Guaranteed state estimation tuning for real time applications

Abstract

Estimating the configuration of a vehicle is crucial for navigation. The most classical approaches are (extended) Kalman filtering and Markov localization, often implemented via particle filtering. Interval analysis allows an alternative approach: bounded-error localization. Contrary to classical Extended Kalman Filtering, this approach allows global localisation, and contrary to Markov 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. This paper describes the bounded-error localization algorithms so as to present a complexity study and how to achieve a real time implementation.