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Dissertations / Theses on the topic 'UAV Localization'
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Peterson, John Ryan. "Autonomous Source Localization." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/97954.
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This work discusses the algorithms and implementation of a multi-robot system for locating radioactive sources. The estimation algorithm presented in this work is able to fuse measurements collected by γ-ray spectrometers carried by an unmanned aerial and unmanned ground vehicle into a single consistent estimate of the probability distribution over the position of a point source in an environment. By constructing a set of hypotheses on the position of the point source, this method converts a non-linear problem into many independent linear ones. Since the underlying model is probabilistic, candidate paths may be evaluated by their expected reduction in uncertainty, allowing the algorithm to select good paths for vehicles to take. An initial hardware test conducted at Savannah River National Laboratory served as a proof of concept and demonstrated that the algorithm successfully locates a radioactive source in the environment, and moves the vehicle to that location. This approach also demonstrated the capability to utilize radiation data collected from an unmanned aerial vehicle to aid the ground vehicle’s exploration. Subsequent numerical experiments characterized the performance of several reward functions and different exploration algorithms in scenarios covering a range of source strengths and region sizes. These experiments demonstrated the improved performance of planning-based algorithms over the myopic method initially tested in the hardware experiments.<br>Doctor of Philosophy<br>This work discusses the use of unmanned aerial and ground vehicles to autonomously locate radioactive materials. Using radiation detectors onboard each vehicle, they are commanded to search the environment using a method that incorporates measurements as they are collected. A mathematical model allows measurements taken from different vehicles in different positions to be combined together. This approach decreases the time required to locate sources by using previously collected measurements to improve the quality of later measurements. This approach also provides a best estimate of the location of a source as data is collected. This algorithm was tested in an experiment conducted at Savannah River National Laboratory. Further numerical experiments were conducted testing different reward functions and exploration algorithms.
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Gilson, Maximillian Andrew. "Fault-tolerant mapping and localization for Quadrotor UAV." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright157865858408435.
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Barac, Daniel. "Localization algorithms for indoor UAVs." Thesis, Linköpings universitet, Reglerteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72217.
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The increased market for navigation, localization and mapping system has encouraged the research to dig deeper into these new and challenging areas. The remarkable development of computer soft- and hardware have also opened up many new doors. Things which more or less where impossible ten years ago are now reality. The possibilities of using a mathematical approach to compensate for the need of expensive sensors has been one of the main objectives in this thesis. Here you will find the basic principles of localization of indoor UAVs using particle filter (PF) and Octomaps, but also the procedures of implementing 2D scanmatching algorithms and quaternions. The performance of the algorithms is evaluated using a high precision motion capture system. The UAV which forms the basis for this thesis is equipped with a 2D laser and an inertial measurement unit (IMU). The results show that it is possible to perform localization in 2D with centimetre precision only by using information from a laser and a predefined Octomap.
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Brewer, Eric Thomas. "Autonomous Localization of 1/R^2 Sources Using an Aerial Platform." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/36378.
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Unmanned vehicles are often used in time-critical missions such as reconnaissance or
search and rescue. To this end, this thesis provides autonomous localization and mapping
tools for 1=R2 sources. A \1=R2" source is one in which the received intensity of the source
is inversely proportional to the square of the distance from the source. An autonomous
localization algorithm is developed which utilizes a particle swarm particle ltering method
to recursively estimate the location of a source.
To implement the localization algorithm experimentally, a command interface with
Virginia Tech's autonomous helicopter was developed. The interface accepts state informa-
tion from the helicopter, and returns command inputs to drive the helicopter autonomously
to the source. To make the use of the system more intuitive, a graphical user interface was
developed which provides localization functionality as well as a waypoint navigation outer-
loop controller for the helicopter. This assists in positioning the helicopter and returning it
home after the the algorithm is completed.
An autonomous mapping mission with a radioactive source is presented, along with a
localization experiment utilizing simulated sensor readings.
This work is the rst phase of an on-going project at the Unmanned Systems Lab.
Accordingly, this thesis also provides a framework for its continuation in the next phase of
the project.<br>Master of Science
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Redding, Joshua D. "Vision-based Target Localization from a Small, Fixed-wing Unmanned Air Vehicle." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd895.pdf.
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Christie, Gordon A. "Collaborative Unmanned Air and Ground Vehicle Perception for Scene Understanding, Planning and GPS-denied Localization." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/83807.
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Autonomous robot missions in unknown environments are challenging. In many cases, the systems involved are unable to use a priori information about the scene (e.g. road maps). This is especially true in disaster response scenarios, where existing maps are now out of date. Areas without GPS are another concern, especially when the involved systems are tasked with navigating a path planned by a remote base station. Scene understanding via robots' perception data (e.g. images) can greatly assist in overcoming these challenges. This dissertation makes three contributions that help overcome these challenges, where there is a focus on the application of autonomously searching for radiation sources with unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) in unknown and unstructured environments. The three main contributions of this dissertation are: (1) An approach to overcome the challenges associated with simultaneously trying to understand 2D and 3D information about the environment. (2) Algorithms and experiments involving scene understanding for real-world autonomous search tasks. The experiments involve a UAV and a UGV searching for potentially hazardous sources of radiation is an unknown environment. (3) An approach to the registration of a UGV in areas without GPS using 2D image data and 3D data, where localization is performed in an overhead map generated from imagery captured in the air.<br>Ph. D.
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Acuna, Virgilio. "Using Unmanned Aerial Vehicles for Wireless Localization in Search and Rescue." FIU Digital Commons, 2017. https://digitalcommons.fiu.edu/etd/3646.
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This thesis presents how unmanned aerial vehicles (UAVs) can successfully assist in search and rescue (SAR) operations using wireless localization. The zone-grid to partition to capture/detect WiFi probe requests follows the concepts found in Search Theory Method. The UAV has attached a sensor, e.g., WiFi sniffer, to capture/detect the WiFi probes from victims or lost people’s smartphones. Applying the Random-Forest based machine learning algorithm, an estimation of the user's location is determined with a 81.8% accuracy.
UAV technology has shown limitations in the navigational performance and limited flight time. Procedures to optimize these limitations are presented. Additionally, how the UAV is maneuvered during flight is analyzed, considering different SAR flight patterns and Li-Po battery consumption rates of the UAV. Results show that controlling the UAV by remote-controll detected the most probes, but it is less power efficient compared to control it autonomously.
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Darling, Michael B. "Autonomous Close Formation Flight of Small UAVs Using Vision-Based Localization." DigitalCommons@CalPoly, 2014. https://digitalcommons.calpoly.edu/theses/1185.
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As Unmanned Aerial Vehicles (UAVs) are integrated into the national airspace to comply with the 2012 Federal Aviation Administration Reauthorization Act, new civilian uses for robotic aircraft will come about in addition to the more obvious military applications. One particular area of interest for UAV development is the autonomous cooperative control of multiple UAVs. In this thesis, a decentralized leader-follower control strategy is designed, implemented, and tested from the follower’s perspective using vision-based localization.
The tasks of localization and control were carried out with separate processing hardware dedicated to each task. First, software was written to estimate the relative state of a lead UAV in real-time from video captured by a camera on-board the following UAV. The software, written using OpenCV computer vision libraries and executed on an embedded single-board computer, uses the Efficient Perspective-n-Point algorithm to compute the 3-D pose from a set of 2-D image points. High-intensity, red, light emitting diodes (LEDs) were affixed to specific locations on the lead aircraft’s airframe to simplify the task if extracting the 2-D image points from video. Next, the following vehicle was controlled by modifying a commercially available, open source, waypoint-guided autopilot to navigate using the relative state vector provided by the vision software. A custom Hardware-In-Loop (HIL) simulation station was set up and used to derive the required localization update rate for various flight patterns and levels of atmospheric turbulence. HIL simulation showed that it should be possible to maintain formation, with a vehicle separation of 50 ± 6 feet and localization estimates updated at 10 Hz, for a range of flight conditions. Finally, the system was implemented into low-cost remote controlled aircraft and flight tested to demonstrate formation convergence to 65.5 ± 15 feet of separation.
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Suzdalev, Ivan. "Artificial neural networks to updrafts localization and forecasting." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2013. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2013~D_20130308_165902-64063.
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The dissertation examines the thermal flow detection and prediction prob-lems during an autonomous aircraft flight. The main research object is the thermal flows and artificial neural networks. Thermal flows are a very im-portant source for improving autonomous aircraft flight parameters, such as flight time and duration.
The primary aim of the dissertation is to create methodologies and algorithms to detect, identify and to successfully predict the parameters the thermal flows. The application are of the methods and algorithms developed is autonomous aircraft control system synthesis, research on mesoscale meteorological phenomena and synthesis of computing systems using biological models.
The following objectives are carried out: thermal flow sensing using aircraft navigational parameters measurement data, thermal flow simulation modeling and data input necessary for modeling.
The dissertation consists of an introduction, four chapters, conclusions, bibliography, and list of author publications on the topic as well as three annexes.
The introductory chapter discusses the research problem and the relevance of the research described in the thesis, formulates the goal and objectives, describes the research methodology, scientific novelty, the practical significance of the results, hypotheses. In the end of the introduction a list of author's publications on the topic and the structure of the dissertation are presented.
The first section provides a review of previous... [to full text]<br>Disertacijoje nagrinėjamos terminių srautų paieškos ir prognozavimo autonominio orlaivio skrydžio metu problemos. Pagrindinis tyrimų objektas yra terminių srautų aparatinis aptikimas ir prognozavimas. Terminiai srautai yra labai svarbus autonominio orlaivio skrydžio charakteristikų, kaip antai skrydžio laikas ir trukmė, gerinimo šaltinis.
Pagrindinis disertacijos tikslas – sukurti metodikas ir algoritmus, leidžiančius aptikti terminį srautą, nustatyti bei sėkmingai prognozuoti jo parametrus. Sukurtų metodų ir algoritmų taikymo sritis – autonominių orlaivių valdymo sistemų sintezė, meteorologiniai mezomastelinių meteorologinių reiškinių tyrimai, biologinius skaičiavimo modelius naudojančių sistemų sintezė.
Darbe sprendžiami keli uždaviniai: terminio srauto aptikimas naudojant orlaivio navigacinių parametrų matavimo duomenis, terminio srauto modeliavimas bei modeliui reikalingų duomenų pateikimas.
Disertaciją sudaro įvadas, keturi skyriai, rezultatų apibendrinimas, naudotos literatūros ir autoriaus publikacijų disertacijos tema sąrašai ir tris priedai.
Įvadiniame skyriuje aptariama tiriamoji problema, darbo aktualumas, aprašomas tyrimų objektas, formuluojamas darbo tikslas bei uždaviniai, aprašoma tyrimų metodika, darbo mokslinis naujumas, darbo rezultatų praktinė reikšmė, ginamieji teiginiai. Įvado pabaigoje pristatomos disertacijos tema autoriaus paskelbtos publikacijos ir konferencijų pranešimai bei disertacijos struktūra.
Pirmajame skyriuje pateikiama su disertacijos... [toliau žr. visą tekstą]
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Towler, Jerry Alwynne. "Autonomous Aerial Localization of Radioactive Point Sources via Recursive Bayesian Estimation and Contour Analysis." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/43465.
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The rapid, accurate determination of the positions and strengths of sources of dangerous radioactivity takes high priority after a catastrophic event to ensure the safety of personnel, civilians, and emergency responders. This thesis presents approaches and algorithms to autonomously investigate radioactive material using an unmanned aerial vehicle.<br />
Performing this autonomous analysis comprises five major steps: ingress from a base of operations to the danger zone, initial detection of radioactive material, measurement of the strength of radioactive emissions, analysis of the data to provide position and intensity estimates, and finally egress from the area of interest back to the launch site. In all five steps, time is of critical importance: faster responses promise potentially saved lives.<br />
A time-optimal ingress and egress path planning method solves the first and last steps. Vehicle capabilities and instrument sensitivity inform the development of an efficient search path within the area of interest. Two algorithmsâ a grid-based recursive Bayesian estimator and a novel radiation contour analysis methodâ are presented to estimate the position of radioactive sources using simple gross gamma ray event count data from a nondirectional radiation detector. The latter procedure also correctly estimates the number of sources present and their intensities.<br />
Ultimately, a complete unsupervised mission is developed, requiring minimal initial operator interaction, that provides accurate characterization of the radiation environment of an area of interest as quickly as reasonably possible.<br>Master of Science
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Montecchiari, Leonardo. "Progettazione e valutazione sperimentale di una piattaforma di mobilità controllata per UAV-aided sensor networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19131/.
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Con “mobilità controllata” s’intende la tecnica attraverso la quale si utilizzano veicoli (droni/rover) sprovvisti di equipaggio ed in grado di controllare in maniera autonoma la propria posizione.
Molte applicazioni della mobilità controllata fanno riferimento a scenari nel quale il veicolo a guida autonoma funge da collettore dati raccolti da installazione di sensori a terra.
Questo elaborato presenta Sybelius, un piattaforma di mobilità controllata per UAV-aided sensor networks. È stato progettato un sistema di controllo autonomo per "Unmanned Aerial Vehicle" (UAV), in grado di eseguire delle intere "Missioni" di volo con lo scopo di raccogliere dati da sensori a terra. La sperimentazione è stata effettuata in ambiente outdoor usando un drone "manned" ed equipaggiandolo di apposito coordinatore di bordo e di modulo per l'acquisizione dati dai sensori a terra.
Sono stati eseguiti test sperimentali relativi alla direzionalità, distanza e potenza della comunicazione drone-sensore, alla precisione del puntamento verso la coordinata GPS del sensore a terra e all'efficienza energetica in fase di comunicazione, volo stazionario e volo continuo.
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Mayalu, Jr Alfred Kulua. "Beyond LiDAR for Unmanned Aerial Event-Based Localization in GPS Denied Environments." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104024.
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Finding lost persons, collecting information in disturbed communities, efficiently traversing
urban areas after a blast or similar catastrophic events have motivated researchers to develop
intelligent sensor frameworks to aid law enforcement, first responders, and military personnel
with situational awareness. This dissertation consists of a two-part framework for providing
situational awareness using both acoustic ground sensors and aerial sensing modalities.
Ground sensors in the field of data-driven detection and classification approaches typically
rely on computationally expensive inputs such as image or video-based methods [6, 91].
However, the information given by an acoustic signal offers several advantages, such as low
computational needs and possible classification of occluded events including gunshots or
explosions. Once an event is identified, responding to real-time events in urban areas is
difficult using an Unmanned Aerial Vehicle (UAV) especially when GPS is unreliable due
to coverage blackouts and/or GPS degradation [10]. Furthermore, if it is possible to deploy
multiple in-situ static intelligent acoustic autonomous sensors that can identify anomalous
sounds given context, then the sensors can communicate with an autonomous UAV that can
navigate in a GPS-denied urban environment for investigation of the event; this could offer
several advantages for time-critical and precise, localized response information necessary for
life-saving decision-making.
Thus, in order to implement a complete intelligent sensor framework, the need for both an intelligent static ground acoustic autonomous unattended sensors (AAUS) and improvements
to GPS-degraded localization has become apparent for applications such as anomaly
detection, public safety, as well as intelligence surveillance and reconnaissance (ISR) operations.
Distributed AAUS networks could provide end-users with near real-time actionable
information for large urban environments with limited resources. Complete ISR mission
profiles require a UAV to fly in GPS challenging or denied environments such as natural or
urban canyons, at least in a part of a mission.
This dissertation addresses, 1) the development of intelligent sensor framework through the
development of a static ground AAUS capable of machine learning for audio feature classification
and 2) GPS impaired localization through a formal framework for trajectory-based
flight navigation for unmanned aircraft systems (UAS) operating BVLOS in low-altitude urban
airspace. Our AAUS sensor method utilizes monophonic sound event detection in which
the sensor detects, records, and classifies each event utilizing supervised machine learning
techniques [90]. We propose a simulated framework to enhance the performance of localization
in GPS-denied environments. We do this by using a new representation of 3D geospatial
data using planar features that efficiently capture the amount of information required for
sensor-based GPS navigation in obstacle-rich environments. The results from this dissertation
would impact both military and civilian areas of research with the ability to react to
events and navigate in an urban environment.<br>Doctor of Philosophy<br>Emergency scenarios such as missing persons or catastrophic events in urban areas require
first responders to gain situational awareness motivating researchers to investigate intelligent
sensor frameworks that utilize drones for observation prompting questions such as: How
can responders detect and classify acoustic anomalies using unattended sensors? and How
do they remotely navigate in GPS-denied urban environments using drones to potentially
investigate such an event?
This dissertation addresses the first question through the development of intelligent WSN
systems that can provide time-critical and precise, localized environmental information necessary
for decision-making. At Virginia Tech, we have developed a static ground Acoustic
Autonomous Unattended Sensor (AAUS) capable of machine learning for audio feature classification.
The prior arts of intelligent AAUS and network architectures do not account
for network failure, jamming capabilities, or remote scenarios in which cellular data wifi
coverage are unavailable [78, 90]. Lacking a framework for such scenarios illuminates vulnerability
in operational integrity for proposed solutions in homeland security applications.
We address this through data ferrying, a communication method in which a mobile node,
such as a drone, physically carries data as it moves through the environment to communicate
with other sensor nodes on the ground.
When examining the second question of navigation/investigation, concerns of safety arise in
urban areas regarding drones due to GPS signal loss which is one of the first problems that can occur when a drone flies into a city (such as New York City). If this happens, potential
crashes, injury and damage to property are imminent because the drone does not know where
it is in space. In these GPS-denied situations traditional methods use point clouds (a set of
data points in space (X,Y,Z) representing a 3D object [107]) constructed from laser radar
scanners (often seen in a Microsoft Xbox Kinect sensor) to find itself. The main drawback
from using methods such as these is the accumulation of error and computational complexity
of large data-sets such as New York City. An advantage of cities is that they are largely flat;
thus, if you can represent a building with a plane instead of 10,000 points, you can greatly
reduce your data and improve algorithm performance.
This dissertation addresses both the needs of an intelligent sensor framework through the
development of a static ground AAUS capable of machine learning for audio feature classification
as well as GPS-impaired localization through a formal framework for trajectory-based
flight navigation for UAS operating BVLOS in low altitude urban and suburban environments.
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Dahlin, Alfred. "Simultaneous Localization and Mapping for an Unmanned Aerial Vehicle Using Radar and Radio Transmitters." Thesis, Linköpings universitet, Reglerteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-110645.
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The Global Positioning System (GPS) is a cornerstone in Unmanned Aerial Vehicle (UAV) navigation and is by far the most common way to obtain the position of a UAV. However, since there are many scenarios in which GPS measurements might not be available, the possibility of estimating the UAV position without using the GPS would greatly improve the overall robustness of the navigation. This thesis studies the possibility of instead using Simultaneous Localisation and Mapping (SLAM) in order to estimate the position of a UAV using an Inertial Measurement Unit (IMU) and the direction towards ground based radio transmitters without prior knowledge of their position. Simulations using appropriately generated data provides a feasibility analysis which shows promising results for position errors for outdoor trajectories over large areas, however with some issues regarding overall offset. The method seems to have potential but further studies are required using the measurements from a live flight, in order to determine the true performance.
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Rohlén, Andreas. "UAV geolocalization in Swedish fields and forests using Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300390.
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The ability for unmanned autonomous aerial vehicles (UAV) to localize themselves in an environment is fundamental for them to be able to function, even if they do not have access to a global positioning system. Recently, with the success of deep learning in vision based tasks, there have been some proposed methods for absolute geolocalization using vison based deep learning with satellite and UAV images. Most of these are only tested in urban environments, which begs the question: How well do they work in non-urban areas like forests and fields? One drawback of deep learning is that models are often regarded as black boxes, as it is hard to know why the models make the predictions they do, i.e. what information is important and is used for the prediction. To solve this, several neural network interpretation methods have been developed. These methods provide explanations so that we may understand these models better. This thesis investigates the localization accuracy of one geolocalization method in both urban and non-urban environments as well as applies neural network interpretation in order to see if it can explain the potential difference in localization accuracy of the method in these different environments. The results show that the method performs best in urban environments, getting a mean absolute horizontal error of 38.30m and a mean absolute vertical error of 16.77m, while it performed significantly worse in non-urban environments, getting a mean absolute horizontal error of 68.11m and a mean absolute vertical error 22.83m. Further, the results show that if the satellite images and images from the unmanned aerial vehicle are collected during different seasons of the year, the localization accuracy is even worse, resulting in a mean absolute horizontal error of 86.91m and a mean absolute vertical error of 23.05m. The neural network interpretation did not aid in providing an explanation for why the method performs worse in non-urban environments and is not suitable for this kind of problem.<br>Obemannade autonoma luftburna fordons (UAV) förmåga att lokaliera sig själva är fundamental för att de ska fungera, även om de inte har tillgång till globala positioneringssystem. Med den nyliga framgången hos djupinlärning applicerat på visuella problem har det kommit metoder för absolut geolokalisering med visuell djupinlärning med satellit- och UAV-bilder. De flesta av dessa metoder har bara blivit testade i stadsmiljöer, vilket leder till frågan: Hur väl fungerar dessa metoder i icke-urbana områden som fält och skogar? En av nackdelarna med djupinlärning är att dessa modeller ofta ses som svarta lådor eftersom det är svårt att veta varför modellerna gör de gissningar de gör, alltså vilken information som är viktig och används för gissningen. För att lösa detta har flera metoder för att tolka neurala nätverk utvecklats. Dessa metoder ger förklaringar så att vi kan förstå dessa modeller bättre. Denna uppsats undersöker lokaliseringsprecisionen hos en geolokaliseringsmetod i både urbana och icke-urbana miljöer och applicerar även en tolkningsmetod för neurala nätverk för att se ifall den kan förklara den potentialla skillnaden i precision hos metoden i dessa olika miljöer. Resultaten visar att metoden fungerar bäst i urbana miljöer där den får ett genomsnittligt absolut horisontellt lokaliseringsfel på 38.30m och ett genomsnittligt absolut vertikalt fel på 16.77m medan den presterade signifikant sämre i icke-urbana miljöer där den fick ett genomsnittligt absolut horisontellt lokaliseringsfel på 68.11m och ett genomsnittligt absolut vertikalt fel på 22.83m. Vidare visar resultaten att om satellitbilderna och UAV-bilderna är tagna från olika årstider blir lokaliseringsprecisionen ännu sämre, där metoden får genomsnittligt absolut horisontellt lokaliseringsfel på 86.91m och ett genomsnittligt absolut vertikalt fel på 23.05m. Tolkningsmetoden hjälpte inte i att förklara varför metoden fungerar sämre i icke-urbana miljöer och är inte passande att använda för denna sortens problem.
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Mahdoui, Chedly Nesrine. "Communicating multi-UAV system for cooperative SLAM-based exploration." Thesis, Compiègne, 2018. http://www.theses.fr/2018COMP2447/document.
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Dans la communauté robotique aérienne, un croissant intérêt pour les systèmes multirobot (SMR) est apparu ces dernières années. Cela a été motivé par i) les progrès technologiques, tels que de meilleures capacités de traitement à bord des robots et des performances de communication plus élevées, et ii) les résultats prometteurs du déploiement de SMR tels que l’augmentation de la zone de couverture en un minimum de temps. Le développement d’une flotte de véhicules aériens sans pilote (UAV: Unmanned Aerial Vehicle) et de véhicules aériens de petite taille (MAV: Micro Aerial Vehicle) a ouvert la voie à de nouvelles applications à grande échelle nécessitant les caractéristiques de tel système de systèmes dans des domaines tels que la sécurité, la surveillance des catastrophes et des inondations, la recherche et le sauvetage, l’inspection des infrastructures, et ainsi de suite. De telles applications nécessitent que les robots identifient leur environnement et se localisent. Ces tâches fondamentales peuvent être assurées par la mission d’exploration. Dans ce contexte, cette thèse aborde l’exploration coopérative d’un environnement inconnu en utilisant une équipe de drones avec vision intégrée. Nous avons proposé un système multi-robot où le but est de choisir des régions spécifiques de l’environnement à explorer et à cartographier simultanément par chaque robot de manière optimisée, afin de réduire le temps d’exploration et, par conséquent, la consommation d’énergie. Chaque UAV est capable d’effectuer une localisation et une cartographie simultanées (SLAM: Simultaneous Localization And Mapping) à l’aide d’un capteur visuel comme principale modalité de perception. Pour explorer les régions inconnues, les cibles – choisies parmi les points frontières situés entre les zones libres et les zones inconnues – sont assignées aux robots en considérant un compromis entre l’exploration rapide et l’obtention d’une carte détaillée. À des fins de prise de décision, les UAVs échangent habituellement une copie de leur carte locale, mais la nouveauté dans ce travail est d’échanger les points frontières de cette carte, ce qui permet d’économiser la bande passante de communication. L’un des points les plus difficiles du SMR est la communication inter-robot. Nous étudions cette partie sous les aspects topologiques et typologiques. Nous proposons également des stratégies pour faire face à l’abandon ou à l’échec de la communication. Des validations basées sur des simulations étendues et des bancs d’essai sont présentées<br>In the aerial robotic community, a growing interest for Multi-Robot Systems (MRS) appeared in the last years. This is thanks to i) the technological advances, such as better onboard processing capabilities and higher communication performances, and ii) the promising results of MRS deployment, such as increased area coverage in minimum time. The development of highly efficient and affordable fleet of Unmanned Aerial Vehicles (UAVs) and Micro Aerial Vehicles (MAVs) of small size has paved the way to new large-scale applications, that demand such System of Systems (SoS) features in areas like security, disaster surveillance, inundation monitoring, search and rescue, infrastructure inspection, and so on. Such applications require the robots to identify their environment and localize themselves. These fundamental tasks can be ensured by the exploration mission. In this context, this thesis addresses the cooperative exploration of an unknown environment sensed by a team of UAVs with embedded vision. We propose a multi-robot framework where the key problem is to cooperatively choose specific regions of the environment to be simultaneously explored and mapped by each robot in an optimized manner in order to reduce exploration time and, consequently, energy consumption. Each UAV is able to performSimultaneous Localization And Mapping (SLAM) with a visual sensor as the main input sensor. To explore the unknown regions, the targets – selected from the computed frontier points lying between free and unknown areas – are assigned to robots by considering a trade-off between fast exploration and getting detailed grid maps. For the sake of decision making, UAVs usually exchange a copy of their local map; however, the novelty in this work is to exchange map frontier points instead, which allow to save communication bandwidth. One of the most challenging points in MRS is the inter-robot communication. We study this part in both topological and typological aspects. We also propose some strategies to cope with communication drop-out or failure. Validations based on extensive simulations and testbeds are presented
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Johansen, David Linn. "Video Stabilization and Target Localization Using Feature Tracking with Video from Small UAVs." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1522.pdf.
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Johansson, Fredrik, and Samuel Svensson. "Evaluation of Monocular Visual SLAM Methods on UAV Imagery to Reconstruct 3D Terrain." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177585.
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When reconstructing the Earth in 3D, the imagery can come from various mediums, including satellites, planes, and drones. One significant benefit of utilizing drones in combination with a Visual Simultaneous Localization and Mapping (V-SLAM) system is that specific areas of the world can be accurately mapped in real-time at a low cost. Drones can essentially be equipped with any camera sensor, but most commercially available drones use a monocular rolling shutter camera sensor. Therefore, on behalf of Maxar Technologies, multiple monocular V-SLAM systems were studied during this thesis, and ORB-SLAM3 and LDSO were determined to be evaluated further. In order to provide an accurate and reproducible result, the methods were benchmarked on the public datasets EuRoC MAV and TUM monoVO, which includes drone imagery and outdoor sequences, respectively. A third dataset was collected with a DJI Mavic 2 Enterprise Dual drone to evaluate how the methods would perform with a consumer-friendly drone. The datasets were used to evaluate the two V-SLAM systems regarding the generated 3D map (point cloud) and estimated camera trajectory. The results showed that ORB-SLAM3 is less impacted by the artifacts caused by a rolling shutter camera sensor than LDSO. However, ORB-SLAM3 generates a sparse point cloud where depth perception can be challenging since it abstracts the images using feature descriptors. In comparison, LDSO produces a semi-dense 3D map where each point includes the pixel intensity, which improves the depth perception. Furthermore, LDSO is more suitable for dark environments and low-texture surfaces. Depending on the use case, either method can be used as long as the required prerequisites are provided. In conclusion, monocular V-SLAM systems are highly dependent on the type of sensor being used. The differences in the accuracy and robustness of the systems using a global shutter and a rolling shutter are significant, as the geometric artifacts caused by a rolling shutter are devastating for a pure visual pipeline.<br><p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>
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Mantelli, Mathias Fassini. "Um novo modelo de observação para o algoritmo de Monte Carlo aplicado ao problema de localização global de VANTs sobre imagens de satélite." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/156646.
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A cada dia novos modelos de Veículos Aéreos Não Tripulados (VANTs) estão sendo lançados no mercado para serem utilizados em diversas aplicações, tais como mapeamento de ambientes e vigilância. Geralmente, estes robôs utilizam um sensor GPS como fonte de estimativa de localização. Contudo, para um bom funcionamento, este sensor depende de um número mínimo de satélites sincronizados com ele e que o sinal emitido pelos satélites seja recebido com boa qualidade, o que pode ser considerado um fator negativo. Uma forma de contornar este problema é empregar um sistema de localização baseado em visão computacional utilizando a câmera que já está embarcada no robô e imagens de satélite como mapa. Este sistema estima a localização do VANT através de comparações entre as imagens capturadas por ele e uma imagem de satélite, buscando encontrar a sua posição nesta imagem de satélite. Neste contexto, apresentamos uma variação do descritor BRIEF, o abBRIEF, para ser utilizado em um novo modelo de observação que também está sendo proposto. O modelo de observação é responsável por medir quão parecidas são as leituras do robô com diversas partes do mapa, para estimar a sua localização correta. Devido ao grande número de comparações necessárias, é importante que o descritor utilizado no processo seja rápido, não consuma muitos recursos computacionais e seja robusto para lidar com as várias diferenças entre as imagens. O modelo proposto foi utilizado no algoritmo de Monte Carlo (Monte Carlo Localization, MCL) para realizar a localização de VANTs e apresentou resultados satisfatórios que corroboram a eficácia do modelo e do descritor.<br>New models of Unnamed Aerial Vehicles (UAVs) are developed on a daily basis and applied to several tasks, such as mapping terrains and surveillance. GPS sensors are usually the main source of information to estimate the robot position, however, a downside of these sensors is that a minimum number of satellites must be available and emitting high quality signals. A vision-based system can be used to overcome this problem by using a robot embedded camera and satellite images as maps. Computational vision systems estimate the UAV localization through the comparison between the robot extracted image and several parts of the satellite image. These comparisons are performed in order to localize the part of the map which is most similar to the robot perspective. Taking into account all this information, we propose BRIEF descriptor variation, called abBRIEF, to be used on a novel observation model, also proposed in this master thesis. An observation model is responsible for evaluate how similar the robot measurements and the different map parts are. The used descriptor must run fast, consume low computational resources and be robust regarding image changes, all to compensate the large number of necessary comparisons. The proposed model is applied with Monte Carlo Localization (MCL) method to the auto localization of UAVs and presented solid results that corroborate the model and descriptor efficiency.
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Sparnacci, Nicola. "Localizzazione e navigazione congiunta di droni." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15758/.
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In questa tesi vengono analizzati aspetti legati alla navigazione e localizzazione congiunta di Unmanned Aerial-Vehicle (UAV) per la ricerca della formazione ottimale in modo da minimizzare l'errore di stima della posizione di un target, situato in ambienti indoor piuttosto che outdoor. Attraverso l’uso di modelli d'osservazione realistici che tengono conto anche di eventuali scenari in cui vi sia un collegamento Non-Line of Sight (NLOS) tra target e UAV, vengono derivate le grandezze fondamentali legate al Cramér-Rao Lower Bound (CRLB) come la Fisher Information Matrix (FIM) e il Position Error Bound (PEB) per l’implementazione un algoritmo (centralizzato/distribuito) di ottimizzazione vincolata per il calcolo della formazione ottimale. Sono descritte metodologie e principi utili per creare un algoritmo di navigazione degli UAV tale che permetta di districarsi in ambienti caratterizzati dalla presenza di numerosi ostacoli (cluttered environments) e di evitare la collisione tra UAV, mantenendo condizioni di sicurezza. Nell'ipotesi in cui gli UAV siano in grado di comunicare e collaborare, vengono fornite le basi di teoria dei grafi per l'instaurazione della communication multi-hop rispettando i vincoli di massima latenza intrinseci alle applicazioni real-time quali la localizzazione di un target, aspetto non considerato in letteratura. I risultati hanno mostrato come il numero di UAV che compongono la rete giochi un ruolo fondamentale in termini di velocità di convergenza fissata l'accuratezza di localizzazione desiderata anche in presenza di un approccio distribuito single hop. Infatti, il numero di UAV utilizzati incide nel miglioramento dell'accuratezza di localizzazione sia nella versione centralizzata che distribuita dell'algoritmo. Attraverso i risultati delle simulazioni sono state ottenute importanti linee guida sul dimensionamento della rete di UAV al variare delle condizioni operative.
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Bednařík, Jan. "Optická lokalizace velmi vzdálených cílů ve vícekamerovém systému." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255418.
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This work presents a system for semi-autonomous optical localization of distant moving targets using multiple positionable cameras. The cameras were calibrated and stationed using custom designed calibration targets and methodology with the objective to alleviate the main sources of errors which were pinpointed in thorough precision analysis. The detection of the target is performed manually, while the visual tracking is automatic and it utilizes two state-of-the-art approaches. The estimation of the target location in 3-space is based on multi-view triangulation working with noisy measurements. A basic setup consisting of two camera units was tested against static targets and a moving terrestrial target, and the precision of the location estimation was compared to the theoretical model. The modularity and portability of the system allows fast deployment in a wide range of scenarios including perimeter monitoring or early threat detection in defense systems, as well as air traffic control in public space.
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Chadha, Abhimanyu. "Vision Based Localization of Drones in a GPS Denied Environment." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99887.
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In this thesis, we build a robust end-to-end pipeline for the localization of multiple drones in
a GPS-denied environment. This pipeline would help us with cooperative formation control,
autonomous delivery, search and rescue operations etc. To achieve this we integrate a custom
trained YOLO (You Only Look Once) object detection network, for drones, with the ZED2
stereo camera system. With the help of this sensor we obtain a relative vector from the left
camera to that drone. After calibrating it from the left camera to that drone's center of mass,
we then estimate the location of all the drones in the leader drone's frame of reference. We
do this by solving the localization problem with least squares estimation and thus acquire
the location of the follower drone's in the leader drone's frame of reference. We present the
results with the stereo camera system followed by simulations run in AirSim to verify the
precision of our pipeline.<br>Master of Science<br>In the recent years, technologies like Deep Learning and Machine Learning have seen many
rapid developments. This has lead to the rise of fields such as autonomous drones and
their application in fields such as bridge inspection, search and rescue operations, disaster
management relief, agriculture, real estate etc. Since GPS is a highly unreliable sensor, we
need an alternate method to be able to localize the drones in various environments in real
time. In this thesis, we integrate a robust drone detection neural network with a camera
which estimates the location. We then use this data to get the relative location of all
the follower drones from the leader drone. We run experiments with the camera and in a
simulator to show the accuracy of our results.
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Sobers, David Michael Jr. "Efficient ranging-sensor navigation methods for indoor aircraft." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34824.
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Unmanned Aerial Vehicles are often used for reconnaissance, search and rescue, damage assessment, exploration, and other tasks that are dangerous or prohibitively difficult for humans to perform. Often, these tasks include traversing indoor environments where radio links are unreliable, hindering the use of remote pilot links or ground-based control, and effectively eliminating Global Positioning System (GPS) signals as a potential localization method. As a result, any vehicle capable of indoor flight must be able to stabilize itself and perform all guidance, navigation, and control tasks without dependence on a radio link, which may be available only intermittently.
Since the availability of GPS signals in unknown environments is not assured, other sensors must be used to provide position information relative to the environment. This research covers a description of different ranging sensors and methods for incorporating them into the overall guidance, navigation, and control system of a flying vehicle. Various sensors are analyzed to determine their performance characteristics and suitability for indoor navigation, including sonar, infrared range sensors, and a scanning laser rangefinder. Each type of range sensor tested has its own unique characteristics and contributes in a slightly different way to effectively eliminate the dependence on GPS.
The use of low-cost range sensors on an inexpensive passively stabilized coaxial helicopter for drift-tolerant indoor navigation is demonstrated through simulation and flight test. In addition, a higher fidelity scanning laser rangefinder is simulated with an Inertial Measurement Unit (IMU) onboard a quadrotor helicopter to enable active stabilization and position control. Two different navigation algorithms that utilize a scanning laser and techniques borrowed from Simultaneous Localization and Mapping (SLAM) are evaluated for use with an IMU-stabilized flying vehicle. Simulation and experimental results are presented for each of the navigation systems.
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Schiano, Fabrizio. "Bearing-based localization and control for multiple quadrotor UAVs." Thesis, Rennes 1, 2018. http://www.theses.fr/2018REN1S009/document.
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Le but de cette thèse est d'étendre l'état de l'art par des contributions sur le comportement collectif d'un groupe de robots volants, à savoir des quadrirotors UAV. Afin de pouvoir sûrement naviguer dans un environnement, ces derniers peuvent se reposer uniquement sur leurs capacités à bord et non sur des systèmes centralisés (e.g., Vicon ou GPS). Nous réalisons cet objectif en offrant une possible solution aux problèmes de contrôle en formation et de localisation à partir de mesures à bord et via une communication locale. Nous abordons ces problèmes exploitant différents concepts provenant de la théorie des graphes algébriques et de la théorie de la rigidité. Cela nous permet de résoudre ces problèmes de façon décentralisée et de proposer des algorithmes décentralisés capables de prendre en compte également des limites sensorielles classiques. Les capacités embarquées que nous avons mentionnées plus tôt sont représentées par une caméra monoculaire et une centrale inertielle (IMU) auxquelles s'ajoute la capacité de chaque robot à communiquer (par RF) avec certains de ses voisins. Cela est dû au fait que l'IMU et la caméra représentent une possible configuration économique et légère pour la navigation et la localisation autonome d'un quadrirotor UAV<br>The aim of this Thesis is to give contributions to the state of the art on the collective behavior of a group of flying robots, specifically quadrotor UAVs, which can only rely on their onboard capabilities and not on a centralized system (e.g., Vicon or GPS) in order to safely navigate in the environment. We achieve this goal by giving a possible solution to the problems of formation control and localization from onboard sensing and local communication. We tackle these problems exploiting mainly concepts from algebraic graph theory and the so-called theory of rigidity. This allows us to solve these problems in a decentralized fashion, and propose decentralized algorithms able to also take into account some typical sensory limitations. The onboard capabilities we referred to above are represented by an onboard monocular camera and an inertial measurement unit (IMU) in addition to the capability of each robot to communicate (through RF) with some of its neighbors. This is due to the fact that an IMU and a camera represent a possible minimal, lightweight and inexpensive configuration for the autonomous localization and navigation of a quadrotor UAV
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Hansen, Steven R. "Applications of Search Theory to Coordinated Searching by Unmanned Aerial Vehicles." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1809.pdf.
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Davis, Robert B. "Applying Cooperative Localization to swarm UAVs using an extended Kalman Filter." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/43900.
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Approved for public release; distribution is unlimited<br>Cooperative Localization (CL) is a process by which autonomous vehicles operating as a team estimate the position of one another to compensate for errors in the positioning sensors used by a single agent. By combining independent measurements originating from members of the team, a single estimate of increased accuracy will result. This approach has the potential to enhance the positional accuracy of an agent over use of a standard GPS, which would be essential for behaviors within a swarm requiring precision move-ments such as maintaining close formation. CL can also provide accurate positional information to the entire group when operating in an intermittent or denied GPS environment. In this thesis, a distributed CL algorithm is implemented on a swarm of Unmanned Aerial Vehicles (UAVs) using an Extended Kalman Filter. Using a technique created for ground robots, the equations are modified to adapt the algorithm to aerial vehicles, and then operation of the algorithm is demonstrated in a centralized system using AR Drones and the Robot Operating System. During tests, the positional accuracy of the UAV using CL improved over use of dead reckoning. However, the performance is not as expected based on the results noted from the referenced two-dimensional application of the al-gorithm. It is presumed that the sensors on-board the AR Drone are responsible. Since the platform is simply a low-cost solution to show proof-of-concept, it is concluded that the implementation of CL presented in this thesis is a suitable approach for enhancing positional accuracy of UAVs within a swarm.
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Sconyers, Christopher. "Particle filter-based architecture for video target tracking and geo-location using multiple UAVs." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47734.
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Research in the areas of target detection, tracking, and geo-location is most important for enabling an unmanned aerial vehicle (UAV) platform to autonomously execute a mission or task without the need for a pilot or operator. Small-class UAVs and video camera sensors complemented with "soft sensors" realized only in software as a combination of a priori knowledge and sensor measurements are called upon to replace the cumbersome precision sensors on-board a large class UAV. The objective of this research is to develop a geo-location solution for use on-board multiple UAVs with mounted video camera sensors only to accurately geo-locate and track a target. This research introduces an estimation solution that combines the power of the particle filter with the utility of the video sensor as a general solution for passive target geo-location on-board multiple UAVs. The particle filter is taken advantage of, with its ability to use all of the available information about the system model, system uncertainty, and the sensor uncertainty to approximate the statistical likelihood of the target state. The geo-location particle filter is tested online and in real-time in a simulation environment involving multiple UAVs with video cameras and a maneuvering ground vehicle as a target. Simulation results show the geo-location particle filter estimates the target location with a high accuracy, the addition of UAVs or particles to the system improves the location estimation accuracy with minimal addition of processing time, and UAV control and trajectory generation algorithms restrict each UAV to a desired range to minimize error.
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Stone, Thomas Jonathan. "Mechanisms of place recognition and path integration based on the insect visual system." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28909.
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Animals are often able to solve complex navigational tasks in very challenging terrain, despite using low resolution sensors and minimal computational power, providing inspiration for robots. In particular, many species of insect are known to solve complex navigation problems, often combining an array of different behaviours (Wehner et al., 1996; Collett, 1996). Their nervous system is also comparatively simple, relative to that of mammals and other vertebrates. In the first part of this thesis, the visual input of a navigating desert ant, Cataglyphis velox, was mimicked by capturing images in ultraviolet (UV) at similar wavelengths to the ant’s compound eye. The natural segmentation of ground and sky lead to the hypothesis that skyline contours could be used by ants as features for navigation. As proof of concept, sky-segmented binary images were used as input for an established localisation algorithm SeqSLAM (Milford and Wyeth, 2012), validating the plausibility of this claim (Stone et al., 2014). A follow-up investigation sought to determine whether using the sky as a feature would help overcome image matching problems that the ant often faced, such as variance in tilt and yaw rotation. A robotic localisation study showed that using spherical harmonics (SH), a representation in the frequency domain, combined with extracted sky can greatly help robots localise on uneven terrain. Results showed improved performance to state of the art point feature localisation methods on fast bumpy tracks (Stone et al., 2016a). In the second part, an approach to understand how insects perform a navigational task called path integration was attempted by modelling part of the brain of the sweat bee Megalopta genalis. A recent discovery that two populations of cells act as a celestial compass and visual odometer, respectively, led to the hypothesis that circuitry at their point of convergence in the central complex (CX) could give rise to path integration. A firing rate-based model was developed with connectivity derived from the overlap of observed neural arborisations of individual cells and successfully used to build up a home vector and steer an agent back to the nest (Stone et al., 2016b). This approach has the appeal that neural circuitry is highly conserved across insects, so findings here could have wide implications for insect navigation in general. The developed model is the first functioning path integrator that is based on individual cellular connections.
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Bolting, Jan. "Contributions au vol en formation serrée de petits drones." Thesis, Toulouse, ISAE, 2017. http://www.theses.fr/2017ESAE0013.
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Les mini-drones à propulsion électrique sont susceptibles d’avoir une endurance inférieure à celle de drones plus grands.L’exploitation des interactions aérodynamiques, inspirée par les oiseaux migratoires, ainsi que le ravitaillement en vol , sont des approches prometteuses pour améliorer l’endurance des mini-drones. La commande par modes glissants d’ordre supérieur en temps continu (CTHOSM) a été considérée comme un candidat prometteur à ce problème ouvert difficile et a été appliquée avec succès à des modèles cinématiques simples. Dans nos travaux, nous étudions les implications de la présence de la dynamique de la boucle interne et de l’implémentation en temps discret à des taux d’échantillonnage modérés et constatons alors que l’application de la commande CTHOSM devient impossible. Nous proposons donc un schéma de guidage prédictif discret par modes glissants pour approximer les performances de la commande CTHOSM pour une dynamique réaliste du drone. On propose également un problème de référence accessible pour d'autres chercheurs. Les algorithmes de localisation probabilistes existants ne permettent pas la caractérisation de régions de confiance garanties de la position des autres membres de la formation. Dans ce contexte, nous proposons un nouveau filtre ensembliste caractérisant de telles régions de confiance sous forme ellipsoïdale. Nos premières évaluations ont montré que les efforts de calcul induits par cette mise en œuvre restent parfaitement compatibles avec les contraintes des systèmes avioniques des petits drones<br>Small, electrically driven unmanned aircraft are likely to suffer from inferior endurance compared to their larger counterparts. Upwash exploitation by tight formation flight, as well as aerial recharging are the most promising control-driven approaches to mitigate this disadvantage. Continuous time higher order sliding mode control (CTHOSM) has been considered as a candidate for this challenging open problem and was successfully applied to simple kinematic models in simulation, where excellent relative position tracking performance can be demonstrated. In this work we study the implications of the presence of inner loop dynamics and discrete implementation at moderate sampling rates and we find that it precludes the application of CTHOSM control to fixed-wing UAS. We propose a predictive discrete sliding mode guidance scheme to approximate the performance of CTHOSM control assuming realistic fixed-wing UAS dynamics. We show that the proposed guidance scheme in combination with inner load factor tracking loops and a disturbance observer allows for relative position tracking performance compatible with the requirements of upwash exploitation. We propose as well an openly accessible benchmark problem. Existing probabilistic localization algorithms cannot provide guaranteed confidence regions of the relative position between UAS. We present a set membership filter that provides ellipsoidal regions guaranteed to contain the relative positions of the other UAS. It is compatible with the hardware constraints of small low-cost UAS. Simulations suggest computational efforts compatible with the computational resources typically available onboard small UAS
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Schubert, Stefan. "Optimierter Einsatz eines 3D-Laserscanners zur Point-Cloud-basierten Kartierung und Lokalisierung im In- und Outdoorbereich." Master's thesis, Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-161415.
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Die Kartierung und Lokalisierung eines mobilen Roboters in seiner Umgebung ist eine wichtige Voraussetzung für dessen Autonomie. In dieser Arbeit wird der Einsatz eines 3D-Laserscanners zur Erfüllung dieser Aufgaben untersucht. Durch die optimierte Anordnung eines rotierenden 2D-Laserscanners werden hochauflösende Bereiche vorgegeben. Zudem wird mit Hilfe von ICP die Kartierung und Lokalisierung im Stillstand durchgeführt. Bei der Betrachtung zur Verbesserung der Bewegungsschätzung wird auch eine Möglichkeit zur Lokalisierung während der Bewegung mit 3D-Scans vorgestellt. Die vorgestellten Algorithmen werden durch Experimente mit realer Hardware evaluiert.
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Randriamasy, Malalatiana. "Localisation et transmissions sécurisées pour la communication Véhicule à Infrastructure (V2I) : Application au service de télépéage ITS-G5." Thesis, Normandie, 2019. http://www.theses.fr/2019NORMR011/document.
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La localisation précise des véhicules et la sécurité des échanges sont deux grands axes qui font la fiabilité des services fournis dans les systèmes de transport intelligent. Ces dernières années, elles font l’objet de nombreux projets de recherche pour des champs d’application divers. Dans cette thèse, le contexte d’application est la réalisation d’un service de télépéage utilisant la technologie ITS-G5. Cette technologie de communication sans-fil permet dans un premier temps le partage des informations de sécurité routière entre les véhicules (V2V), le véhicule et l’infrastructure (V2I). Dans cette thèse, on propose une architecture permettant d’échanger des transactions de télépéage utilisant les équipements communicants en ITS-G5 embarqués dans les véhicules connectés et les unités bord de route (UBR) de l’infrastructure. Les problématiques de nos travaux de recherche se concentrent sur la méthode de localisation des véhicules ayant effectué la transaction afin de pouvoir la valider et sur la sécurité de l’architecture proposée pour assurer l’échange de cette transaction. Afin de bien localiser les véhicules lors du passage au péage, notre approche propose la compréhension de la cinématique du véhicule par une modélisation adéquate à partir des données recueillies dans les messages coopératifs (CAM : Cooperative Awareness Message) en approche du péage. Cela améliorera les informations de géolocalisation déjà présentes. Notre objectif est d’arriver à une précision de moins d’un mètre pour distinguer 2 véhicules adjacents. D’autre part, le protocole de sécurité proposé permet d’assurer l’authentification des équipements participant à l’échange et à la validation de la transaction, l’intégrité des données échangées ainsi que la confidentialité des échanges compte tenu du contexte de communication sans-fil et de la sensibilité des données échangées. Une preuve de concept de la solution de télépéage utilisant la technologie ITS-G5 est développée et intègre nos deux contributions<br>The precise localization of vehicles and the security of communication are requirements that make almost of the services provided in intelligent transport systems (ITS) more reliable. In recent years, they have been the subject of numerous research projects for various fields of application. In this thesis, the context is the development of an electronic toll service using the ITS-G5 technology. This wireless communication technology initially allows the sharing of traffic safety information between vehicles (V2V), vehicle and infrastructure (V2I). In our work, we propose a tolling application using equipment operating in ITS-G5 embedded in the connected vehicles and roadside units. For this, ensuring both precise geolocation of the vehicles and security of communication are required to validate the transaction.In order to properly locate the vehicles during the toll crossing, our approach is based on the understanding of the kinematics of the vehicle through a suitable modeling from the data collected in the cooperative messages (called CAM: Cooperative Awareness Message). This approach aims to improve the geolocation information already present in the message. Our goal is to achieve vehicle localization with an accuracy lower than one meter to distinguish two adjacent vehicles. On the other hand, the proposed tolling protocol ensures the authentication of the equipment or entities involved in the exchange and the validation of the transaction, the integrity of the transmitted data as well as the confidentiality of the communication. In this way, we take into account the context of the wireless communication and the sensitivity of the exchanged data. Our two contributions are integrated in the implemented Proof of Concept of the tolling application using the ITS-G5 technology
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Coelho, Vishal Savio. "Localization system for UAV/UGV in urban environments." 2009. http://hdl.handle.net/10106/2020.
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Jou-AnChen and 陳柔安. "Deep Representation on One-shot Learning Model for Cross-view Localization of UAV." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/2whx32.
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碩士<br>國立成功大學<br>工程科學系<br>106<br>This study targeted at cross-view localization of UAV, hoping to solve the dependency of GPS signals in GPS-denied scenario. How can a UAV localize itself with the surrounding scene in such environment, or even find a global path for path planning are the issues we discussed.
In this study, an one-shot learning model based on CNN feature representation for cross-view localization of UAV is proposed. The system will first learn the scene features from same/different viewpoints in same/different spots to recognize whether two given images are indicating same spots. Then, the video captured by UAV will be streaming real-time and match with the precomputed feature vectors of supporting set images. Finally, the nearest feature vectors indicating the location will be outputted.
This study transformed the previous cross-view image retrieval problem to actual localization problem on multi-rotor UAVs, applying to real campus environment for structuring the problem and testing. The work can be divided into two parts: In partⅠ, the training process of the proposed model on self-collected campus images was presented. Result and observation were included for further improvement. In partⅡ, the training model was revised through these observations to address the localization error. Finally, the improved model was validated on UAV captured videos and showed that the localization error can be bounds to 25-30m, confirming the feasibility of the proposed method.
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Jian-ShiunWu and 吳建勳. "Integration of Siamese Network and ResNet Models for Cross-view Localization of UAV." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/cm2fxa.
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(10716747), Facundo Ramiro Esquivel Fagiani. "UAV DETECTION AND LOCALIZATION SYSTEM USING AN INTERCONNECTED ARRAY OF ACOUSTIC SENSORS AND MACHINE LEARNING ALGORITHMS." Thesis, 2021.
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<div> The Unmanned Aerial Vehicles (UAV) technology has evolved exponentially in recent years. Smaller and less expensive devices allow a world of new applications in different areas, but as this progress can be beneficial, the use of UAVs with malicious intentions also poses a threat. UAVs can carry weapons or explosives and access restricted zones passing undetected, representing a real threat for civilians and institutions. Acoustic detection in combination with machine learning models emerges as a viable solution since, despite its limitations related with environmental noise, it has provided promising results on classifying UAV sounds, it is adaptable to multiple environments, and especially, it can be a cost-effective solution, something much needed in the counter UAV market with high projections for the coming years. The problem addressed by this project is the need for a real-world adaptable solution which can show that an array of acoustic sensors can be implemented for the detection and localization of UAVs with minimal cost and competitive performance.<br><br></div><div> In this research, a low-cost acoustic detection system that can detect, in real time, about the presence and direction of arrival of a UAV approaching a target was engineered and validated. The model developed includes an array of acoustic sensors remotely connected to a central server, which uses the sound signals to estimate the direction of arrival of the UAV. This model works with a single microphone per node which calculates the position based on the acoustic intensity change produced by the UAV, reducing the implementation costs and being able to work asynchronously. The development of the project included collecting data from UAVs flying both indoors and outdoors, and a performance analysis under realistic conditions. <br><br></div><div> The results demonstrated that the solution provides real time UAV detection and localization information to protect a target from an attacking UAV, and that it can be applied in real world scenarios. </div><div><br></div>
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Jha, Naveen K. "Studies of carrier localization in aluminum-gallium-nitrogen alloys for efficient UV-emitters." 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3341186.
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Schubert, Stefan. "Optimierter Einsatz eines 3D-Laserscanners zur Point-Cloud-basierten Kartierung und Lokalisierung im In- und Outdoorbereich." Master's thesis, 2014. https://monarch.qucosa.de/id/qucosa%3A20206.
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Die Kartierung und Lokalisierung eines mobilen Roboters in seiner Umgebung ist eine wichtige Voraussetzung für dessen Autonomie. In dieser Arbeit wird der Einsatz eines 3D-Laserscanners zur Erfüllung dieser Aufgaben untersucht. Durch die optimierte Anordnung eines rotierenden 2D-Laserscanners werden hochauflösende Bereiche vorgegeben. Zudem wird mit Hilfe von ICP die Kartierung und Lokalisierung im Stillstand durchgeführt. Bei der Betrachtung zur Verbesserung der Bewegungsschätzung wird auch eine Möglichkeit zur Lokalisierung während der Bewegung mit 3D-Scans vorgestellt. Die vorgestellten Algorithmen werden durch Experimente mit realer Hardware evaluiert.