Unsupervised calibration of wheeled mobile platforms

This paper describes an unsupervised approach to retrieve the kinematic parameters of a wheeled mobile robot. The robot chooses which action to take in order to minimize the uncertainty in the parameter estimate and to fully explore the parameter space. Our method explores the effects of a set of elementary motion on the platform to dynamically select the best action and to stop the process when the estimate can be no further improved. We tested our approach both in simulation and with real robots. Our method is reported to obtain in shorter time parameter estimates that are statistically more accurate than the ones obtained by steering the robot on predefined patterns.

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