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Robust Square-Root Cubature FastSLAM with Genetic Operators

Published online by Cambridge University Press:  26 August 2020

Ramazan Havangi Open the ORCID record for Ramazan Havangi [Opens in a new window]
Ramazan Havangi*
Affiliation:
Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
*
*Corresponding author. E-mail: Havangi@birjand.ac.ir
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Summary

An improved FastSLAM based on the robust square-root cubature Kalman filter (RSRCKF) with partial genetic resampling is proposed in this paper. In the proposed method, RSRCKF is used to design the proposal distribution of FastSLAM and to estimate environment landmarks. The proposed method does not require a priori knowledge of the noise statistics. In addition, to increase diversity, it uses the genetic operators-based strategy to further improve the particle diversity. In fact, a partial genetic resampling operation is carried out to maintain the diversity of particles. The proposed method is compared with other methods via simulation and experimental data. It can be seen from the results that the proposed method provides significantly more accurate and robust estimation results compared with other methods even with fewer particles and unknown a priori. In addition, the consistency of the proposed method is better than that of other methods.

Keywords

SLAMH-infinity filteringParticle filterCubature Kalman filterUFastSLAM
Type
Articles
Information
Robotica , Volume 39 , Issue 4 , April 2021 , pp. 665 - 685
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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