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The International Journal of Robotics Research
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Real-time Bounded-error State Estimation for Vehicle Tracking

E. Seignez

Institut d'Électronique Fondamentale CNRS, Université Paris-Sud, F-91400 Orsay, France, Emmanuel.Seignez{at}u-psud.fr

M. Kieffer

Laboratoire des Signaux et Systèmes CNRS, Supélec Université Paris-Sud, Plateau de Moulon, F-91192 Gif-sur-Yvette, France Kieffer{at}lss.supelec.fr

A. Lambert

Institut d'Électronique Fondamentale CNRS, Université Paris-Sud, F-91400 Orsay, France Alain.Lambert{at}u-psud.fr

E. Walter

Laboratoire des Signaux et Systèmes CNRS, Supélec Université Paris-Sud, Plateau de Moulon, F-91192 Gif-sur-Yvette, France Lambert{at}lss.supelec.fr

T. Maurin

Institut d'Électronique Fondamentale CNRS, Université Paris-Sud, F-91400 Orsay, France Thierry.Maurin{at}u-psud.fr

Estimating the configuration of a vehicle is crucial for its navigation. Most approaches are based on (extended) Kalman filtering or particle filtering. An attractive alternative is considered here, which relies on interval analysis. Contrary to classical extended Kalman filtering it allows global localization, and contrary to particle filtering 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 presents a real-time implementation of the process including a description of the platform and its modeling, the integration of the errors on the model and the localization method itself.

Key Words: Autonomous agents • cognitive robotics • wheeled robots • mechanics • design and control • localization • mobile and distributed robotics SLAM • range sensing • sensing and perception • computer vision

The International Journal of Robotics Research, Vol. 28, No. 1, 34-48 (2009)
DOI: 10.1177/0278364908096237
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