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Open AccessJournal ArticleDOI

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.
Abstract
. Nowadays the modern smartphones include several sensors which are usually adopted in geomatic application, as digital camera, GNSS (Global Navigation Satellite System) receivers, inertial platform, RFID and Wi-Fi systems. In this paper the authors would like to testing the performances of internal sensors (Inertial Measurement Unit, IMU) 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. Moreover, the Image Based Navigation (IBN) approach is also investigated: this approach can be very useful in hard-urban environment or for indoor positioning, as alternative to GNSS positioning. IBN allows to obtain a sub-metrical accuracy, but a special database of georeferenced images (Image DataBase, IDB) is needed, moreover it is necessary to use dedicated algorithm to resizing the images which are collected by smartphone, in order to share it with the server where is stored the IDB. Moreover, it is necessary to characterize smartphone camera lens in terms of focal length and lens distortions. The authors have developed an innovative method with respect to those available today, which has been tested in a covered area, adopting a special support where all sensors under testing have been installed. Geomatic instrument have been used to define the reference trajectory, with purpose to compare this one, with the path obtained with IBN solution. First results leads to have an horizontal and vertical accuracies better than 60 cm, respect to the reference trajectories. IBN method, sensors, test and result will be described in the paper.

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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

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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Book

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

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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