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A new compensation technique based on analysis of resampling process in FastSLAM

Published online by Cambridge University Press:  01 March 2008

Nosan Kwak ,
Gon-Woo Kim  and
Beom-Hee Lee
Nosan Kwak*
Affiliation:
School of Electrical Engineering, Seoul National University, Seoul 151-744, Korea.
Gon-Woo Kim
Affiliation:
Applied Robot Technology, Korea Institute of Technology, Cheonan, Chungnam 330-825, Korea.
Beom-Hee Lee
Affiliation:
School of Electrical Engineering, Seoul National University, Seoul 151-744, Korea.
*
*Corresponding author. E-mail: robot97@snu.ac.kr
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Summary

The state-of-the-art FastSLAM algorithm has been shown to cause a particle depletion problem while performing simultaneous localization and mapping for mobile robots. As a result, it always produces over-confident estimates of uncertainty as time progresses. This particle depletion problem is mainly due to the resampling process in FastSLAM, which tends to eliminate particles with low weights. Therefore, the number of particles to conduct loop-closure decreases, which makes the performance of FastSLAM degenerate. The resampling process has not been thoroughly analyzed even though it is the main reason for the particle depletion problem. In this paper, standard resampling algorithms (systematic residual and partial resampling), a rank-based resampling adopting genetic algorithms are analyzed using computer simulations. Several performance measures such as the effective sample size, the number of distinct particles, estimation errors, and complexity are used for the thorough analysis of the resampling algorithms. Moreover, a new compensation technique is proposed instead of resampling to resolve the particle depletion problem in FastSLAM. In estimation errors, the compensation technique outperformed other resampling algorithms though its run-time was longer than those of others. The most appropriate time to instigate compensation to reduce the run-time was also analyzed with the diminishing number of particles.

Keywords

FastSLAMParticle filterResamplingParticle depletionCompensationMobile robot
Type
Article
Information
Robotica , Volume 26 , Issue 2 , March 2008 , pp. 205 - 217
Copyright
Copyright © Cambridge University Press 2007

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