Experimental Comparison of Bayesian Outdoor Vehicle Localization Filters
نویسندگان
چکیده
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 DividedDifferences of 1 and 2 order (DD1 and DD2).We have compared these filters using the same experimental data. The results obtained aim to rank these approaches by their performances in terms of accuracy, confidence and consistency.
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