PSO-FastSLAM: an improved FastSLAM framework using particle swarm optimization

Authors:
Heon-Cheol Lee;Shin-Kyu Park;Jeong-Sik Choi;Beom-Hee Lee
Affiliations:
School of Electrical Engineering and Computer Sciences, Seoul National University, Seoul, Korea;School of Electrical Engineering and Computer Sciences, Seoul National University, Seoul, Korea;School of Electrical Engineering and Computer Sciences, Seoul National University, Seoul, Korea;School of Electrical Engineering and Computer Sciences, Seoul National University, Seoul, Korea
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

Citing 8
Cited 2

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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.