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학술지 Hierarchical Sampling Optimization of Particle Filter for Global Robot Localization in Pervasive Network Environment
Cited 2 time in scopus Download 9 time
저자
이유철, 명현
발행일
201912
출처
ETRI Journal, v.41 no.6, pp.782-796
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.2018-0550
협약과제
17PV1100, 쾌적한 실내환경을 위한 지능형 종합 공기질 관리 솔루션 개발, 이유철
초록
© 2019 ETRI 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.
키워드
global localization, mobile robot, particle filter, RSSI histogram bin, sampling optimization
KSP 제안 키워드
Hierarchical framework, Mobile robots, Optimal sampling, Particle filter (pf), Range data, Robot localization, Sampling Optimization, Sampling distribution, Sampling size, Sensor information, Sensor observation