1,665 research outputs found
Sistema de localização automática de dispositivos móveis com recurso à sequências perfeitas de rádio frequência
Recent advancements in the area of nanotechnology have brought us into a new age of pervasive computing devices. These computing devices grow ever smaller and are being used in ways which were unimaginable before. Recent interest in developing a precise indoor positioning system, as opposed to existing outdoor systems, has given way to much research heading into the area. The use of these small computing devices offers many conveniences for usage in indoor positioning systems. This thesis will deal with using small computing devices Raspberry Pi’s to enable and improve position estimation of mobile devices within closed spaces. The newly patented Orthogonal Perfect DFT Golay coding sequences will be used inside this scenario, and their positioning properties will be tested. After that, testing and comparisons with other coding sequences will be done
Reliable localization methods for intelligent vehicles based on environment perception
Mención Internacional en el título de doctorIn the near past, we would see autonomous vehicles and Intelligent Transport
Systems (ITS) as a potential future of transportation. Today, thanks to all the
technological advances in recent years, the feasibility of such systems is no longer a
question. Some of these autonomous driving technologies are already sharing our
roads, and even commercial vehicles are including more Advanced Driver-Assistance
Systems (ADAS) over the years. As a result, transportation is becoming more efficient
and the roads are considerably safer.
One of the fundamental pillars of an autonomous system is self-localization. An
accurate and reliable estimation of the vehicle’s pose in the world is essential to
navigation. Within the context of outdoor vehicles, the Global Navigation Satellite
System (GNSS) is the predominant localization system. However, these systems are
far from perfect, and their performance is degraded in environments with limited
satellite visibility. Additionally, their dependence on the environment can make them
unreliable if it were to change.
Accordingly, the goal of this thesis is to exploit the perception of the environment
to enhance localization systems in intelligent vehicles, with special attention to
their reliability. To this end, this thesis presents several contributions: First, a study
on exploiting 3D semantic information in LiDAR odometry is presented, providing
interesting insights regarding the contribution to the odometry output of each type
of element in the scene. The experimental results have been obtained using a public
dataset and validated on a real-world platform. Second, a method to estimate the
localization error using landmark detections is proposed, which is later on exploited
by a landmark placement optimization algorithm. This method, which has been
validated in a simulation environment, is able to determine a set of landmarks
so the localization error never exceeds a predefined limit. Finally, a cooperative
localization algorithm based on a Genetic Particle Filter is proposed to utilize vehicle
detections in order to enhance the estimation provided by GNSS systems. Multiple
experiments are carried out in different simulation environments to validate the
proposed method.En un pasado no muy lejano, los vehículos autónomos y los Sistemas Inteligentes
del Transporte (ITS) se veían como un futuro para el transporte con gran potencial.
Hoy, gracias a todos los avances tecnológicos de los últimos años, la viabilidad
de estos sistemas ha dejado de ser una incógnita. Algunas de estas tecnologías
de conducción autónoma ya están compartiendo nuestras carreteras, e incluso los
vehículos comerciales cada vez incluyen más Sistemas Avanzados de Asistencia a la
Conducción (ADAS) con el paso de los años. Como resultado, el transporte es cada
vez más eficiente y las carreteras son considerablemente más seguras.
Uno de los pilares fundamentales de un sistema autónomo es la autolocalización.
Una estimación precisa y fiable de la posición del vehículo en el mundo es esencial
para la navegación. En el contexto de los vehículos circulando en exteriores, el
Sistema Global de Navegación por Satélite (GNSS) es el sistema de localización predominante.
Sin embargo, estos sistemas están lejos de ser perfectos, y su rendimiento
se degrada en entornos donde la visibilidad de los satélites es limitada. Además, los
cambios en el entorno pueden provocar cambios en la estimación, lo que los hace
poco fiables en ciertas situaciones.
Por ello, el objetivo de esta tesis es utilizar la percepción del entorno para mejorar
los sistemas de localización en vehículos inteligentes, con una especial atención a
la fiabilidad de estos sistemas. Para ello, esta tesis presenta varias aportaciones:
En primer lugar, se presenta un estudio sobre cómo aprovechar la información
semántica 3D en la odometría LiDAR, generando una base de conocimiento sobre la
contribución de cada tipo de elemento del entorno a la salida de la odometría. Los
resultados experimentales se han obtenido utilizando una base de datos pública y se
han validado en una plataforma de conducción del mundo real. En segundo lugar,
se propone un método para estimar el error de localización utilizando detecciones
de puntos de referencia, que posteriormente es explotado por un algoritmo de
optimización de posicionamiento de puntos de referencia. Este método, que ha
sido validado en un entorno de simulación, es capaz de determinar un conjunto de
puntos de referencia para el cual el error de localización nunca supere un límite
previamente fijado. Por último, se propone un algoritmo de localización cooperativa
basado en un Filtro Genético de Partículas para utilizar las detecciones de vehículos
con el fin de mejorar la estimación proporcionada por los sistemas GNSS. El método
propuesto ha sido validado mediante múltiples experimentos en diferentes entornos
de simulación.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridSecretario: Joshué Manuel Pérez Rastelli.- Secretario: Jorge Villagrá Serrano.- Vocal: Enrique David Martí Muño
Innovative Wireless Localization Techniques and Applications
Innovative methodologies for the wireless localization of users and related applications
are addressed in this thesis.
In last years, the widespread diffusion of pervasive wireless communication
(e.g., Wi-Fi) and global localization services (e.g., GPS) has boosted the interest
and the research on location information and services. Location-aware
applications are becoming fundamental to a growing number of consumers (e.g.,
navigation, advertising, seamless user interaction with smart places), private and
public institutions in the fields of energy efficiency, security, safety,
fleet management, emergency response. In this context, the position of the user - where
is often more valuable for deploying services of interest than the identity of the
user itself - who.
In detail, opportunistic approaches based on the analysis of electromagnetic
field indicators (i.e., received signal strength and channel state information) for
the presence detection, the localization, the tracking and the posture recognition
of cooperative and non-cooperative (device-free) users in indoor environments are
proposed and validated in real world test sites. The methodologies are designed
to exploit existing wireless infrastructures and commodity devices without any
hardware modification.
In outdoor environments, global positioning technologies are already available
in commodity devices and vehicles, the research and knowledge transfer
activities are actually focused on the design and validation of algorithms and
systems devoted to support decision makers and operators for increasing efficiency,
operations security, and management of large fleets as well as localized
sensed information in order to gain situation awareness. In this field, a decision
support system for emergency response and Civil Defense assets management
(i.e., personnel and vehicles equipped with TETRA mobile radio) is described in
terms of architecture and results of two-years of experimental validation
A Novel Approach To Intelligent Navigation Of A Mobile Robot In A Dynamic And Cluttered Indoor Environment
The need and rationale for improved solutions to indoor robot navigation is increasingly driven by the influx of domestic and industrial mobile robots into the market. This research has developed and implemented a novel navigation technique for a mobile robot operating in a cluttered and dynamic indoor environment. It divides the indoor navigation problem into three distinct but interrelated parts, namely, localization, mapping and path planning. The localization part has been addressed using dead-reckoning (odometry). A least squares numerical approach has been used to calibrate the odometer parameters to minimize the effect of systematic errors on the performance, and an intermittent resetting technique, which employs RFID tags placed at known locations in the indoor environment in conjunction with door-markers, has been developed and implemented to mitigate the errors remaining after the calibration. A mapping technique that employs a laser measurement sensor as the main exteroceptive sensor has been developed and implemented for building a binary occupancy grid map of the environment. A-r-Star pathfinder, a new path planning algorithm that is capable of high performance both in cluttered and sparse environments, has been developed and implemented. Its properties, challenges, and solutions to those challenges have also been highlighted in this research. An incremental version of the A-r-Star has been developed to handle dynamic environments. Simulation experiments highlighting properties and performance of the individual components have been developed and executed using MATLAB. A prototype world has been built using the WebotsTM robotic prototyping and 3-D simulation software. An integrated version of the system comprising the localization, mapping and path planning techniques has been executed in this prototype workspace to produce validation results
Wireless Sensor Localization: Error Modeling and Analysis for Evaluation and Precision
Wireless sensor networks (WSNs) have shown promise in a broad range of applications. One of the primary challenges in leveraging WSNs lies in gathering accurate position information for the deployed sensors while minimizing power cost. In this research, detailed background research is discussed regarding existing methods and assumptions of modeling methods and processes for estimating sensor positions. Several novel localization methods are developed by applying rigorous mathematical and statistical principles, which exploit constraining properties of the physical problem in order to produce improved location estimates. These methods are suitable for one-, two-, and three-dimensional position estimation in ascending order of difficulty and complexity. Unlike many previously existing methods, the techniques presented in this dissertation utilize practical, realistic assumptions and are progressively designed to mitigate incrementally discovered limitations. The design and results of a developed multiple-layered simulation environment are also presented that model and characterize the developed methods. The approach, developed methodologies, and software infrastructure presented in this dissertation provide a framework for future endeavors within the field of wireless sensor networks
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