Hierarchical sampling optimization of particle filter for global robot localization in pervasive network environment

Yu Cheol Lee, Hyun Myung

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)782-796
Number of pages15
JournalETRI Journal
Volume41
Issue number6
DOIs
StatePublished - 1 Dec 2019

Keywords

  • global localization
  • mobile robot
  • particle filter
  • RSSI histogram bin
  • sampling optimization

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