TrackInFactory: A Tight Coupling Particle Filter for Industrial Vehicle Tracking in Indoor Environments
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Título
TrackInFactory: A Tight Coupling Particle Filter for Industrial Vehicle Tracking in Indoor EnvironmentsFecha de publicación
2021-07-06Editor
Institute of Electrical and Electronics Engineers; IEEEISSN
2168-2216; 2168-2232Cita bibliográfica
I. Silva, C. Pendão, J. Torres-Sospedra and A. Moreira, "TrackInFactory: A Tight Coupling Particle Filter for Industrial Vehicle Tracking in Indoor Environments," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, doi: 10.1109/TSMC.2021.3091987.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221021Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
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Resumen
Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements of industrial vehicle tracking and ... [+]
Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements of industrial vehicle tracking and navigation, existing systems explore technologies, such as LiDAR or ultra wide-band to achieve low positioning errors. In this article we propose TrackInFactory, a system that combines Wi-Fi with motion sensors, achieving submeter accuracy and a low maximum error. A tight coupling approach is explored in sensor fusion with a particle filter (PF). Information regarding the vehicle's initial position and heading is not required. This approach uses the similarity of Wi-Fi samples to update the particles' weights as they move according to motion sensor data. The PF dynamically adjusts its parameters based on a metric for estimating the confidence in position estimates, allowing to improve positioning performance. A series of simulations were performed to tune the PF. Then the approach was validated in real-world experiments with an industrial tow tractor, achieving a mean error of 0.81 m. In comparison to a loose coupling approach, this method reduced the maximum error by more than 60% and improved the overall mean error by more than 20%. [-]
Entidad financiadora
Fundação para a Ciência e Tecnologia | Ministerio de Ciencia, Innovación y Universidades (Spain)
Código del proyecto o subvención
UIDB/00319/2020 | PD/BD/137401/2018 | PTQ2018-009981
Derechos de acceso
info:eu-repo/semantics/openAccess
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- INIT_Articles [752]