Experimental comparison of Bayesian positioning methods based on multi-sensor data fusion

Abstract

Localizing a vehicle consists in estimating its position 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. However, owing to the presence of nonlinearities, the Kalman estimator is applicable only through some recursive variants, among which are the Extended Kalman filter (EKF), the Unscented Kalman Filter (UKF) and the Divided Differences of first and second 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 and consistency.