Staff Members - Benha University
Appraisal of Different Particle Filter Resampling Schemes Effect in Robot Localization
• 2012
Publication Information
Authors
Imbaby I. Mahmoud, Asmaa Abd El Tawab, May Salama and Howida A. Abd El-Halym
Keywords
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Journal
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Publisher
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Volume
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Issue
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Pages
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publication.type
Local
Paper Link
Open Link
Supplementary Materials
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Abstract
This paper considers the effect of the Resampling schemes in the behavior of Particle Filter (PF) based robot
localizer. The investigated schemes are Multinomial Resampling, Residual Resampling, Residual Systematic
Resampling, Stratified Resampling and Systematic Resampling. An algorithm is built in Matlab environment to
host these schemes. The performances are evaluated in terms of computational complexity and error from ground
truth and the results are reported. The results showed that the localization plan which adopts the Systematic or
Stratified Resampling scheme achieves higher accuracy localization while decreasing consumed computational
time. However, the difference is not significant. Moreover, a particle excitation strategy is proposed. This strategy
achieved significant improvement in the behavior of PF based robot localization.
localizer. The investigated schemes are Multinomial Resampling, Residual Resampling, Residual Systematic
Resampling, Stratified Resampling and Systematic Resampling. An algorithm is built in Matlab environment to
host these schemes. The performances are evaluated in terms of computational complexity and error from ground
truth and the results are reported. The results showed that the localization plan which adopts the Systematic or
Stratified Resampling scheme achieves higher accuracy localization while decreasing consumed computational
time. However, the difference is not significant. Moreover, a particle excitation strategy is proposed. This strategy
achieved significant improvement in the behavior of PF based robot localization.