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Unsupervised pedestrian trajectory reconstruction from IMU sensors

Stéphane Derrode 1 Haoyu Li 1, * Lamia Benyoussef 2 Wojciech Pieczynski 3, 4
* Corresponding author
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
3 TIPIC-SAMOVAR - Traitement de l'Information Pour Images et Communications
SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux
Abstract : This paper presents a pedestrian navigation algorithm based on a foot-mounted 9DOF Inertial Measurement Unit, which provides accelerations, angular rates and magnet-ics along 3-axis during the motion. Most of algorithms used worldwide are based on stance detection to reduce the tremendous integration errors, from acceleration to displacement. As the crucial part is to detect stance phase precisely, we introduced a cyclic left-to-right style Hidden Markov Model that is able to appropriately model the periodic nature of signals. Stance detection is then made unsupervised by using a suited learning algorithm. Then, assisted by a simplified error-state Kalman filter, trajectory can be reconstructed. Experimental results show that the proposed algorithm can provide more accurate location, compared to competitive algorithms, w.r.t. ground-truth obtained from OpenStreet Map.
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Contributor : Stéphane Derrode <>
Submitted on : Saturday, May 5, 2018 - 10:05:32 AM
Last modification on : Thursday, December 19, 2019 - 1:30:14 AM
Document(s) archivé(s) le : Tuesday, September 25, 2018 - 8:59:09 PM


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  • HAL Id : hal-01786223, version 1


Stéphane Derrode, Haoyu Li, Lamia Benyoussef, Wojciech Pieczynski. Unsupervised pedestrian trajectory reconstruction from IMU sensors. TAIMA 2018: Traitement et Analyse de l'Information Méthodes et Applications, Apr 2018, Hammamet, Tunisia. ⟨hal-01786223⟩



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