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An improved FastSLAM 2.0 algorithm based on FC&ASD-PSO

Published online by Cambridge University Press:  09 August 2016

Taizhi Lv  and
Maoyan Feng
Taizhi Lv*
Affiliation:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, P. R. China
Maoyan Feng
Affiliation:
School of Information Technology, Jiangsu Maritime Institute, Nanjing, Jiangsu 211170, P. R. China. E-mail: fmy999@163.com
*
*Corresponding author. E-mail: lvtaizhi@163.com
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Summary

FastSLAM 2.0 is a popular framework which uses a Rao-Blackwellized particle filter to solve the simultaneous localization and mapping problem. The sampling process is one of the most important phases in the FastSLAM 2.0 framework. Its estimation accuracy depends heavily on a correct prior knowledge about the control and observation noise statistics (the covariance matrices Q and R). Without the correct prior knowledge about these matrices, the estimation accuracy of the robot path and landmark positions may degrade seriously. However in many applications, the prior knowledge is unknown, or these noises are non-stationary. In this paper, these covariance matrices are supposed to be dynamic and denoted as Qt and Rt. Since there are noises, time-adjacent observations are inconsistent with each other. This inconsistency can reflect the real value of the covariance matrices. By the inconsistency, an extra step is introduced to the FastSLAM 2.0 framework. This step makes Qt and Rt match with their real value by using a particle swarm optimization method based on fractional calculus and alpha stable distribution (FC&ASD-PSO). Both simulation and experimental results show that the proposed algorithm improves the accuracy by the more accurate estimation on the noise covariance matrices.

Keywords

Simultaneous localization and mappingFastSLAM 2.0Particle swarm optimizationFractional calculusAlpha stable distributionMobile robot
Type
Articles
Information
Robotica , Volume 35 , Issue 9 , September 2017 , pp. 1795 - 1815
Copyright
Copyright © Cambridge University Press 2016 

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