Improving the Robustness Properties of Robot Localization Procedures with Respect to Environment Features Uncertainties

摘要

In this paper the localization and environment feature estimation problems are formulated in a stochastic setting and an Extended Kalman Filtering (EKF) approach is proposed for the integration of odometric, gyroscope and sonar measures. As gyroscopic measures are much more reliable than the other ones, the localization algorithm gives rise to a nearly singular EKF. This problem is dealt with defining a lower order non singular EKF.