Sensors integration for smartphone navigation: performances and future challenges
TLDR
In this paper, the authors compared the performance of three modern smartphones (Samsung GalaxyS4, Samsung GalaxyS5 and iPhone4) compared to external mass-market IMU platform in order to verify their accuracy levels in terms of positioning.Citations
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Journal ArticleDOI
Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons.
TL;DR: An algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel- separation fingerprinting (FP), outlier detection and extended Kalman filtering (EKF) for smartphone-based indoor localization with BLE beacons is proposed.
Journal ArticleDOI
Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary
TL;DR: In this paper, the authors summarized the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone.
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Smartphone-based Vehicle Telematics - A Ten-Year Anniversary
TL;DR: This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone.
Journal ArticleDOI
Detailed geological mapping in mountain areas using an unmanned aerial vehicle: application to the Rodoretto Valley, NW Italian Alps
Marco Piras,Glenda Taddia,Maria Gabriella Forno,Marco Gattiglio,Irene Aicardi,Paolo Dabove,Stefano Lo Russo,Alberto Lingua +7 more
TL;DR: In this paper, the authors presented a methodology to use a UAV (unmanned aerial vehicle) to perform photogrammetric surveys and detailed geological mapping in mountain areas.
Proceedings ArticleDOI
Learning to Fuse: A Deep Learning Approach to Visual-Inertial Camera Pose Estimation
TL;DR: This work presents a novel approach to sensor fusion using a deep learning method to learn the relation between camera poses and inertial sensor measurements and results confirm the applicability and tracking performance improvement gained from the proposed sensor fusion system.
References
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Robust Regression and Outlier Detection
TL;DR: This paper presents the results of a two-year study of the statistical treatment of outliers in the context of one-Dimensional Location and its applications to discrete-time reinforcement learning.
An introduction to inertial navigation
TL;DR: This work introduces inertial navigation, focusing on strapdown systems based on MEMS devices, and concludes that whilst MEMS IMU technology is rapidly improving, it is not yet possible to build a MEMS based INS which gives sub-meter position accuracy for more than one minute of operation.
Proceedings ArticleDOI
Pedestrian localisation for indoor environments
Oliver J. Woodman,Robert Harle +1 more
TL;DR: This paper looks at how a foot-mounted inertial unit, a detailed building model, and a particle filter can be combined to provide absolute positioning, despite the presence of drift in the inertial units and without knowledge of the user's initial location.
Proceedings ArticleDOI
A robust dead-reckoning pedestrian tracking system with low cost sensors
TL;DR: A robust DR pedestrian tracking system on top of such commercially accessible sensor sets capable of DR, exploiting the fact that, multiple DR systems, carried by the same pedestrian, have stable relative displacements with respect to the center of motion, and therefore to each other.
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Rigorous Performance Evaluation of Smartphone GNSS/IMU Sensors for ITS Applications
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