Comparison of the SLAM algorithms: Hangar experiments

Abstract

This study purposes to compare two known algorithms in an application scenario of simultaneous localization and mapping (SLAM) and to present issues related with them as well. Mostly used SLAM algorithms Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are compared with respect to the point of accuracy of the robot states, localization and mapping. Because of considering the most implementations in the previous studies, the simulation environments are chosen as big as possible to provide reliable results. In this study, two different hangar regions are tried to be simulated. According to the outcomes of the applications, UKF-based SLAM algorithm has superior performance over the EKF-based one, apart from elapsed time.