Experimental comparison of Kalman Filters for vehicle localization

Abstract

Localizing a vehicle consists in estimating its state by merging data from proprioceptive sensors (inertial measurement unit, gyrometer, odometer, etc.) and exteroceptive sensors (GPS sensor). A well known solution in state estimation is provided by the Kalman filter. But, due to the presence of nonlinearities, the Kalman estimator is applicable only through some alternatives among which the Extended Kalman filter (EKF), the Unscented Kalman Filter (UKF) and the Divided Differences of 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> order (DD1 and DD2). We have compared these filters using the same experimental data. The results obtained are aimed at ranking these approaches by their performances in terms of accuracy and consistency.