Enhanced SLAM for a Mobile Robot using Unscented Kalman Filter and Radial Basis Function Neural Network

Abstract

This paper presents a Hybrid filter based Simultaneous Localization and Mapping (SLAM) for a mobile robot to compensate for the Unscented Kalman Filter (UKF) based SLAM errors inherently caused by its linearization process. The proposed Hybrid filter consists of a Radial Basis Function (RBF) and UKF which is a milestone for SLAM applications. A mobile robot autonomously explores the environment by interpreting the scene, building an appropriate map, and localizing itself relative to this map. The proposed approach, based on a Hybrid filter, has some advantages in handling a robot with nonlinear motions because of the learning property of the RBF neural network. The simulation results show the effectiveness of the proposed algorithm comparing with an UKF based SLAM and also it shows that in larger environments has good efficiency.