Enhanced UAV pose estimation using a KF: experimental validation

Abstract

An experimental validation for improving pose estimation using a linear Kalman Filter (KF) is presented in this paper. The procedure is designed to lead with localization data degraded or lost. The methodology is focused on determination, tuning and dynamics changes in the covariance matrices in the KF algorithm. Several simulations are carried out in order to validate the methodology. Similarly several flights tests are conducted in real time for validating the observer scheme. A localization system is used and modified for emulating the GPS performance. Main results show the good behavior of the proposed methodology and a video of them is available for showing the capabilities of the algorithm.