Deep learning-based UWB-IMU data fusion for indoor positioning in industrial scenario

Accurate and precise wireless infrastructure-based positioning systems become crucial as industries move towards flexible, portable, and autonomous transportation systems such as Automated Guided Vehicles (AGVs). Multipath-dominant dynamic environments like industries present significant challenges...

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Autores: Muthineni, Karthik|||0000-0002-8827-2013, Artemenko, Alexander, Vidal Manzano, José|||0000-0002-1985-2065, Nájar Martón, Montserrat|||0000-0003-3507-5689, Catalán Cid, Marisa, Paradells Aspas, Josep|||0000-0003-4185-2202
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/430121
Acceso en línea:https://hdl.handle.net/2117/430121
https://dx.doi.org/10.1109/OJVT.2025.3566888
Access Level:acceso abierto
Palabra clave:Autonomous transportation systems
Data synchronization
Deep neural networks
Industries
Inertial measurement units
Ultra-wideband
Wireless infrastructure-based positioning
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
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oai_identifier_str oai:upcommons.upc.edu:2117/430121
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Deep learning-based UWB-IMU data fusion for indoor positioning in industrial scenario
title Deep learning-based UWB-IMU data fusion for indoor positioning in industrial scenario
spellingShingle Deep learning-based UWB-IMU data fusion for indoor positioning in industrial scenario
Muthineni, Karthik|||0000-0002-8827-2013
Autonomous transportation systems
Data synchronization
Deep neural networks
Industries
Inertial measurement units
Ultra-wideband
Wireless infrastructure-based positioning
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
title_short Deep learning-based UWB-IMU data fusion for indoor positioning in industrial scenario
title_full Deep learning-based UWB-IMU data fusion for indoor positioning in industrial scenario
title_fullStr Deep learning-based UWB-IMU data fusion for indoor positioning in industrial scenario
title_full_unstemmed Deep learning-based UWB-IMU data fusion for indoor positioning in industrial scenario
title_sort Deep learning-based UWB-IMU data fusion for indoor positioning in industrial scenario
dc.creator.none.fl_str_mv Muthineni, Karthik|||0000-0002-8827-2013
Artemenko, Alexander
Vidal Manzano, José|||0000-0002-1985-2065
Nájar Martón, Montserrat|||0000-0003-3507-5689
Catalán Cid, Marisa
Paradells Aspas, Josep|||0000-0003-4185-2202
author Muthineni, Karthik|||0000-0002-8827-2013
author_facet Muthineni, Karthik|||0000-0002-8827-2013
Artemenko, Alexander
Vidal Manzano, José|||0000-0002-1985-2065
Nájar Martón, Montserrat|||0000-0003-3507-5689
Catalán Cid, Marisa
Paradells Aspas, Josep|||0000-0003-4185-2202
author_role author
author2 Artemenko, Alexander
Vidal Manzano, José|||0000-0002-1985-2065
Nájar Martón, Montserrat|||0000-0003-3507-5689
Catalán Cid, Marisa
Paradells Aspas, Josep|||0000-0003-4185-2202
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Autonomous transportation systems
Data synchronization
Deep neural networks
Industries
Inertial measurement units
Ultra-wideband
Wireless infrastructure-based positioning
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
topic Autonomous transportation systems
Data synchronization
Deep neural networks
Industries
Inertial measurement units
Ultra-wideband
Wireless infrastructure-based positioning
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
description Accurate and precise wireless infrastructure-based positioning systems become crucial as industries move towards flexible, portable, and autonomous transportation systems such as Automated Guided Vehicles (AGVs). Multipath-dominant dynamic environments like industries present significant challenges for wireless signal propagation and affect wireless positioning accuracy due to the interplay of reflected signals from obstacles. The achievable indoor positioning accuracy of the target AGV can be enhanced by fusing the measurements from the wireless infrastructure with the target's onboard sensor data. Nevertheless, the lack of correspondence between the wireless infrastructure and the target's onboard sensors causes the measurements from these two systems to arrive at irregular time steps. Using asynchronous measurements in the data fusion process can degrade the overall positioning accuracy of the target AGV. This paper proposes a novel deep learning-based data fusion approach to deal with asynchronous measurements from the wireless infrastructure Ultra-Wideband (UWB) and the target's onboard Inertial Measurement Unit (IMU) sensor to achieve enhanced positioning accuracy of the target AGV. In particular, a two-stage cascaded Deep Neural Network (DNN) is proposed to deal with the asynchronized measurements from UWB and IMU sensors. The first stage of the DNN is used to obtain the initial position estimate of the AGV by processing the measurements from UWB. Subsequently, the second stage of the DNN fuses the initial position estimate of the AGV with the IMU sensor data to obtain the final enhanced position estimate. The proposed approach is validated with real-world experiments in an indoor industrial scenario using UWB technology in channel 2 (3.7-4.2 GHz) and an IMU sensor placed on an AGV. Moreover, the achievable positioning accuracy and the computational runtime to provide the position estimates with the proposed approach are analyzed. The experimental results show that the proposed approach achieves a mean absolute error of less than 10 cm, outperforming the considered baseline methods, Extended Kalman Filter (EKF) and Long Short-Term Memory (LSTM).
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-05-05
2025
2025-05-22
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/430121
https://dx.doi.org/10.1109/OJVT.2025.3566888
url https://hdl.handle.net/2117/430121
https://dx.doi.org/10.1109/OJVT.2025.3566888
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2023-146378NB-I00 CONSIGUIENDO INTERCONEXION BASADA EN IP EN MULTIPLES ENTORNOS
European Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 956670 Industrial Doctorate Training Network on Future Wireless Connected and Automated Industry enabled by 5G
Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2022-138648OB-I00 COMUNICACIONES 6G Y SENSADO PARA REDES INALAMBRICAS DETERMINISTAS
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Deep learning-based UWB-IMU data fusion for indoor positioning in industrial scenarioMuthineni, Karthik|||0000-0002-8827-2013Artemenko, AlexanderVidal Manzano, José|||0000-0002-1985-2065Nájar Martón, Montserrat|||0000-0003-3507-5689Catalán Cid, MarisaParadells Aspas, Josep|||0000-0003-4185-2202Autonomous transportation systemsData synchronizationDeep neural networksIndustriesInertial measurement unitsUltra-widebandWireless infrastructure-based positioningÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadorsÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàticAccurate and precise wireless infrastructure-based positioning systems become crucial as industries move towards flexible, portable, and autonomous transportation systems such as Automated Guided Vehicles (AGVs). Multipath-dominant dynamic environments like industries present significant challenges for wireless signal propagation and affect wireless positioning accuracy due to the interplay of reflected signals from obstacles. The achievable indoor positioning accuracy of the target AGV can be enhanced by fusing the measurements from the wireless infrastructure with the target's onboard sensor data. Nevertheless, the lack of correspondence between the wireless infrastructure and the target's onboard sensors causes the measurements from these two systems to arrive at irregular time steps. Using asynchronous measurements in the data fusion process can degrade the overall positioning accuracy of the target AGV. This paper proposes a novel deep learning-based data fusion approach to deal with asynchronous measurements from the wireless infrastructure Ultra-Wideband (UWB) and the target's onboard Inertial Measurement Unit (IMU) sensor to achieve enhanced positioning accuracy of the target AGV. In particular, a two-stage cascaded Deep Neural Network (DNN) is proposed to deal with the asynchronized measurements from UWB and IMU sensors. The first stage of the DNN is used to obtain the initial position estimate of the AGV by processing the measurements from UWB. Subsequently, the second stage of the DNN fuses the initial position estimate of the AGV with the IMU sensor data to obtain the final enhanced position estimate. The proposed approach is validated with real-world experiments in an indoor industrial scenario using UWB technology in channel 2 (3.7-4.2 GHz) and an IMU sensor placed on an AGV. Moreover, the achievable positioning accuracy and the computational runtime to provide the position estimates with the proposed approach are analyzed. The experimental results show that the proposed approach achieves a mean absolute error of less than 10 cm, outperforming the considered baseline methods, Extended Kalman Filter (EKF) and Long Short-Term Memory (LSTM).The work of Josep Paradells was supported in part by the Spanish MCIU/AEI/10.13039/501100011033/FEDER/UE through Project PID2023-146378NB-I00 and in part by the Secretaria d’Universitats i Recerca del departament d’Empresa i Coneixement de la Generalitat de Catalunya under Grant 2021 SGR 00330. This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme through the Marie Sklodowska-Curie under Grant 956670, in part by Project 6-SENSES under Grant PID2022-138648OB-I00 funded by MCIN/AEI/10.13039/501100011033, and in part by ERDF A way of making Europe.Peer Reviewed20252025-05-0520252025-05-22journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/430121https://dx.doi.org/10.1109/OJVT.2025.3566888reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2023-146378NB-I00 CONSIGUIENDO INTERCONEXION BASADA EN IP EN MULTIPLES ENTORNOSEuropean Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 956670 Industrial Doctorate Training Network on Future Wireless Connected and Automated Industry enabled by 5GAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2022-138648OB-I00 COMUNICACIONES 6G Y SENSADO PARA REDES INALAMBRICAS DETERMINISTASopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4301212026-05-27T15:37:01Z
score 15.812429

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