Hybrid filter based simultaneous localization and mapping for a mobile robot

Authors:
Amir Panah;Karim Faez
Affiliations:
Mechatronics Research Laboratory, and Young Researchers Club, Qazvin Islamic Azad University, Qazvin, Iran;Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran
Venue:
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Year:
2011

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Abstract

A mobile robot autonomously explores the environment by interpreting the scene, building an appropriate map, and localizing itself relative to this map. This paper presents a Hybrid filter based Simultaneous Localization and Mapping (SLAM) approach 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 Multi Layer Perceptron (MLP) for neural network and UKF which is a milestone for SLAM applications. The proposed approach, based on a Hybrid filter, has some advantages in handling a robotic system with nonlinear motions because of the learning property of the MLP neural network. The simulation results show the effectiveness of the proposed algorithm comparing with an UKF based SLAM.