Enhanced SLAM for a Mobile Robot using Extended Kalman Filter and Neural Networks

Title
Enhanced SLAM for a Mobile Robot using Extended Kalman Filter and Neural Networks
Author(s)
이석규최경식[최경식]
Keywords
FUSION
Issue Date
201004
Publisher
KOREAN SOC PRECISION ENG
Citation
INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING, v.11, no.2, pp.255 - 264
Abstract
This paper presents a Hybrid filter based Simultaneous Localization and Mapping (SLAM) scheme for a mobile robot to compensate for the Extended Kalman Filter (EKF) based SLAM errors inherently caused by its linearization process. The proposed Hybrid filter consists of a Radial Basis Function (RBF) and EKE 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. A probabilistic approach has dominated the solution to the SLAM problem, which is a fundamental requirement for mobile robot navigation. The proposed approach, based on a Hybrid filter, has some advantages in handling a robotic system with nonlinear dynamics because of the learning property of the neural networks. The simulation and experimental results show the effectiveness of the proposed algorithm comparing with an EKE based SLAM and Multi Layer Perceptron (MLP) method.
URI
http://hdl.handle.net/YU.REPOSITORY/22587http://dx.doi.org/10.1007/s12541-010-0029-9
ISSN
1229-8557
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공과대학 > 전기공학과 > Articles
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