Spherical simplex sigma-point Kalman filters: A comparison in the inertial navigation of a terrestrial vehicle

Abstract

To determine the position of a mobile robot continues being a complex and interesting challenge in localization algorithms, whose solution requires the use of estimation techniques of nonlinear systems, a well selection of sensors that fulfill the restrictions imposed by the characteristics of the system, and the selection of the suitable algorithm of navigation. In this article, the performance of three variants of Sigma Point Kalman Filter (SPKF) are analyzed and compared, where the selection strategy of spherical simplex sigma points is used in order to improve its performance for real time execution. The analyzed filters are applied to an inertial navigation system that is used for the localization of a terrestrial mobile vehicle. The obtained results of the comparative analysis are illustrated by some simulations.