Contributions to improve the technologies supporting unmanned aircraft operations

Mención Internacional en el título de doctorUnmanned Aerial Vehicles (UAVs), in their smaller versions known as drones, are becoming increasingly important in today's societies. The systems that make them up present a multitude of challenges, of which error can be considered the common denominator. The perception of the environment is measured by sensors that have errors, the models that interpret the information and/or define behaviors are approximations of the world and therefore also have errors. Explaining error allows extending the limits of deterministic models to address real-world problems. The performance of the technologies embedded in drones depends on our ability to understand, model, and control the error of the systems that integrate them, as well as new technologies that may emerge. Flight controllers integrate various subsystems that are generally dependent on other systems. One example is the guidance systems. These systems provide the engine's propulsion controller with the necessary information to accomplish a desired mission. For this purpose, the flight controller is made up of a control law for the guidance system that reacts to the information perceived by the perception and navigation systems. The error of any of the subsystems propagates through the ecosystem of the controller, so the study of each of them is essential. On the other hand, among the strategies for error control are state-space estimators, where the Kalman filter has been a great ally of engineers since its appearance in the 1960s. Kalman filters are at the heart of information fusion systems, minimizing the error covariance of the system and allowing the measured states to be filtered and estimated in the absence of observations. State Space Models (SSM) are developed based on a set of hypotheses for modeling the world. Among the assumptions are that the models of the world must be linear, Markovian, and that the error of their models must be Gaussian. In general, systems are not linear, so linearization are performed on models that are already approximations of the world. In other cases, the noise to be controlled is not Gaussian, but it is approximated to that distribution in order to be able to deal with it. On the other hand, many systems are not Markovian, i.e., their states do not depend only on the previous state, but there are other dependencies that state space models cannot handle. This thesis deals a collection of studies in which error is formulated and reduced. First, the error in a computer vision-based precision landing system is studied, then estimation and filtering problems from the deep learning approach are addressed. Finally, classification concepts with deep learning over trajectories are studied. The first case of the collection xviiistudies the consequences of error propagation in a machine vision-based precision landing system. This paper proposes a set of strategies to reduce the impact on the guidance system, and ultimately reduce the error. The next two studies approach the estimation and filtering problem from the deep learning approach, where error is a function to be minimized by learning. The last case of the collection deals with a trajectory classification problem with real data. This work completes the two main fields in deep learning, regression and classification, where the error is considered as a probability function of class membership.Los vehículos aéreos no tripulados (UAV) en sus versiones de pequeño tamaño conocidos como drones, van tomando protagonismo en las sociedades actuales. Los sistemas que los componen presentan multitud de retos entre los cuales el error se puede considerar como el denominador común. La percepción del entorno se mide mediante sensores que tienen error, los modelos que interpretan la información y/o definen comportamientos son aproximaciones del mundo y por consiguiente también presentan error. Explicar el error permite extender los límites de los modelos deterministas para abordar problemas del mundo real. El rendimiento de las tecnologías embarcadas en los drones, dependen de nuestra capacidad de comprender, modelar y controlar el error de los sistemas que los integran, así como de las nuevas tecnologías que puedan surgir. Los controladores de vuelo integran diferentes subsistemas los cuales generalmente son dependientes de otros sistemas. Un caso de esta situación son los sistemas de guiado. Estos sistemas son los encargados de proporcionar al controlador de los motores información necesaria para cumplir con una misión deseada. Para ello se componen de una ley de control de guiado que reacciona a la información percibida por los sistemas de percepción y navegación. El error de cualquiera de estos sistemas se propaga por el ecosistema del controlador siendo vital su estudio. Por otro lado, entre las estrategias para abordar el control del error se encuentran los estimadores en espacios de estados, donde el filtro de Kalman desde su aparición en los años 60, ha sido y continúa siendo un gran aliado para los ingenieros. Los filtros de Kalman son el corazón de los sistemas de fusión de información, los cuales minimizan la covarianza del error del sistema, permitiendo filtrar los estados medidos y estimarlos cuando no se tienen observaciones. Los modelos de espacios de estados se desarrollan en base a un conjunto de hipótesis para modelar el mundo. Entre las hipótesis se encuentra que los modelos del mundo han de ser lineales, markovianos y que el error de sus modelos ha de ser gaussiano. Generalmente los sistemas no son lineales por lo que se realizan linealizaciones sobre modelos que a su vez ya son aproximaciones del mundo. En otros casos el ruido que se desea controlar no es gaussiano, pero se aproxima a esta distribución para poder abordarlo. Por otro lado, multitud de sistemas no son markovianos, es decir, sus estados no solo dependen del estado anterior, sino que existen otras dependencias que los modelos de espacio de estados no son capaces de abordar. Esta tesis aborda un compendio de estudios sobre los que se formula y reduce el error. En primer lugar, se estudia el error en un sistema de aterrizaje de precisión basado en visión por computador. Después se plantean problemas de estimación y filtrado desde la aproximación del aprendizaje profundo. Por último, se estudian los conceptos de clasificación con aprendizaje profundo sobre trayectorias. El primer caso del compendio estudia las consecuencias de la propagación del error de un sistema de aterrizaje de precisión basado en visión artificial. En este trabajo se propone un conjunto de estrategias para reducir el impacto sobre el sistema de guiado, y en última instancia reducir el error. Los siguientes dos estudios abordan el problema de estimación y filtrado desde la perspectiva del aprendizaje profundo, donde el error es una función que minimizar mediante aprendizaje. El último caso del compendio aborda un problema de clasificación de trayectorias con datos reales. Con este trabajo se completan los dos campos principales en aprendizaje profundo, regresión y clasificación, donde se plantea el error como una función de probabilidad de pertenencia a una clase.I would like to thank the Ministry of Science and Innovation for granting me the funding with reference PRE2018-086793, associated to the project TEC2017-88048-C2-2-R, which provide me the opportunity to carry out all my PhD. activities, including completing an international research internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Antonio Berlanga de Jesús.- Secretario: Daniel Arias Medina.- Vocal: Alejandro Martínez Cav

Fusion de données capteurs étendue pour applications vidéo embarquées

This thesis deals with sensor fusion between camera and inertial sensors measurements in order to provide a robust motion estimation algorithm for embedded video applications. The targeted platforms are mainly smartphones and tablets. We present a real-time, 2D online camera motion estimation algorithm combining inertial and visual measurements. The proposed algorithm extends the preemptive RANSAC motion estimation procedure with inertial sensors data, introducing a dynamic lagrangian hybrid scoring of the motion models, to make the approach adaptive to various image and motion contents. All these improvements are made with little computational cost, keeping the complexity of the algorithm low enough for embedded platforms. The approach is compared with pure inertial and pure visual procedures. A novel approach to real-time hybrid monocular visual-inertial odometry for embedded platforms is introduced. The interaction between vision and inertial sensors is maximized by performing fusion at multiple levels of the algorithm. Through tests conducted on sequences with ground-truth data specifically acquired, we show that our method outperforms classical hybrid techniques in ego-motion estimation.Le travail réalisé au cours de cette thèse se concentre sur la fusion des données d'une caméra et de capteurs inertiels afin d'effectuer une estimation robuste de mouvement pour des applications vidéos embarquées. Les appareils visés sont principalement les téléphones intelligents et les tablettes. On propose une nouvelle technique d'estimation de mouvement 2D temps réel, qui combine les mesures visuelles et inertielles. L'approche introduite se base sur le RANSAC préemptif, en l'étendant via l'ajout de capteurs inertiels. L'évaluation des modèles de mouvement se fait selon un score hybride, un lagrangien dynamique permettant une adaptation à différentes conditions et types de mouvements. Ces améliorations sont effectuées à faible coût, afin de permettre une implémentation sur plateforme embarquée. L'approche est comparée aux méthodes visuelles et inertielles. Une nouvelle méthode d'odométrie visuelle-inertielle temps réelle est présentée. L'interaction entre les données visuelles et inertielles est maximisée en effectuant la fusion dans de multiples étapes de l'algorithme. A travers des tests conduits sur des séquences acquises avec la vérité terrain, nous montrons que notre approche produit des résultats supérieurs aux techniques classiques de l'état de l'art

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Design and implementation of a relative localization system for ground and aerial robotic teams

The main focus of this thesis is to address the relative localization problem of a heterogenous team which comprises of both ground and micro aerial vehicle robots. This team configuration allows to combine the advantages of increased accessibility and better perspective provided by aerial robots with the higher computational and sensory resources provided by the ground agents, to realize a cooperative multi robotic system suitable for hostile autonomous missions. However, in such a scenario, the strict constraints in flight time, sensor pay load, and computational capability of micro aerial vehicles limits the practical applicability of popular map-based localization schemes for GPS denied navigation. Therefore, the resource limited aerial platforms of this team demand simpler localization means for autonomous navigation. Relative localization is the process of estimating the formation of a robot team using the acquired inter-robot relative measurements. This allows the team members to know their relative formation even without a global localization reference, such as GPS or a map. Thus a typical robot team would benefit from a relative localization service since it would allow the team to implement formation control, collision avoidance, and supervisory control tasks, independent of a global localization service. More importantly, a heterogenous team such as ground robots and computationally constrained aerial vehicles would benefit from a relative localization service since it provides the crucial localization information required for autonomous operation of the weaker agents. This enables less capable robots to assume supportive roles and contribute to the more powerful robots executing the mission. Hence this study proposes a relative localization-based approach for ground and micro aerial vehicle cooperation, and develops inter-robot measurement, filtering, and distributed computing modules, necessary to realize the system. The research study results in three significant contributions. First, the work designs and validates a novel inter-robot relative measurement hardware solution which has accuracy, range, and scalability characteristics, necessary for relative localization. Second, the research work performs an analysis and design of a novel nonlinear filtering method, which allows the implementation of relative localization modules and attitude reference filters on low cost devices with optimal tuning parameters. Third, this work designs and validates a novel distributed relative localization approach, which harnesses the distributed computing capability of the team to minimize communication requirements, achieve consistent estimation, and enable efficient data correspondence within the network. The work validates the complete relative localization-based system through multiple indoor experiments and numerical simulations. The relative localization based navigation concept with its sensing, filtering, and distributed computing methods introduced in this thesis complements system limitations of a ground and micro aerial vehicle team, and also targets hostile environmental conditions. Thus the work constitutes an essential step towards realizing autonomous navigation of heterogenous teams in real world applications

Inertial-optical motion-estimating camera for electronic cinematrography

Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1997.Includes bibliographical references (leaves 108-109).by Christopher James Verplaetse.M.S

Benthic Habitat Analyses Using Micro-bathymetry Data and Subsea Photogrammetry

The very first map of the Arctic Ocean basin with a few lead line sounding changed the supposal of large continental land beneath of the ice. More resolution added over the decades, reveals the detail of the Arctic seafloor structure of seamounts and ridges below the frozen sea. Numerous methods of bathymetry and mapping were applied as the technology developed over the years for different purposes. While the airborne and satellite-based altimetry and gravimetry data provides a large-scale estimation of the seafloor topography by hundreds of meters resolution, the shipborne and submarine sonars focuses on certain features and areas with higher resolution. During the last century the knowledge of the Arctic seabed geomorphology increased dramatically by the development of acoustic technology combined with altimetry and gravimetry while the habitat characteristic of the polar region still contains lots of mysteries. The new developments of underwater survey vehicles are bringing new clarity and perspective from the deep sea to the questioners. The sub-meter resolution data of the seabed could be employed for very high-resolution micro topography as well as habitat mapping and feature detection. The Alfred Wegner Institute for Polar and Marine Research (AWI) developed the Ocean Floor Observation and Bathymetry System (OFOBS) for deep sea research, mostly in polar region. The tailored deep tow system of the AWI is equipped with optical and acoustic sensors in addition to underwater positioning systems. The OFOBS, first deployed during the PS101 expedition, provides a novel dataset of megafauna’s habitats at the Karasik seamount. This thesis is implementing geospatial data mining and knowledge discovery for feature detection by means of habitat mapping in the study area with a focus on the central mount of Karasik seamount where an imperial assemblage of the Geodia sponges are dominating the seafloor. The main datasets for this study are based on the optical sensor of the OFOBS, including video and still images collected during the dives, while the feature detection within the sonar dataset is in the second place. During this work study, the development of the OFOBS is also considered in order to improve the capability of the dataset for further expeditions

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application