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.
Guaranteed state estimation tuning for real time applications
2009 IEEE Intelligent Vehicles Symposium ; 453-458
2009-06-01
1118438 byte
Conference paper
Electronic Resource
English
Guaranteed State Estimation Tuning for Real Time Applications
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