A mutated FastSLAM using soft computing

A mutated FastSLAM using soft computing PurposeSimultaneous localization and mapping (SLAM) is the problem of determining the pose (position and orientation) of an autonomous robot moving through an unknown environment. The classical FastSLAM is a well-known solution to SLAM. In FastSLAM, a particle filter is used for the robot pose estimation, and the Kalman filter (KF) is used for the feature location’s estimation. However, the performance of the conventional FastSLAM is inconsistent. To tackle this problem, this study aims to propose a mutated FastSLAM (MFastSLAM) using soft computing.Design/methodology/approachThe proposed method uses soft computing. In this approach, particle swarm optimization (PSO) estimator is used for the robot’s pose estimation and an adaptive neuro-fuzzy unscented Kalman filter (ANFUKF) is used for the feature location’s estimation. In ANFUKF, a neuro-fuzzy inference system (ANFIS) supervises the performance of the unscented Kalman filter (UKF) with the aim of reducing the mismatch between the theoretical and actual covariance of the residual sequences to get better consistency.FindingsThe simulation and experimental results indicate that the consistency and estimated accuracy of the proposed algorithm are superior FastSLAM.Originality/valueThe main contribution of this paper is the introduction of MFastSLAM to solve the problems of FastSLAM. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Industrial Robot: An International Journal Emerald Publishing

A mutated FastSLAM using soft computing

Industrial Robot: An International Journal, Volume 44 (4): 12 – Jun 19, 2017

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0143-991X
DOI
10.1108/IR-11-2016-0277
Publisher site
See Article on Publisher Site

Abstract

PurposeSimultaneous localization and mapping (SLAM) is the problem of determining the pose (position and orientation) of an autonomous robot moving through an unknown environment. The classical FastSLAM is a well-known solution to SLAM. In FastSLAM, a particle filter is used for the robot pose estimation, and the Kalman filter (KF) is used for the feature location’s estimation. However, the performance of the conventional FastSLAM is inconsistent. To tackle this problem, this study aims to propose a mutated FastSLAM (MFastSLAM) using soft computing.Design/methodology/approachThe proposed method uses soft computing. In this approach, particle swarm optimization (PSO) estimator is used for the robot’s pose estimation and an adaptive neuro-fuzzy unscented Kalman filter (ANFUKF) is used for the feature location’s estimation. In ANFUKF, a neuro-fuzzy inference system (ANFIS) supervises the performance of the unscented Kalman filter (UKF) with the aim of reducing the mismatch between the theoretical and actual covariance of the residual sequences to get better consistency.FindingsThe simulation and experimental results indicate that the consistency and estimated accuracy of the proposed algorithm are superior FastSLAM.Originality/valueThe main contribution of this paper is the introduction of MFastSLAM to solve the problems of FastSLAM.

Journal

Industrial Robot: An International JournalEmerald Publishing

Published: Jun 19, 2017

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