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An Improved FastSLAM Algorithm Based on Revised Genetic Resampling and SR-UPF

Research Article

Abstract

FastSLAM is a popular framework which uses a Rao-Blackwellized particle filter to solve the simultaneous localization and mapping problem (SLAM). However, in this framework there are two important potential limitations, the particle depletion problem and the linear approximations of the nonlinear functions. To overcome these two drawbacks, this paper proposes a new FastSLAM algorithm based on revised genetic resampling and square root unscented particle filter (SR-UPF). Double roulette wheels as the selection operator, and fast Metropolis-Hastings (MH) as the mutation operator and traditional crossover are combined to form a new resampling method. Amending the particle degeneracy and keeping the particle diversity are both taken into considerations in this method. As SR-UPF propagates the sigma points through the true nonlinearity, it decreases the linearization errors. By directly transferring the square root of the state covariance matrix, SR-UPF has better numerical stability. Both simulation and experimental results demonstrate that the proposed algorithm can improve the diversity of particles, and perform well on estimation accuracy and consistency.

Keywords

Simultaneous localization and mapping (SLAM) genetic algorithm square root unscented particle filter (SR-UPF) fast Metropolis-Hastings (MH) double roulette wheels 

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Notes

Acknowledgments

Tai-Zhi Lv would like to thank Jiangsu Overseas Research & Training Program for University Prominent Young & Middle-aged Teachers and Presidents for financial support.

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Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2015

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyNanjing University of Science and TechnologyNanjingChina

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