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Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation

In the case of a single scene feature, the positioning of an indoor service robot takes a long time, and localization errors are likely to occur. A new method for a hybrid indoor localization system according to multi-sensor fusion is proposed to solve these problems. The localization process is div...

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Detalles Bibliográficos
Autores principales: Shi, Yongliang, Zhang, Weimin, Yao, Zhuo, Li, Mingzhu, Liang, Zhenshuo, Cao, Zhongzhong, Zhang, Hua, Huang, Qiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6211104/
https://www.ncbi.nlm.nih.gov/pubmed/30360423
http://dx.doi.org/10.3390/s18103581
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author Shi, Yongliang
Zhang, Weimin
Yao, Zhuo
Li, Mingzhu
Liang, Zhenshuo
Cao, Zhongzhong
Zhang, Hua
Huang, Qiang
author_facet Shi, Yongliang
Zhang, Weimin
Yao, Zhuo
Li, Mingzhu
Liang, Zhenshuo
Cao, Zhongzhong
Zhang, Hua
Huang, Qiang
author_sort Shi, Yongliang
collection PubMed
description In the case of a single scene feature, the positioning of an indoor service robot takes a long time, and localization errors are likely to occur. A new method for a hybrid indoor localization system according to multi-sensor fusion is proposed to solve these problems. The localization process is divided in two stages: rough positioning and precise positioning. By virtue of the K nearest neighbors based on possibility (KNNBP) algorithm first created in the present study, the rough position of a robot is determined according to the received signal strength indicator (RSSI) of Wi-Fi. Then, the hybrid particle filter localization (HPFL) algorithm improved on the basis of adaptive Monte Carlo localization (AMCL) is adopted to get the precise localization, which integrates various information, including the rough position and information from Lidar, a compass, an occupancy grid map, and encoders. The experiments indicated that the positioning error was 0.05 m; the success rate of localization was 96% with even 3000 particles, and the global positioning time was 1.9 s. However, under the same conditions, the success rate of AMCL was approximately 40%, the required time was approximately 25.6 s, and the positioning accuracy was the same. This indicates that the hybrid indoor location system is efficient and accurate.
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spelling pubmed-62111042018-11-02 Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation Shi, Yongliang Zhang, Weimin Yao, Zhuo Li, Mingzhu Liang, Zhenshuo Cao, Zhongzhong Zhang, Hua Huang, Qiang Sensors (Basel) Article In the case of a single scene feature, the positioning of an indoor service robot takes a long time, and localization errors are likely to occur. A new method for a hybrid indoor localization system according to multi-sensor fusion is proposed to solve these problems. The localization process is divided in two stages: rough positioning and precise positioning. By virtue of the K nearest neighbors based on possibility (KNNBP) algorithm first created in the present study, the rough position of a robot is determined according to the received signal strength indicator (RSSI) of Wi-Fi. Then, the hybrid particle filter localization (HPFL) algorithm improved on the basis of adaptive Monte Carlo localization (AMCL) is adopted to get the precise localization, which integrates various information, including the rough position and information from Lidar, a compass, an occupancy grid map, and encoders. The experiments indicated that the positioning error was 0.05 m; the success rate of localization was 96% with even 3000 particles, and the global positioning time was 1.9 s. However, under the same conditions, the success rate of AMCL was approximately 40%, the required time was approximately 25.6 s, and the positioning accuracy was the same. This indicates that the hybrid indoor location system is efficient and accurate. MDPI 2018-10-22 /pmc/articles/PMC6211104/ /pubmed/30360423 http://dx.doi.org/10.3390/s18103581 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shi, Yongliang
Zhang, Weimin
Yao, Zhuo
Li, Mingzhu
Liang, Zhenshuo
Cao, Zhongzhong
Zhang, Hua
Huang, Qiang
Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation
title Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation
title_full Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation
title_fullStr Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation
title_full_unstemmed Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation
title_short Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation
title_sort design of a hybrid indoor location system based on multi-sensor fusion for robot navigation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6211104/
https://www.ncbi.nlm.nih.gov/pubmed/30360423
http://dx.doi.org/10.3390/s18103581
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