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Relational FastSLAM: an improved Rao-Blackwellized particle filtering framework using particle swarm characteristics

Published online by Cambridge University Press:  15 October 2014

Seung-Hwan Lee ,
Gyuho Eoh  and
Beom H. Lee
Seung-Hwan Lee*
Affiliation:
Seoul National University (ASRI), Seoul 151600, South Korea
Gyuho Eoh
Affiliation:
Seoul National University (ASRI), Seoul 151600, South Korea
Beom H. Lee
Affiliation:
Seoul National University (ASRI), Seoul 151600, South Korea
*
*Corresponding author. E-mail: leeyiri1@snu.ac.kr
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Summary

This paper presents an improved Rao-Blackwellized particle filtering framework with consideration of the particle swarm characteristics in FastSLAM, called Relational FastSLAM or R-FastSLAM. The R-FastSLAM seeks to cope with the inherent problems of FastSLAM, i.e., a particle depletion problem and an error accumulation problem in large environments. The R-FastSLAM uses the particle swarm characteristics in calculating the importance weight and maintaining a particle formation. We assign more accurate weights to particles by clustering them using the Expectation-Maximization (EM) algorithm according to an adaptive weight compensation scheme. In addition, particles constitute an adaptive triangular mesh formation to maintain multiple data association hypotheses without any resampling step. Its outstanding accomplishments are verified on simulations and a test using the Victoria Park dataset by comparing the standard FastSLAM 2.0 with the particle swarm optimization based FastSLAM.

Keywords

Rao-Blackwellized particle filterFastSLAM 2.0Triangular formationEM algorithmParticle swarm characteristics
Type
Articles
Information
Robotica , Volume 34 , Issue 6 , June 2016 , pp. 1282 - 1296
Copyright
Copyright © Cambridge University Press 2014 

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