Extended and Unscented Kalman Filters in a cells-covering method for environment reconstruction

We compare the effectiveness of two widely used filters for nonlinear systems, i.e., the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF), in reconstructing the unknown environment where a mobile robot moves. The reconstruction is obtained by a novel cells-covering algorithm that only uses the distance measurements taken from the robot’s on-board sonar sensors. We show that, despite the superior theoretical properties of the UKF, both filters perform comparably well, and that the proposed algorithm provides good localization performance and a reliable environment reconstruction.