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Localisation autonome par apprentissage des dynamiques de déplacement en transport multimodal

Abstract : The growing development of smart objects offers new opportunities for locating the connected traveler. However, tracking the pedestrian's trajectory remains problematic and navigation applications do not propose to track the traveller's trajectory on a multimodal scale autonomously. This work focuses on the implementation of a single solution capable of locating the user according to different travel modes and whatever the environment, using inertial,magnetic and GNSS sensors. In a first step, a new method for locating the cyclist is implemented. GNSS phase measurements are used to correct the velocity vector by time differences and themovement direction is constrained using inertial signals. These elements were used in a second time and adapted to implement a new method of pedestrian localization with a handheld sensor. The PDR approach which is an inertial navigation technique using dead reckoning is parameterized in an extended Kalman filter. An innovative update merging the device attitude estimation and a statistical estimation of the walking direction allows to correct the walking direction prediction and to obtain a consistent and smoothed estimate. GNSS measurements are used to correct the velocity vector, orientation, step length and absolute position. Finally, a multimodal approach is proposed and the management of transitions between the different algorithms, assisted by the use of an innovative sensor, is studied. Multimodal experiments in real conditions are conducted to analyze the performance of the proposed solution.
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Submitted on : Friday, January 7, 2022 - 5:29:13 PM
Last modification on : Thursday, June 16, 2022 - 5:43:04 PM


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  • HAL Id : tel-03152114, version 4



Johan Perul. Localisation autonome par apprentissage des dynamiques de déplacement en transport multimodal. Sciences de l'ingénieur [physics]. Ecole centrale de Nantes, 2020. Français. ⟨NNT : ⟩. ⟨tel-03152114v4⟩



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