NDLI: PSO-FastSLAM: An improved FastSLAM framework using particle swarm optimization

Content Provider	IEEE Xplore Digital Library
Author	Heon-Cheol Lee Shin-Kyu Park Jeong-Sik Choi Beom-Hee Lee
Copyright Year	2009
Description	Author affiliation: School of Electrical Engineering and Computer Sciences, Seoul National University, Seoul, Korea (Heon-Cheol Lee; Shin-Kyu Park; Jeong-Sik Choi; Beom-Hee Lee)
Abstract	FastSLAM is a framework which solves the problem of simultaneous localization and mapping using a Rao-Blackwellized particle filter. Conventional FastSLAM is known to degenerate over time in terms of accuracy due to the particle depletion in resampling phase. In this work, a new FastSLAM framework is proposed to prevent the degeneracy by particle cooperation. First, after resampling phase, a target that represents an estimated robot position is computed using the positions of particles. Then, particle swarm optimization is performed to update the robot position by means of particle cooperation. Computer simulations revealed that the proposed framework shows lower RMS error in both robot and feature positions than conventional FastSLAM.
Starting Page	2763
Ending Page	2768
File Size	233360
Page Count	6
File Format	PDF
ISBN	9781424427932
ISSN	1062922X
DOI	10.1109/ICSMC.2009.5346572
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Particle swarm optimization Simultaneous localization and mapping Particle filters Computer simulation Mobile robots Cybernetics USA Councils Target tracking Particle tracking Random variables particle swarm optimization mobile robot FastSLAM particle filter
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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