputs are available, bounded-error estimation techniques make it possible to characterize the set of all acceptable parameter vectors in a guaranteed way, even when the model is nonlinear and the number of data points small. However, when the data may contain outliers, i.e., data points for which these bounds should be violated, this set may turn out to be empty, or at least unrealistically small. The outlier minimal number estimator (OMNE) has been designed to deal with such a situation, by minimizing the number of data points considered as outliers. OMNE has been shown in previous papers to be remarkably robust, even to a majority of outliers. Up to now, it was implemented by random scanning, so its results could not be guaranteed. In this paper, a new algorithm based on set inversion via interval analysis provides a guaranteed OMNE, which is applied to the initial localization of an actual...
Luc Jaulin, Michel Kieffer, Eric Walter, Dominique
Added 23 Dec 2010
Updated 23 Dec 2010
Type Journal
Year 2002
Where TSMC
Authors Luc Jaulin, Michel Kieffer, Eric Walter, Dominique Meizel
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