Location of Repository

Experimental Vehicle Localization by Bounded-Error State Estimation Using Interval Analysis

By Emmanuel Seignez, Michel Kieffer, Alain Lambert, Eric Walter and Thierry Maurin

Abstract

Abstract — Estimating the configuration of a vehicle is crucial for navigation. The most classical approaches are Kalman filtering and Bayesian localization, often implemented via particle filtering. This paper reports on-going experimentation with an attractive alternative approach recently developed and based on interval analysis. Contrary to classical Extended Kalman Filtering, this approach allows global localization, and contrary to Bayesian localization it provides guaranteed results in the sense that a set is computed that contains all of the configurations that are consistent with the data and hypotheses. The approach is particularly robust to outliers. Index Terms — Bounded-error estimation, interval analysis, outliers, robust localization

Year: 2005
OAI identifier: oai:CiteSeerX.psu:10.1.1.178.1394
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.lss.supelec.fr/file... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.