New Approach for Beacons Based Mobile Robot Localization using Kalman Filters

Abstract

New approaches on industrial mobile robots are changing the localization systems from old methods such as magnetic tapes to laser beacons based systems and natural landmarks since they are more adaptable and easier to install on the shop floor. Sensor fusion methods needs to be applied since there is information provided from different sources. Extended Kalman Filters are very used in the pose estimation of mobile robots with sensors that detect beacons and measure its distance and angle in a local referential frame. In certain situations, like for example wheels slippage, the number of impulses read for the encoders is wrong, resulting in a very large displacement or rotation and causing a bad estimation at the end of the prediction step. This bad estimation is used for the linearization of the non-linear equations, causing a bad linear approximation and probably a failure in the Kalman Filter. In this paper it is demonstrated that if we use the last state estimation calculated in the update step at the last cycle, instead of the estimation from the prediction step in the actual cycle, the result is an estimator much more robust to errors in the odometry information. Simulated and real results from several experiments are illustrated to demonstrate this new approach.