Floor plan-free particle filter for indoor positioning of industrial vehicles
Main Author: | |
---|---|
Publication Date: | 2020 |
Other Authors: | , , |
Language: | eng |
Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
Download full: | http://hdl.handle.net/1822/70557 |
Summary: | Industry 4.0 is triggering the rapid development of solutions for indoor localization of industrial ve- hicles in the factories of the future. Either to support indoor navigation or to improve the operations of the factory, the localization of industrial vehicles imposes demanding requirements such as high accuracy, coverage of the entire operating area, low convergence time and high reliability. Industrial vehicles can be located using Wi-Fi fingerprinting, although with large positioning errors. In addition, these vehicles may be tracked with motion sensors, however an initial position is necessary and these sensors often suffer from cumulative errors (e.g. drift in the heading). To overcome these problems, we propose an indoor positioning system (IPS) based on a particle filter that combines Wi-Fi fingerprinting with data from motion sensors (displacement and heading). Wi-Fi position estimates are obtained using a novel approach, which explores signal strength measurements from multiple Wi-Fi interfaces. This IPS is capable of locating a vehicle prototype without prior knowledge of the starting position and heading, without depending on the building’s floor plan. An average positioning error of 0.74 m was achieved in performed tests in a factory-like building. |
id |
RCAP_d42931c6cb6e895a00719dd1b184cb8a |
---|---|
oai_identifier_str |
oai:repositorium.sdum.uminho.pt:1822/70557 |
network_acronym_str |
RCAP |
network_name_str |
Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
repository_id_str |
https://opendoar.ac.uk/repository/7160 |
spelling |
Floor plan-free particle filter for indoor positioning of industrial vehiclesIndoor positioningParticle filterWi-Fi fingerprintingSensor fusionIndustrial vehiclesEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaIndustry 4.0 is triggering the rapid development of solutions for indoor localization of industrial ve- hicles in the factories of the future. Either to support indoor navigation or to improve the operations of the factory, the localization of industrial vehicles imposes demanding requirements such as high accuracy, coverage of the entire operating area, low convergence time and high reliability. Industrial vehicles can be located using Wi-Fi fingerprinting, although with large positioning errors. In addition, these vehicles may be tracked with motion sensors, however an initial position is necessary and these sensors often suffer from cumulative errors (e.g. drift in the heading). To overcome these problems, we propose an indoor positioning system (IPS) based on a particle filter that combines Wi-Fi fingerprinting with data from motion sensors (displacement and heading). Wi-Fi position estimates are obtained using a novel approach, which explores signal strength measurements from multiple Wi-Fi interfaces. This IPS is capable of locating a vehicle prototype without prior knowledge of the starting position and heading, without depending on the building’s floor plan. An average positioning error of 0.74 m was achieved in performed tests in a factory-like building.FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, the PhD fellowship PD/BD/137401/2018 and the Technological Development in the scope of the projects in co-promotion no 002814/2015 (iFACTORY 2015-2018)CEUR-WsUniversidade do MinhoSilva, Ivo Miguel MenezesMoreira, AdrianoNicolau, Maria JoãoPendão, Cristiano Gonçalves2020-06-022020-06-02T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/1822/70557engIvo Silva, Adriano Moreira, Maria João Nicolau, Cristiano Pendão, “Floor Plan-free Particle Filter for Indoor Positioning of Industrial Vehicles”, in proceedings of the International Conference on Localization and GNSS, ICL-GNSS 2020, 2-4 June, Tampere, Finland (online), 2020, Volume 2626, ISSN: 1613-00731613-0073http://ceur-ws.org/Vol-2626/paper2.pdfinfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-11T04:45:16Zoai:repositorium.sdum.uminho.pt:1822/70557Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T14:57:30.352667Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
dc.title.none.fl_str_mv |
Floor plan-free particle filter for indoor positioning of industrial vehicles |
title |
Floor plan-free particle filter for indoor positioning of industrial vehicles |
spellingShingle |
Floor plan-free particle filter for indoor positioning of industrial vehicles Silva, Ivo Miguel Menezes Indoor positioning Particle filter Wi-Fi fingerprinting Sensor fusion Industrial vehicles Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
title_short |
Floor plan-free particle filter for indoor positioning of industrial vehicles |
title_full |
Floor plan-free particle filter for indoor positioning of industrial vehicles |
title_fullStr |
Floor plan-free particle filter for indoor positioning of industrial vehicles |
title_full_unstemmed |
Floor plan-free particle filter for indoor positioning of industrial vehicles |
title_sort |
Floor plan-free particle filter for indoor positioning of industrial vehicles |
author |
Silva, Ivo Miguel Menezes |
author_facet |
Silva, Ivo Miguel Menezes Moreira, Adriano Nicolau, Maria João Pendão, Cristiano Gonçalves |
author_role |
author |
author2 |
Moreira, Adriano Nicolau, Maria João Pendão, Cristiano Gonçalves |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Silva, Ivo Miguel Menezes Moreira, Adriano Nicolau, Maria João Pendão, Cristiano Gonçalves |
dc.subject.por.fl_str_mv |
Indoor positioning Particle filter Wi-Fi fingerprinting Sensor fusion Industrial vehicles Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
topic |
Indoor positioning Particle filter Wi-Fi fingerprinting Sensor fusion Industrial vehicles Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
description |
Industry 4.0 is triggering the rapid development of solutions for indoor localization of industrial ve- hicles in the factories of the future. Either to support indoor navigation or to improve the operations of the factory, the localization of industrial vehicles imposes demanding requirements such as high accuracy, coverage of the entire operating area, low convergence time and high reliability. Industrial vehicles can be located using Wi-Fi fingerprinting, although with large positioning errors. In addition, these vehicles may be tracked with motion sensors, however an initial position is necessary and these sensors often suffer from cumulative errors (e.g. drift in the heading). To overcome these problems, we propose an indoor positioning system (IPS) based on a particle filter that combines Wi-Fi fingerprinting with data from motion sensors (displacement and heading). Wi-Fi position estimates are obtained using a novel approach, which explores signal strength measurements from multiple Wi-Fi interfaces. This IPS is capable of locating a vehicle prototype without prior knowledge of the starting position and heading, without depending on the building’s floor plan. An average positioning error of 0.74 m was achieved in performed tests in a factory-like building. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-06-02 2020-06-02T00:00:00Z |
dc.type.driver.fl_str_mv |
conference paper |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/70557 |
url |
http://hdl.handle.net/1822/70557 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ivo Silva, Adriano Moreira, Maria João Nicolau, Cristiano Pendão, “Floor Plan-free Particle Filter for Indoor Positioning of Industrial Vehicles”, in proceedings of the International Conference on Localization and GNSS, ICL-GNSS 2020, 2-4 June, Tampere, Finland (online), 2020, Volume 2626, ISSN: 1613-0073 1613-0073 http://ceur-ws.org/Vol-2626/paper2.pdf |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
CEUR-Ws |
publisher.none.fl_str_mv |
CEUR-Ws |
dc.source.none.fl_str_mv |
reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia instacron:RCAAP |
instname_str |
FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
collection |
Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
repository.name.fl_str_mv |
Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia |
repository.mail.fl_str_mv |
info@rcaap.pt |
_version_ |
1833595002164346880 |