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An advanced method for pedestrian dead reckoning using BLSTM-RNNs

  • Location estimation and navigation, especially on smartphones has shown great progress in the past decade due to its low cost and ability to work without additional infrastructure. However, a challenge is the positioning, both in terms of step detection, step length approximation as well as heading estimation, which must be accurate and robust, even when the use of the device is varied in terms of placement or orientation. In this paper, we propose a scheme for retrieving relevant information to detect steps and to estimate the correct step length from raw inertial measurement unit (IMU) data. This approach uses Bidirectional Long Short-Term Memory Recurrent Neural Networks (BLSTM-RNNs). Designed to take contextual information into account, the network can process data gathered from different positions, resulting in a system, which is invariant with respect to transformation and distortions of the input patterns. An experimental evaluation on a dataset produced from 10 individualsLocation estimation and navigation, especially on smartphones has shown great progress in the past decade due to its low cost and ability to work without additional infrastructure. However, a challenge is the positioning, both in terms of step detection, step length approximation as well as heading estimation, which must be accurate and robust, even when the use of the device is varied in terms of placement or orientation. In this paper, we propose a scheme for retrieving relevant information to detect steps and to estimate the correct step length from raw inertial measurement unit (IMU) data. This approach uses Bidirectional Long Short-Term Memory Recurrent Neural Networks (BLSTM-RNNs). Designed to take contextual information into account, the network can process data gathered from different positions, resulting in a system, which is invariant with respect to transformation and distortions of the input patterns. An experimental evaluation on a dataset produced from 10 individuals demonstrates that this new approach achieves significant improvements over previous attempts and increase the current state-of-the-art results even in the presence of variations and degradations. We achieved a mean classification rate of 98.5% and a standard deviation of 0.70 for 10000 different test sequences and an average error of 1.45% regarding the step length. Thus is the best result on the task gathered in the experiments compared with competing techniques.zeige mehrzeige weniger

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Metadaten
Autor*innen:M. Edel, Enrico Köppe
Dokumenttyp:Beitrag zu einem Tagungsband
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):IPIN 2015 - 6th International conference on indoor positioning and indoor navigation (USB proceedings)
Jahr der Erstveröffentlichung:2015
Herausgeber (Institution):Institute of Electrical and Electronics Engineers (IEEE)
Erste Seite:Paper 1570149259, 1
Letzte Seite:6
Freie Schlagwörter:Dead reckoning; Deep learning; Indoor positioning; Inertial tracking; Machine learning
Veranstaltung:IPIN 2015 - 6th International conference on indoor positioning and indoor navigation
Veranstaltungsort:Banff, Alberta, Canada
Beginndatum der Veranstaltung:13.10.2015
Enddatum der Veranstaltung:16.10.2015
DOI:10.1109/IPIN.2015.7346954
ISSN:2162-7347
ISBN:978-1-4673-8402-5
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:20.02.2016
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:05.08.2016
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