Internationales Kolloquium über Anwendungen der Informatik und Mathematik in Architektur und Bauwesen : 20. bis 22.7. 2015, Bauhaus-Universität Weimar

The 20th International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering will be held at the Bauhaus University Weimar from 20th till 22nd July 2015. Architects, computer scientists, mathematicians, and engineers from all over the world will meet in Weimar for an interdisciplinary exchange of experiences, to report on their results in research, development and practice and to discuss. The conference covers a broad range of research areas: numerical analysis, function theoretic methods, partial differential equations, continuum mechanics, engineering applications, coupled problems, computer sciences, and related topics. Several plenary lectures in aforementioned areas will take place during the conference. We invite architects, engineers, designers, computer scientists, mathematicians, planners, project managers, and software developers from business, science and research to participate in the conference

Robust and affordable localization and mapping for 3D reconstruction. Application to architecture and construction

La localización y mapeado simultáneo a partir de una sola cámara en movimiento se conoce como Monocular SLAM. En esta tesis se aborda este problema con cámaras de bajo coste cuyo principal reto consiste en ser robustos al ruido, blurring y otros artefactos que afectan a la imagen. La aproximación al problema es discreta, utilizando solo puntos de la imagen significativos para localizar la cámara y mapear el entorno. La principal contribución es una simplificación del grafo de poses que permite mejorar la precisión en las escenas más habituales, evaluada de forma exhaustiva en 4 datasets. Los resultados del mapeado permiten obtener una reconstrucción 3D de la escena que puede ser utilizada en arquitectura y construcción para Modelar la Información del Edificio (BIM). En la segunda parte de la tesis proponemos incorporar dicha información en un sistema de visualización avanzada usando WebGL que ayude a simplificar la implantación de la metodología BIM.Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Doctorado en Informátic

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

A Comprehensive Introduction of Visual-Inertial Navigation

In this article, a tutorial introduction to visual-inertial navigation(VIN) is presented. Visual and inertial perception are two complementary sensing modalities. Cameras and inertial measurement units (IMU) are the corresponding sensors for these two modalities. The low cost and light weight of camera-IMU sensor combinations make them ubiquitous in robotic navigation. Visual-inertial Navigation is a state estimation problem, that estimates the ego-motion and local environment of the sensor platform. This paper presents visual-inertial navigation in the classical state estimation framework, first illustrating the estimation problem in terms of state variables and system models, including related quantities representations (Parameterizations), IMU dynamic and camera measurement models, and corresponding general probabilistic graphical models (Factor Graph). Secondly, we investigate the existing model-based estimation methodologies, these involve filter-based and optimization-based frameworks and related on-manifold operations. We also discuss the calibration of some relevant parameters, also initialization of state of interest in optimization-based frameworks. Then the evaluation and improvement of VIN in terms of accuracy, efficiency, and robustness are discussed. Finally, we briefly mention the recent development of learning-based methods that may become alternatives to traditional model-based methods.Comment: 35 pages, 10 figure

Inertial Measurement Unit based Virtual Antenna Arrays - DoA Estimation and Positioning in Wireless Networks

Today we have different location based services available in a mobile phone or mobile station (MS). These services include: direction finding to nearby ATMs, locating favorite food restaurants, or finding any target destination. Similarly, we see different applications of the positioning and navigation systems in firefighting or other rescue operations. The common factor in almost all of the location based services is the system's ability to determine the user's current position, with reference to a floor plan or a navigation map. Current technologies are using sensor data measurements from one or more sensors, available to the positioning device, for positioning and navigation. Typical examples are radio based positioning such as global positioning system, inertial sensors based inertial navigation system, or camera based positioning systems. Different accuracy and availability conditions of the positioning and navigation solution can be obtained depending on the positioning algorithms and the available sensor information.Nowadays, the focus of research in positioning and navigation has been mostly on the use of existing hardware infrastructure and low-cost solutions, such that the proposed technique can be deployed with ease and without extra infrastructure requirements as well as without any expensive sensor equipment. In this work, we investigate a novel idea for positioning using existing wireless networks and low-cost inertial sensor measurements available at the MS. We propose to use received baseband radio signal along with inertial sensor data, such as accelerometer and rate gyroscope measurements, for direction of arrival (DoA) estimation and positioning. The DoA information from different base stations or access points can be used to estimate the MS position using triangulation technique. Furthermore, due to size and cost restrictions it is difficult to have real antenna arrays at the MS, the idea of DoA estimation and positioning is proposed to be used with single antenna devices by using the so-called virtual antenna arrays.We have presented our research results in three different papers. We provide measurement based results to perform a quantitative evaluation of DoA estimation using arbitrary virtual antenna arrays in 3-D; where a state-of-the-art high-resolution algorithm has been used for radio signal parameter estimation. Furthermore, we provide an extended Kalman filter framework to investigate the performance of unaided inertial navigation systems with 3-axis accelerometer and 3-axis rate gyroscope measurements, from a six-degrees-of-freedom inertial measurement unit. Using the extended Kalman filter framework, we provide results for position estimation error standard deviation with respect to integration time for an unaided inertial navigation system; where the effect of different stochastic errors noise sources in the inertial sensors measurements such as white Gaussian noise and bias instability noise is investigated. Also, we derive a closed form expression for Cramér-Rao lower bound to investigate DoA estimation accuracy for a far-field source using random antenna arrays in 3-D. The Cramér-Rao lower bound is obtained using known antenna coordinates as well as using estimated antenna coordinates, where the antenna coordinates are estimated with an uncertainty whose standard deviation is known. Furthermore, using Monte-Carlo simulations for random antenna arrays, we provide Cramér-Rao lower bound based performance evaluation of random 3-D antenna arrays for DoA estimation

Theoretical framework for In-Car Navigation based on Integrated GPS/IMU Technologies

In this report the problem of vehicular navigation based on the integration of the global positioning system and an inertial navigation system is tackled. After analysing some fundamental technical issues about reference systems, vehicle modelling and sensors, a novel solution, combining extended Kalman filtering with particle filltering, is developed. This solution allows to embed highly non-linear constraints originating from digital maps in the position estimation process and is expected to be implementable on commercial hardware platforms equipped with low cost inertial sensorsJRC.G.6-Digital Citizen Securit

System Development of an Unmanned Ground Vehicle and Implementation of an Autonomous Navigation Module in a Mine Environment

There are numerous benefits to the insights gained from the exploration and exploitation of underground mines. There are also great risks and challenges involved, such as accidents that have claimed many lives. To avoid these accidents, inspections of the large mines were carried out by the miners, which is not always economically feasible and puts the safety of the inspectors at risk. Despite the progress in the development of robotic systems, autonomous navigation, localization and mapping algorithms, these environments remain particularly demanding for these systems. The successful implementation of the autonomous unmanned system will allow mine workers to autonomously determine the structural integrity of the roof and pillars through the generation of high-fidelity 3D maps. The generation of the maps will allow the miners to rapidly respond to any increasing hazards with proactive measures such as: sending workers to build/rebuild support structure to prevent accidents. The objective of this research is the development, implementation and testing of a robust unmanned ground vehicle (UGV) that will operate in mine environments for extended periods of time. To achieve this, a custom skid-steer four-wheeled UGV is designed to operate in these challenging underground mine environments. To autonomously navigate these environments, the UGV employs the use of a Light Detection and Ranging (LiDAR) and tactical grade inertial measurement unit (IMU) for the localization and mapping through a tightly-coupled LiDAR Inertial Odometry via Smoothing and Mapping framework (LIO-SAM). The autonomous navigation module was implemented based upon the Fast likelihood-based collision avoidance with an extension to human-guided navigation and a terrain traversability analysis framework. In order to successfully operate and generate high-fidelity 3D maps, the system was rigorously tested in different environments and terrain to verify its robustness. To assess the capabilities, several localization, mapping and autonomous navigation missions were carried out in a coal mine environment. These tests allowed for the verification and tuning of the system to be able to successfully autonomously navigate and generate high-fidelity maps

On the Uncertainty in Active SLAM: Representation, Propagation and Monotonicity

La localización y mapeo simultáneo activo (SLAM activo) ha recibido mucha atención por parte de la comunidad de robótica por su relevancia en aplicaciones de robot móviles. El objetivo de un algoritmo de SLAM activo es planificar la trayectoria del robot para maximizar el área explorada y minimizar la incertidumbre asociada con la estimación de la posición del robot. Durante la fase de exploración de un algoritmo de SLAM, donde el robot navega en una región previamente desconocida, la incertidumbre asociada con la localización del robot crece sin límites. Solo después de volver a visitar regiones previamente conocidas, se espera una reducción en la incertidumbre asociada con la localización del robot mediante la detección de cierres de bucle. Esta tesis doctoral se centra en la importancia de representar y cuantificar la incertidumbre para calcular correctamente la confianza asociada con la estimación de la localización del robot en cada paso de tiempo a lo largo de su recorrido y, por lo tanto, decidir la trayectoria correcta de acuerdo con el objetivo de SLAM activo.En la literatura, se han propuesto fundamentalemente dos tipos de modelos de representación de la incertidumbre: absoluta y diferencial. En representación absoluta, la información sobre la incertidumbre asociada con la localización del robot está representada por una función de distribución de probabilidad, generalmente gausiana, sobre las variables de localización absoluta con respecto a una referencia base elegida. La estimación de la posición del robot está dada por la esperanza de las variables asociadas con la localización y la incertidumbre por su matriz de covarianza asociada. La representación diferencial utiliza una representación local de la incertidumbre, la posición estimada del robot se representa mediante la mejor aproximación de la posición absoluta y el error de estimación se representa localmente mediante un vector diferencial. Este vector generalmente también está representado por una función de distribución de probabilidad gausiana. Representaciones equivalentes al modelo diferencial han utilizado las herramientas de Grupos de Lie y Álgebras de Lie para representar la incertidumbre. Además de estos modelos, existen diferentes formas de representar la posición y orientación de la posición del robot, ángulos de Euler, cuaterniones y transformaciones homogéneas.Los enfoques más comunes para cuantificar la incertidumbre en SLAM se basan en criterios de optimalidad con el objetivo de cuantificar el mapa y la incertidumbre de la posición del robot: A-opt (traza de la matriz de covarianza, o suma de sus autovalores), D-opt (determinante de la matriz de covarianza, o producto de sus autovalores) y E-opt (criterio del mayor autovalor). Alternativamente, otros algoritmos de SLAM activo, basados en la Teoría de la Información, se basan en el uso de la entropía de Shannon para seleccionar acciones que lleven al robot al objetivo seleccionado. En un escenario de SLAM activo, garantizar la monotonicidad de estos criterios en la toma de decisiones durante la exploración, es decir, cuantificar correctamente que la incertidumbre encapsulada en una matriz de covarianza está aumentando, es un paso esencial para tomar decisiones correctas. Como ya se ha mencionado, durante la fase de exploración la incertidumbre asociada con la localización del robot aumenta. Por lo tanto, si no se preserva la monotonicidad de los criterios considerados, el sistema puede seleccionar trayectorias o caminos que creen falsamente que conducen a una menor incertidumbre de la localización del robot.En esta tesis, revisamos el trabajo relacionado sobre representación y propagación de la incertidumbre de la posición del robot en los diferentes modelos propuestos en la literatura. Además, se lleva a cabo un análisis de la incertidumbre representada localmente con un vector diferencial y la incertidumbre representada usando grupos de Lie. Investigamos la monotonicidad de diferentes criterios para la toma de decisiones, tanto en 2D como en 3D, dependiendo de la representación de la incertidumbre y de la representación de la orientación del robot. Nuestra conclusión fundamental es que la representación de la incertidumbre sobre grupos de Lie y usando un vector diferencial son similares e independientes de la representación utilizada para la parte rotacional de la posición del robot. Esto se debe a que la incertidumbre se representa localmente en el espacio de las transformaciones diferenciales que se corresponde con el álgebra de Lie del grupo euclidiano especial SE(n). Sin embargo, en el espacio tridimensional, la estimación de la localización del robot depende de las diferentes formas de representación de la parte rotacional. Por lo tanto, una forma adecuada de manipular conjuntamente la estimación y la incertidumbre del robot es utilizando la teoría de grupos de Lie debido a que es una representación que garantiza propiedades tales como una representación mínima y libre de singularidades en los ángulos de rotación. Analíticamente, demostramos que, utilizando representaciones diferenciales para la propagación de la incertidumbre, la monotonicidad se conserva para todos los criterios de optimalidad, A-opt, D-opt y E-opt y para la entropía de Shannon. También demostramos que la monotonicidad no se cumple para ninguno de ellos en representaciones absolutas usando ángulos Roll-Pitch-Yaw y Euler. Finalmente, mostramos que al usar cuaterniones unitarios en representaciones absolutas, los únicos criterios que preservan la monotonicidad son D-opt y la entropía de Shannon.Estos hallazgos pueden guiar a los investigadores de SLAM activo a seleccionar adecuadamente un modelo de representación de la incertidumbre, de modo que la planificación de trayectorias y los algoritmos de exploración puedan evaluar correctamente la evolución de la incertidumbre asociada a la posición del robot.Active Simultaneous Localization and Mapping (Active SLAM) has received a lot of attention from the robotics community for its relevance in mobile robotics applications. The objective of an active SLAM algorithm is to plan ahead the robot motion in order to maximize the area explored and minimize the uncertainty associated with the estimation, all within a time and computation budget. During the exploration phase of a SLAM algorithm, where the robot navigates in a previously unknown region, the uncertainty associated with the robot's localization grows unbounded. Only after revisiting previously known regions a reduction in the robot's localization uncertainty is expected by detecting loop-closures. This doctoral thesis focuses on the paramount importance of representing and quantifying uncertainty to correctly report the associated confidence of the robot's location estimate at each time step along its trajectory and therefore deciding the correct course of action in an active SLAM mission. Two fundamental types of models of probabilistic representation of the uncertainty have been proposed in the literature: absolute and dfferential. In absolute representations, the information about the uncertainty in the location of the robot's pose is represented by a probability distribution function, usually Gaussian, over the variables of the absolute location with respect to a chosen base reference. The estimated location is given by the expected location variables and the uncertainty by its associated covariance matrix. Differential representations use a local representation of the uncertainty, the estimated location of the robot is represented by the best approximation of the absolute location and the estimation error is represented locally by a differential location vector. This vector is usually also represented by a Gaussian probability distribution function. Equivalent representations to differential models have used the tools of Lie groups and Lie algebras to represent uncertainties. In addition to uncertainty models, there are different ways to represent the position and orientation of the robot's pose, Euler angles, quaternions and homogeneous transformations. The most common approaches to quantifying uncertainty in SLAM are based on optimality criteria which aim at quantifying the map and robot's pose uncertainty, namely A-opt (trace of the covariance matrix, or sum of its eigenvalues), D-opt (determinant of the covariance matrix, or product of its eigenvalues) and E-opt (largest eigenvalue) criteria. Alternatively, other active SLAM algorithms, based on Information Theory, rely on the use of the Shannon's entropy to select courses of action for the robot to reach the commanded goal location. In an active SLAM scenario, guaranteeing monotonicity of these decision making criteria during exploration, i.e. quantifying correctly that the uncertainty encapsulated in a covariance matrix is increasing, is an essential step towards making correct decisions. As already mentioned, during exploration the uncertainty associated with the robot's localization increases. Therefore, if monotonicity of the criteria considered is not preserved, the system might select courses of action or paths that it falsely believes lead to less uncertainty in the robot. In this thesis, we review related work about representation and propagation of the uncertainty of robot's pose and present a survey of different types of models proposed in the literature. Additionally, an analysis of the uncertainty represented with a differential uncertainty vector and the uncertainty represented on Lie groups is carried out. We investigate the monotonicity of different decision making criteria, both in 2D and 3D, depending on the representation of uncertainty and the orientation of the robot's pose. Our fundamental conclusion is that uncertainty representation over Lie groups and using differential location vectors are similar and independent of the representation used for rotational part of the robot's pose. This is due to the uncertainty is represented locally in the space of differential transformations for translation and rotation that correspond with the Lie algebra of special Euclidean group SE(n). However, in 3-dimensional space, the homogeneous transformation associated to the approximation of the real location depend on the different ways of representation the rotational part. Therefore, a proper way to jointly manipulating the estimation and uncertainty of the pose is to use the theory of Lie groups due to it is a representation to guarantee properties such as a minimal representation and free of singularities in rotation angles. We analytically show that, using differential representations to propagate spatial uncertainties, monotonicity is preserved for all optimality criteria, A-opt, D-opt and E-opt and for Shannon's entropy. We also show that monotonicity does not hold for any of them in absolute representations using Roll-Pitch-Yaw and Euler angles. Finally, we show that using unit quaternions in absolute representations, the only criteria that preserve monotonicity are D-opt and Shannon's entropy. These findings can guide active SLAM researchers to adequately select a representation model for uncertainty, so that path planning and exploration algorithms can correctly assess the evolution of location uncertainty.<br /