Sistema de soporte al desarrollo de un planificador de misiones en un vehículo aéreo no tripulado

En esta memoria se describe el trabajo de construcción de una arquitectura software diseñada para facilitar el desarrollo un planificador de misión de un vehículo aéreo no tripulado (UAV), con el fin de que éste alcance los objetivos marcados en la competición internacional de robótica IARC (séptima edición). A lo largo de la memoria, se describe en primer lugar, una revisión de técnicas de robótica inteligente aplicadas a la construcción de vehículos aéreos no tripulados, en el que se ven los diferentes paradigmas de programación de la robótica inteligente y la clasificación de dichos robots aéreos, dependiendo de su autonomía. Este descripción finaliza con la presentación del problema correspondiente a la competición IARC. A continuación se describe el diseño realizado para soporte al desarrollo de un planificador de misiones de UAVs, con simulación de comportamiento de vehículos robóticos y visualización 3D con movimiento. Finalmente, se muestran las pruebas que se han realizado para validar la construcción de dicha arquitectura software. ---ABSTRACT---In this report it is presented the construction of a software architecture, designed to facilitate the development of a mission planner for an unmanned aerial vehicle (UAV), so that it reaches the goals set in the International Aerial Robotics Competition - IARC (seventh edition). Throughout this report, it is described first, a review of intelligent robotics techniques applied to the construction of unmanned aerial vehicles, where different paradigms of intelligent robotics are seen, along with a classification of such aerial robots, depending on their autonomy. Description ends with the presentation of the problem corresponding to the IARC competition. Following, it is described the design made to satisfy the support to the development of a mission planner for UAV´s, with a simulation of the robotics vehicles’ behaviours and a 3D display with motion. Finally, we will deal with the tests that have been conducted to validate the construction of the software architecture.

Control reactivo de vehículo aéreo no tripulado para la competición internacional IARC

El trabajo está centrado en la construcción de una simulación y en el desarrollo de un control reactivo para un vehículo aéreo no tripulado con fin de participar en la séptima edición de la competición internacional IARC. Para cumplir los objetivos de la competición se van a estudiar técnicas existentes de inteligencia artificial aplicadas al control de vehículos aéreos no tripulados, así como las técnicas para la elaboración de un modelo de simulación realista sobre el que realizar las distintas pruebas. Por último, se explica el trabajo realizado para crear un controlador reactivo que satisface las reglas de la competición y permite al vehículo aéreo no tripulado operar de forma autónoma en el ambiente de la simulación. Para validar el comportamiento, se realizan casos de prueba y un estudio de los resultados.---ABSTRACT---This report is focused on the construction of a simulation and the development of a reactive control for an unmanned aerial vehicle in order to participate in the seventh edition of the international competition IARC. Artificial intelligence techniques applied to the control of unmanned aerial vehicles are going to be studied to meet the objectives of the competition, as well as techniques for developing a realistic simulation model on which to perform the different tests. Finally, the last part of the report explains the work accomplished to create a reactive controller that meets the rules of the competition and allows the unmanned aerial vehicle to operate autonomously in the simulation environment. Test cases and a study of the results is performed to validate the behavior.

Vision-Based Tracking, Odometry and Control for UAV Autonomy

El principal objetivo de este trabajo es proporcionar una solución en tiempo real basada en visión estéreo o monocular precisa y robusta para que un vehículo aéreo no tripulado (UAV) sea autónomo en varios tipos de aplicaciones UAV, especialmente en entornos abarrotados sin señal GPS. Este trabajo principalmente consiste en tres temas de investigación de UAV basados en técnicas de visión por computador: (I) visual tracking, proporciona soluciones efectivas para localizar visualmente objetos de interés estáticos o en movimiento durante el tiempo que dura el vuelo del UAV mediante una aproximación adaptativa online y una estrategia de múltiple resolución, de este modo superamos los problemas generados por las diferentes situaciones desafiantes, tales como cambios significativos de aspecto, iluminación del entorno variante, fondo del tracking embarullado, oclusión parcial o total de objetos, variaciones rápidas de posición y vibraciones mecánicas a bordo. La solución ha sido utilizada en aterrizajes autónomos, inspección de plataformas mar adentro o tracking de aviones en pleno vuelo para su detección y evasión; (II) odometría visual: proporciona una solución eficiente al UAV para estimar la posición con 6 grados de libertad (6D) usando únicamente la entrada de una cámara estéreo a bordo del UAV. Un método Semi-Global Blocking Matching (SGBM) eficiente basado en una estrategia grueso-a-fino ha sido implementada para una rápida y profunda estimación del plano. Además, la solución toma provecho eficazmente de la información 2D y 3D para estimar la posición 6D, resolviendo de esta manera la limitación de un punto de referencia fijo en la cámara estéreo. Una robusta aproximación volumétrica de mapping basada en el framework Octomap ha sido utilizada para reconstruir entornos cerrados y al aire libre bastante abarrotados en 3D con memoria y errores correlacionados espacialmente o temporalmente; (III) visual control, ofrece soluciones de control prácticas para la navegación de un UAV usando Fuzzy Logic Controller (FLC) con la estimación visual. Y el framework de Cross-Entropy Optimization (CEO) ha sido usado para optimizar el factor de escala y la función de pertenencia en FLC. Todas las soluciones basadas en visión en este trabajo han sido probadas en test reales. Y los conjuntos de datos de imágenes reales grabados en estos test o disponibles para la comunidad pública han sido utilizados para evaluar el rendimiento de estas soluciones basadas en visión con ground truth. Además, las soluciones de visión presentadas han sido comparadas con algoritmos de visión del estado del arte. Los test reales y los resultados de evaluación muestran que las soluciones basadas en visión proporcionadas han obtenido rendimientos en tiempo real precisos y robustos, o han alcanzado un mejor rendimiento que aquellos algoritmos del estado del arte. La estimación basada en visión ha ganado un rol muy importante en controlar un UAV típico para alcanzar autonomía en aplicaciones UAV. ABSTRACT The main objective of this dissertation is providing real-time accurate robust monocular or stereo vision-based solution for Unmanned Aerial Vehicle (UAV) to achieve the autonomy in various types of UAV applications, especially in GPS-denied dynamic cluttered environments. This dissertation mainly consists of three UAV research topics based on computer vision technique: (I) visual tracking, it supplys effective solutions to visually locate interesting static or moving object over time during UAV flight with on-line adaptivity approach and multiple-resolution strategy, thereby overcoming the problems generated by the different challenging situations, such as significant appearance change, variant surrounding illumination, cluttered tracking background, partial or full object occlusion, rapid pose variation and onboard mechanical vibration. The solutions have been utilized in autonomous landing, offshore floating platform inspection and midair aircraft tracking for sense-and-avoid; (II) visual odometry: it provides the efficient solution for UAV to estimate the 6 Degree-of-freedom (6D) pose using only the input of stereo camera onboard UAV. An efficient Semi-Global Blocking Matching (SGBM) method based on a coarse-to-fine strategy has been implemented for fast depth map estimation. In addition, the solution effectively takes advantage of both 2D and 3D information to estimate the 6D pose, thereby solving the limitation of a fixed small baseline in the stereo camera. A robust volumetric occupancy mapping approach based on the Octomap framework has been utilized to reconstruct indoor and outdoor large-scale cluttered environments in 3D with less temporally or spatially correlated measurement errors and memory; (III) visual control, it offers practical control solutions to navigate UAV using Fuzzy Logic Controller (FLC) with the visual estimation. And the Cross-Entropy Optimization (CEO) framework has been used to optimize the scaling factor and the membership function in FLC. All the vision-based solutions in this dissertation have been tested in real tests. And the real image datasets recorded from these tests or available from public community have been utilized to evaluate the performance of these vision-based solutions with ground truth. Additionally, the presented vision solutions have compared with the state-of-art visual algorithms. Real tests and evaluation results show that the provided vision-based solutions have obtained real-time accurate robust performances, or gained better performance than those state-of-art visual algorithms. The vision-based estimation has played a critically important role for controlling a typical UAV to achieve autonomy in the UAV application.

Estudio, ensamblaje, caracterización y ensayos de dos modelos reales de RPA

Un dron o un RPA (del inglés, Remote Piloted Aircraft) es un vehículo aéreo no tripulado capaz de despegar, volar y aterrizar de forma autónoma, semiautónoma o manual, siempre con control remoto. Además, toda aeronave de estas características debe ser capaz de mantener un nivel de vuelo controlado y sostenido. A lo largo de los años, estos aparatos han ido evolución tanto en aplicaciones como en su estética y características físicas, siempre impulsado por los requerimientos militares en cada momento. Gracias a este desarrollo, hoy en día los drones son uno más en la sociedad y desempeñan tareas que para cualquier ser humano serían peligrosas o difíciles de llevar a cabo. Debido a la reciente proliferación de los RPA, los gobiernos de los distintos países se han visto obligados a redactar nuevas leyes y/o modificar las ya existentes en relación a los diferentes usos del espacio aéreo para promover la convivencia de estas aeronaves con el resto de vehículos aéreos. El objeto principal de este proyecto es ensamblar, caracterizar y configurar dos modelos reales de dron: el DJI F450 y el TAROT t810. Para conseguir un montaje apropiado a las aplicaciones posteriores que se les va a dar, antes de su construcción se ha realizado un estudio individualizado en detalle de cada una de las partes y módulos que componen estos vehículos. Adicionalmente, se ha investigado acerca de los distintos tipos de sistemas de transmisión de control remoto, vídeo y telemetría, sin dejar de lado las baterías que impulsarán al aparato durante sus vuelos. De este modo, es sabido que los RPA están compuestos por distintos módulos operativos: los principales, todo aquel módulo para que el aparato pueda volar, y los complementarios, que son aquellos que dotan a cada aeronave de características adicionales y personalizadas que lo hacen apto para diferentes usos. A lo largo de este proyecto se han instalado y probado diferentes módulos adicionales en cada uno de los drones, además de estar ambos constituidos por distintos bloques principales, incluyendo el controlador principal: NAZA-M Lite instalado en el dron DJI F450 y NAZA-M V2 incorporado en el TAROT t810. De esta forma se ha podido establecer una comparativa real acerca del comportamiento de éstos, tanto de forma conjunta como de ambos controladores individualmente. Tras la evaluación experimental obtenida tras diversas pruebas de vuelo, se puede concluir que ambos modelos de controladores se ajustan a las necesidades demandadas por el proyecto y sus futuras aplicaciones, siendo más apropiada la elección del modelo M Lite por motivos estrictamente económicos, ya que su comportamiento en entornos recreativos es similar al modelo M V2. ABSTRACT. A drone or RPA (Remote Piloted Aircraft) is an unmanned aerial vehicle that is able to take off, to fly and to land autonomously, semi-autonomously or manually, always connected via remote control. In addition, these aircrafts must be able to keep a controlled and sustained flight level. Over the years, the applications for these devices have evolved as much as their aesthetics and physical features both boosted by the military needs along time. Thanks to this development, nowadays drones are part of our society, executing tasks potentially dangerous or difficult to complete by humans. Due to the recent proliferation of RPA, governments worldwide have been forced to draft legislation and/or modify the existing ones about the different uses of the aerial space to promote the cohabitation of these aircrafts with the rest of the aerial vehicles. The main objective of this project is to assemble, to characterize and to set-up two real drone models: DJI F450 and TAROT t810. Before constructing the vehicles, a detailed study of each part and module that composes them has been carried out, in order to get an appropriate structure for their expected uses. Additionally, the different kinds of remote control, video and telemetry transmission systems have been investigated, including the batteries that will power the aircrafts during their flights. RPA are made of several operative modules: main modules, i.e. those which make the aircraft fly, and complementary modules, that customize each aircraft and equip them with additional features, making them suitable for a particular use. Along this project, several complementary modules for each drone have been installed and tested. Furthermore, both are built from different main units, including the main controller: NAZA-M Lite installed on DJI F450 and NAZA-M V2 on board of TAROT t810. This way, it has been possible to establish an accurate comparison, related to the performance of both models, not only jointly but individually as well. After several flight tests and an experimental evaluation, it can be concluded that both main controller models are suitable for the requirements fixed for the project and the future applications, being more appropriate to choose the M Lite model strictly due to economic reasons, as its performance in recreational environment is similar to the M V2.

Omnidirectional bearing-only see-and-avoid for small aerial robots:

Motivated by the growing interest in unmanned aerial system's applications in indoor and outdoor settings and the standardisation of visual sensors as vehicle payload. This work presents a collision avoidance approach based on omnidirectional cameras that does not require the estimation of range between two platforms to resolve a collision encounter. It will achieve a minimum separation between the two vehicles involved by maximising the view-angle given by the omnidirectional sensor. Only visual information is used to achieve avoidance under a bearing-only visual servoing approach. We provide theoretical problem formulation, as well as results from real flight using small quadrotors

A Multiresolution Image Alignment Technique Based on Direct Methods for Pose Estimation of Aerial Vehicles

In this paper, we seek to expand the use of direct methods in real-time applications by proposing a vision-based strategy for pose estimation of aerial vehicles. The vast majority of approaches make use of features to estimate motion. Conversely, the strategy we propose is based on a MR (Multi-Resolution) implementation of an image registration technique (Inverse Compositional Image Alignment ICIA) using direct methods. An on-board camera in a downwards-looking configuration, and the assumption of planar scenes, are the bases of the algorithm. The motion between frames (rotation and translation) is recovered by decomposing the frame-to-frame homography obtained by the ICIA algorithm applied to a patch that covers around the 80% of the image. When the visual estimation is required (e.g. GPS drop-out), this motion is integrated with the previous known estimation of the vehicles' state, obtained from the on-board sensors (GPS/IMU), and the subsequent estimations are based only on the vision-based motion estimations. The proposed strategy is tested with real flight data in representative stages of a flight: cruise, landing, and take-off, being two of those stages considered critical: take-off and landing. The performance of the pose estimation strategy is analyzed by comparing it with the GPS/IMU estimations. Results show correlation between the visual estimation obtained with the MR-ICIA and the GPS/IMU data, that demonstrate that the visual estimation can be used to provide a good approximation of the vehicle's state when it is required (e.g. GPS drop-outs). In terms of performance, the proposed strategy is able to maintain an estimation of the vehicle's state for more than one minute, at real-time frame rates based, only on visual information.

Aerial Remote Sensing in Agriculture: A Practical Approach to Area Coverage and Path Planning for Fleets of Mini Aerial Robots

In this paper, a system that allows applying precision agriculture techniques is described. The application is based on the deployment of a team of unmanned aerial vehicles that are able to take georeferenced pictures in order to create a full map by applying mosaicking procedures for postprocessing. The main contribution of this work is practical experimentation with an integrated tool. Contributions in different fields are also reported. Among them is a new one-phase automatic task partitioning manager, which is based on negotiation among the aerial vehicles, considering their state and capabilities. Once the individual tasks are assigned, an optimal path planning algorithm is in charge of determining the best path for each vehicle to follow. Also, a robust flight control based on the use of a control law that improves the maneuverability of the quadrotors has been designed. A set of field tests was performed in order to analyze all the capabilities of the system, from task negotiations to final performance. These experiments also allowed testing control robustness under different weather conditions.

A General Purpose Configurable Controller for Indoors and Outdoors GPS-Denied Navigation for Multirotor Unmanned Aerial Vehicles

This research on odometry based GPS-denied navigation on multirotor Unmanned Aerial Vehicles is focused among the interactions between the odometry sensors and the navigation controller. More precisely, we present a controller architecture that allows to specify a speed specified flight envelope where the quality of the odometry measurements is guaranteed. The controller utilizes a simple point mass kinematic model, described by a set of configurable parameters, to generate a complying speed plan. For experimental testing, we have used down-facing camera optical-flow as odometry measurement. This work is a continuation of prior research to outdoors environments using an AR Drone 2.0 vehicle, as it provides reliable optical flow on a wide range of flying conditions and floor textures. Our experiments show that the architecture is realiable for outdoors flight on altitudes lower than 9 m. A prior version of our code was utilized to compete in the International Micro Air Vehicle Conference and Flight Competition IMAV 2012. The code will be released as an open-source ROS stack hosted on GitHub.

A General Purpose Configurable Navigation Controller for Micro Aerial Multirotor Vehicles

In this paper, we consider the problem of autonomous navigation of multirotor platforms in GPS-denied environments. The focus of this work is on safe navigation based on unperfect odometry measurements, such as on-board optical flow measurements. The multirotor platform is modeled as a flying object with specific kinematic constraints that must be taken into account in order to obtain successful results. A navigation controller is proposed featuring a set of configurable parameters that allow, for instance, to have a configuration setup for fast trajectory following, and another to soften the control laws and make the vehicle navigation more precise and slow whenever necessary. The proposed controller has been successfully implemented in two different multirotor platforms with similar sensoring capabilities showing the openness and tolerance of the approach. This research is focused around the Computer Vision Group's objective of applying multirotor vehicles to civilian service applications. The presented work was implemented to compete in the International Micro Air Vehicle Conference and Flight Competition IMAV 2012, gaining two awards: the Special Award on "Best Automatic Performance - IMAV 2012" and the second overall prize in the participating category "Indoor Flight Dynamics - Rotary Wing MAV". Most of the code related to the present work is available as two open-source projects hosted in GitHub.

Analysis of The Key Parameters in Pedestrian Safety Assessment for the European Vehicle Fleet Using Multibody and Finite Element Mathematical Models

Esta tesis investiga cuales son los parámetros más críticos que condicionan los resultados que obtienen en los ensayos de protección de peatones la flota Europea de vehículos, según la reglamentación europea de protección de peatones de 2003 (Directiva CE 2003/102) y el posterior Reglamento de 2009 (Reglamento CE 2009/78). En primer lugar se ha analizado el contexto de la protección de peatones en Europa, viendo la historia de las diferentes propuestas de procedimientos de ensayo así como los cambios (y las razones de los mismos) que han sufrido a lo largo del proceso de definición de la normativa Europea. Con la información disponible de más de 400 de estos ensayos se han desarrollado corredores de rigidez para los frontales de los diferentes segmentos de la flota de vehículos europea, siendo este uno de los resultados más relevantes de esta tesis. Posteriormente, esta tesis ha realizado un estudio accidentológico en detalle de los escenarios de atropello de peatones, identificando sus características más relevantes, los grupos de población con mayor riesgo y los tipos de lesiones más importantes que aparecen (en frecuencia y severidad), que han sentado las bases para analizar con modelos matemáticos hasta qué punto los métodos de ensayo propuestos realmente tienen estos factores en cuenta. Estos análisis no habrían sido posibles sin el desarrollo de las nuevas herramientas que se presentan en esta tesis, que permiten construir instantáneamente el modelo matemático de cualquier vehículo y cualquier peatón adulto para analizar su iteración. Así, esta tesis ha desarrollado una metodología rápida para desarrollar modelos matemáticos de vehículos a demanda, de cualquier marca y modelo y con las características geométricas y de rigidez deseados que permitan representarlo matemáticamente y del mismo modo, ha investigado cómo evoluciona el comportamiento del cuerpo humano durante el envejecimiento y ha implementado una funcionalidad de escalado en edad al modelo de peatón en multicuerpo de MADYMO (ya escalable en tamaño) para permitir modelar ad hoc cualquier peatón adulto (en género y edad). Finalmente, esta tesis también ha realizado, utilizando modelos de elementos finitos del cuerpo humano, diferentes estudios sobre la biomecánica de las lesiones más frecuentes de este tipo de accidentes, (en piernas y cabeza) con el objetivo de mejorar los procedimientos de ensayo para que predigan mejor el tipo de lesiones que se quieren evitar. Con el marco temporal y las condiciones de contorno de esta tesis se han centrado los esfuerzos en reforzar algunos aspectos críticos pero puntuales sobre cómo mejorar el ensayo de cabeza y, sobretodo, en proponer soluciones viables y con un valor añadido real al ensayo de pierna contra parachoques, sin cambiar la esencia del mismo pero proponiendo un nuevo impactador mejorado que incorpore una masa extra que representa a la parte superior del cuerpo y sea válido para toda la flota europea de vehículos independiente de la geometría de su frontal.

Video analysis based vehicle detection and tracking using an MCMC sampling framework

This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is dened. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences.

Analysis of Thematic Classified Aerial Images Trough Multispectral and LIDAR Data

The application of thematic maps obtained through the classification of remote images needs the obtained products with an optimal accuracy. The registered images from the airplanes display a very satisfactory spatial resolution, but the classical methods of thematic classification not always give better results than when the registered data from satellite are used. In order to improve these results of classification, in this work, the LIDAR sensor data from first return (Light Detection And Ranging) registered simultaneously with the spectral sensor data from airborne are jointly used. The final results of the thematic classification of the scene object of study have been obtained, quantified and discussed with and without LIDAR data, after applying different methods: Maximum Likehood Classification, Support Vector Machine with four different functions kernel and Isodata clustering algorithm (ML, SVM-L, SVM-P, SVM-RBF, SVM-S, Isodata). The best results are obtained for SVM with Sigmoide kernel. These allow the correlation with others different physical parameters with great interest like Manning hydraulic coefficient, for their incorporation in a GIS and their application in hydraulic modeling.

Vision-based vehicle detection and tracking with a mobile camera using a statistical framework

En esta tesis se aborda la detección y el seguimiento automático de vehículos mediante técnicas de visión artificial con una cámara monocular embarcada. Este problema ha suscitado un gran interés por parte de la industria automovilística y de la comunidad científica ya que supone el primer paso en aras de la ayuda a la conducción, la prevención de accidentes y, en última instancia, la conducción automática. A pesar de que se le ha dedicado mucho esfuerzo en los últimos años, de momento no se ha encontrado ninguna solución completamente satisfactoria y por lo tanto continúa siendo un tema de investigación abierto. Los principales problemas que plantean la detección y seguimiento mediante visión artificial son la gran variabilidad entre vehículos, un fondo que cambia dinámicamente debido al movimiento de la cámara, y la necesidad de operar en tiempo real. En este contexto, esta tesis propone un marco unificado para la detección y seguimiento de vehículos que afronta los problemas descritos mediante un enfoque estadístico. El marco se compone de tres grandes bloques, i.e., generación de hipótesis, verificación de hipótesis, y seguimiento de vehículos, que se llevan a cabo de manera secuencial. No obstante, se potencia el intercambio de información entre los diferentes bloques con objeto de obtener el máximo grado posible de adaptación a cambios en el entorno y de reducir el coste computacional. Para abordar la primera tarea de generación de hipótesis, se proponen dos métodos complementarios basados respectivamente en el análisis de la apariencia y la geometría de la escena. Para ello resulta especialmente interesante el uso de un dominio transformado en el que se elimina la perspectiva de la imagen original, puesto que este dominio permite una búsqueda rápida dentro de la imagen y por tanto una generación eficiente de hipótesis de localización de los vehículos. Los candidatos finales se obtienen por medio de un marco colaborativo entre el dominio original y el dominio transformado. Para la verificación de hipótesis se adopta un método de aprendizaje supervisado. Así, se evalúan algunos de los métodos de extracción de características más populares y se proponen nuevos descriptores con arreglo al conocimiento de la apariencia de los vehículos. Para evaluar la efectividad en la tarea de clasificación de estos descriptores, y dado que no existen bases de datos públicas que se adapten al problema descrito, se ha generado una nueva base de datos sobre la que se han realizado pruebas masivas. Finalmente, se presenta una metodología para la fusión de los diferentes clasificadores y se plantea una discusión sobre las combinaciones que ofrecen los mejores resultados. El núcleo del marco propuesto está constituido por un método Bayesiano de seguimiento basado en filtros de partículas. Se plantean contribuciones en los tres elementos fundamentales de estos filtros: el algoritmo de inferencia, el modelo dinámico y el modelo de observación. En concreto, se propone el uso de un método de muestreo basado en MCMC que evita el elevado coste computacional de los filtros de partículas tradicionales y por consiguiente permite que el modelado conjunto de múltiples vehículos sea computacionalmente viable. Por otra parte, el dominio transformado mencionado anteriormente permite la definición de un modelo dinámico de velocidad constante ya que se preserva el movimiento suave de los vehículos en autopistas. Por último, se propone un modelo de observación que integra diferentes características. En particular, además de la apariencia de los vehículos, el modelo tiene en cuenta también toda la información recibida de los bloques de procesamiento previos. El método propuesto se ejecuta en tiempo real en un ordenador de propósito general y da unos resultados sobresalientes en comparación con los métodos tradicionales. ABSTRACT This thesis addresses on-road vehicle detection and tracking with a monocular vision system. This problem has attracted the attention of the automotive industry and the research community as it is the first step for driver assistance and collision avoidance systems and for eventual autonomous driving. Although many effort has been devoted to address it in recent years, no satisfactory solution has yet been devised and thus it is an active research issue. The main challenges for vision-based vehicle detection and tracking are the high variability among vehicles, the dynamically changing background due to camera motion and the real-time processing requirement. In this thesis, a unified approach using statistical methods is presented for vehicle detection and tracking that tackles these issues. The approach is divided into three primary tasks, i.e., vehicle hypothesis generation, hypothesis verification, and vehicle tracking, which are performed sequentially. Nevertheless, the exchange of information between processing blocks is fostered so that the maximum degree of adaptation to changes in the environment can be achieved and the computational cost is alleviated. Two complementary strategies are proposed to address the first task, i.e., hypothesis generation, based respectively on appearance and geometry analysis. To this end, the use of a rectified domain in which the perspective is removed from the original image is especially interesting, as it allows for fast image scanning and coarse hypothesis generation. The final vehicle candidates are produced using a collaborative framework between the original and the rectified domains. A supervised classification strategy is adopted for the verification of the hypothesized vehicle locations. In particular, state-of-the-art methods for feature extraction are evaluated and new descriptors are proposed by exploiting the knowledge on vehicle appearance. Due to the lack of appropriate public databases, a new database is generated and the classification performance of the descriptors is extensively tested on it. Finally, a methodology for the fusion of the different classifiers is presented and the best combinations are discussed. The core of the proposed approach is a Bayesian tracking framework using particle filters. Contributions are made on its three key elements: the inference algorithm, the dynamic model and the observation model. In particular, the use of a Markov chain Monte Carlo method is proposed for sampling, which circumvents the exponential complexity increase of traditional particle filters thus making joint multiple vehicle tracking affordable. On the other hand, the aforementioned rectified domain allows for the definition of a constant-velocity dynamic model since it preserves the smooth motion of vehicles in highways. Finally, a multiple-cue observation model is proposed that not only accounts for vehicle appearance but also integrates the available information from the analysis in the previous blocks. The proposed approach is proven to run near real-time in a general purpose PC and to deliver outstanding results compared to traditional methods.

Precision Agriculture without borders: Practical issues and improvements in farmland coverage with aerial vehicles.

This work presents a solution for the aerial coverage of a field by using a fleet of aerial vehicles. The use of Unmanned Aerial Vehicles allows to obtain high resolution mosaics to be used in Precision Agriculture techniques. This report is focus on providing a solution for the full simultaneous coverage problem taking into account restrictions as the required spatial resolution and overlap while maintaining similar light conditions and safety operation of the drones. Results obtained from real field tests are finally reported

Application of virtual certification techniques to vehicle design and track maintenance

Virtual certification partially substitutes by computer simulations the experimental techniques required for rail vehicle certification. In this paper, several works were these techniques were used in the vehicle design and track maintenance processes are presented. Dynamic simulation of multibody systems was used to virtually apply the EN14363 standard to certify the dynamic behaviour of vehicles. The works described are: assessment of a freight bogie design adapted to meter-gauge, assessment of a railway track layout for a subway network, freight bogie design with higher speed and axle load, and processing of the data acquired by a track recording vehicle for track maintenance.