Design of augmented extended and unscented Kalman filters for differential-drive mobile robots

In this study, we present the local reconstruction of differential-drive mobile robots position and orientation with an accurate odometry calibration. Starting from the encoders readings and assuming an absolute measurement available, Augmented Extended and Unscented Kalman Filters (AEUKF) are proposed to localize the vehicle while estimating a proper set of odometric parameters. In order to compare their estimation performances explicitly, both observers are designed for the same mobile robot model and run with the equal covariance matrices under the identical initial conditions. In the simulation results, it is shown that Augmented Unscented Kalman Filter (AUKF) outperforms the Augmented Extended Kalman Filter (AEKF). © 2008 Asian Network for Scientific Information.