Hierarchical Sampling Optimization of Particle Filter for Global Robot Localization in Pervasive Network Environment

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This paper presents a hierarchical framework for managing the sampling distribution of a particle filter (PF) that estimates the global positions of mobile robots in a large-scale area. The key concept is to gradually improve the accuracy of the global localization by fusing sensor information with different characteristics. The sensor observations are the received signal strength indications (RSSIs) of Wi-Fi devices as network facilities and the range of a laser scanner. First, the RSSI data used for determining certain global areas within which the robot is located are represented as RSSI bins. In addition, the results of the RSSI bins contain the uncertainty of localization, which is utilized for calculating the optimal sampling size of the PF to cover the regions of the RSSI bins. The range data are then used to estimate the precise position of the robot in the regions of the RSSI bins using the core process of the PF. The experimental results demonstrate superior performance compared with other approaches in terms of the success rate of the global localization and the amount of computation for managing the optimal sampling size.
Publisher
WILEY
Issue Date
2019-12
Language
English
Article Type
Article
Citation

ETRI JOURNAL, v.41, no.6, pp.782 - 796

ISSN
1225-6463
DOI
10.4218/etrij.2018-0550
URI
http://hdl.handle.net/10203/268793
Appears in Collection
EE-Journal Papers(저널논문)
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