NDLI: On sample diversity in Particle Filter based robot SLAM

Content Provider	IEEE Xplore Digital Library
Author	Xiuzhi Li Songmin Jia Wei Cui
Copyright Year	2011
Description	Author affiliation: College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing, China (Xiuzhi Li; Songmin Jia)
Abstract	This article proposed a simple and effective methodology for improving diversity of samples in Particle Filter (PF). The motivation lies in the situation that resampling procedure which aims for amending particle degeneracy always leads to particle depletion and less diversity of particles has severe consequence on the filter estimation accuracy. Various resample approaches have been developed in recent years, including multi-nominal resample, residual resample, stratified resample and systematic resample. All of them, however, will inevitably lead to lose of diversity in scatter of particles because they simply replace lower weighed particles with higher weighed particles. In this paper, we developed practical MCMC solutions for drawing particles for PF. The selections of proposal distribution and convergent chain node are taken into careful considerations. It is revealed from the simulations and real experiment that the proposed resampling method is capable of improving the performance of Particle Filter.
Starting Page	1072
Ending Page	1077
File Size	1610563
Page Count	6
File Format	PDF
ISBN	9781457721366
e-ISBN	9781457721380
DOI	10.1109/ROBIO.2011.6181430
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-07
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Particle filters Proposals Simultaneous localization and mapping Markov processes Mobile robots Signal processing algorithms Resample Monte Carlo Markov Chain Particle Filter Simultaneous Localization and Mapping Mobile Robot
Content Type	Text
Resource Type	Article

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