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Prototyping a Context-Aware Framework for Pervasive Entertainment Applications
Indoor Localization Solutions for a Marine Industry Augmented Reality Tool
In this report are described means for indoor localization in special, challenging circum-stances in marine industry. The work has been carried out in MARIN project, where a tool based on mobile augmented reality technologies for marine industry is developed. The tool can be used for various inspection and documentation tasks and it is aimed for improving the efficiency in design and construction work by offering the possibility to visualize the newest 3D-CAD model in real environment. Indoor localization is needed to support the system in initialization of the accurate camera pose calculation and auto-matically finding the right location in the 3D-CAD model. The suitability of each indoor localization method to the specific environment and circumstances is evaluated.Siirretty Doriast
Human Crowdsourcing Data for Indoor Location Applied to Ambient Assisted Living Scenarios
In the last decades, the rise of life expectancy has accelerated the demand for new technological
solutions to provide a longer life with improved quality. One of the major areas
of the Ambient Assisted Living aims to monitor the elderly location indoors. For this purpose,
indoor positioning systems are valuable tools and can be classified depending on the
need of a supporting infrastructure. Infrastructure-based systems require the investment
on expensive equipment and existing infrastructure-free systems, although rely on the
pervasively available characteristics of the buildings, present some limitations regarding
the extensive process of acquiring and maintaining fingerprints, the maps that store the
environmental characteristics to be used in the localisation phase. These problems hinder
indoor positioning systems to be deployed in most scenarios.
To overcome these limitations, an algorithm for the automatic construction of indoor
floor plans and environmental fingerprints is proposed. With the use of crowdsourcing
techniques, where the extensiveness of a task is reduced with the help of a large undefined
group of users, the algorithm relies on the combination ofmultiple sources of information,
collected in a non-annotated way by common smartphones. The crowdsourced data is
composed by inertial sensors, responsible for estimating the users’ trajectories, Wi-Fi
radio and magnetic field signals. Wi-Fi radio data is used to cluster the trajectories into
smaller groups, each corresponding to specific areas of the building. Distance metrics
applied to magnetic field signals are used to identify geomagnetic similarities between
different users’ trajectories. The building’s floor plan is then automatically created, which
results in fingerprints labelled with physical locations.
Experimental results show that the proposed algorithm achieved comparable floor
plan and fingerprints to those acquired manually, allowing the conclusion that is possible
to automate the setup process of infrastructure-free systems. With these results, this
solution can be applied in any fingerprinting-based indoor positioning system
Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping
The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability
Estimating Movement from Mobile Telephony Data
Mobile enabled devices are ubiquitous in modern society. The information gathered by
their normal service operations has become one of the primary data sources used in the
understanding of human mobility, social connection and information transfer. This thesis
investigates techniques that can extract useful information from anonymised call detail records
(CDR). CDR consist of mobile subscriber data related to people in connection with the network
operators, the nature of their communication activity (voice, SMS, data, etc.), duration of the
activity and starting time of the activity and servicing cell identification numbers of both the
sender and the receiver when available.
The main contributions of the research are a methodology for distance measurements
which enables the identification of mobile subscriber travel paths and a methodology for
population density estimation based on significant mobile subscriber regions of interest. In
addition, insights are given into how a mobile network operator may use geographically located
subscriber data to create new revenue streams and improved network performance. A range of
novel algorithms and techniques underpin the development of these methodologies. These
include, among others, techniques for CDR feature extraction, data visualisation and CDR data
cleansing.
The primary data source used in this body of work was the CDR of Meteor, a mobile
network operator in the Republic of Ireland. The Meteor network under investigation has just
over 1 million customers, which represents approximately a quarter of the country’s 4.6 million
inhabitants, and operates using both 2G and 3G cellular telephony technologies.
Results show that the steady state vector analysis of modified Markov chain mobility
models can return population density estimates comparable to population estimates obtained
through a census. Evaluated using a test dataset, results of travel path identification showed
that developed distance measurements achieved greater accuracy when classifying the routes
CDR journey trajectories took compared to traditional trajectory distance measurements.
Results from subscriber segmentation indicate that subscribers who have perceived similar
relationships to geographical features can be grouped based on weighted steady state mobility
vectors. Overall, this thesis proposes novel algorithms and techniques for the estimation of
movement from mobile telephony data addressing practical issues related to sampling, privacy
and spatial uncertainty
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