Robust autonomous robot tracking using interval analysis
Résumé
This paper deals with robust and autonomous robot tracking using distance measurements provided by a belt of on-board ultrasonic sensors. The measurements errors are assumed to be bounded. The method presented uses a sey-valued nonlinear state estimator. As a Kalman filter, it alternates prediction based on past data and correction to take new measurements into account. Special attention is paid to the treatment of outliers due, e.g., to a partially outdated map or to faulty sensors.
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