Three Symmetries for Data-Driven Pedestrian Inertial Navigation
Author:
Affiliation:
1. Department of Computer Science, University of Exeter, Exeter, U.K.
2. Delft Center for Systems and Control, Delft University of Technology, Delft, CD, The Netherlands
Funder
Sensor Fusion for Indoor Localization Using the Magnetic Field under Project 18213 of the Research Program Veni
Dutch Research Council [Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO)]
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/9733818/09709566.pdf?arnumber=9709566
Reference28 articles.
1. FootSLAM meets Adaptive Thresholding
2. Preintegrated IMU features for efficient deep inertial odometry;khorrambakht;arXiv 2007 02929,2020
3. A Bidirectional LSTM for Estimating Dynamic Human Velocities from a Single IMU
4. StepNet—Deep Learning Approaches for Step Length Estimation
5. Deep Neural Network Based Inertial Odometry Using Low-Cost Inertial Measurement Units
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