Near real-time monitoring of buried oil pipeline right-of-way for third-party incursion

Many security systems employing different methods have been proposed to protect buried oil pipelines transporting petroleum products from the well head via the refinery to: depots and other receiving stations. Currently there is a security gap in the monitoring of these buried pipelines in real time and in keeping them protected from third party interference. This thesis addresses the problem of monitoring these systems by developing an automated image analysis system with the aid of a low-cost multisensory Unmanned Aerial Vehicle (UAV) for monitoring of buried pipeline right-of-way (ROW). The method used in this research is based on the identification of threat objects of interest from the video frame sequences of the pipeline right-of-way acquired by the UAV. This is achieved by training the system to recognise objects of interest using trained correlation filters. To determine the geographical location of detected objects, the Video frame sequences captured by the UAV platform were ortho-rectified to form ortho-images which were then mosaicked to form a seamless Digital Surface Model (DSM) covering the test area using a photogrammetry model. The DSM formed from the mosaicking of ortho-images is then emerged with a digital globe for geo-referencing of detected objects. Experiments were carried out on a test field located in United Kingdom and Nigeria, where video and telemetry data were collected, then processed using the techniques created in this research. The results demonstrated that the developed correlation filter was able to detect objects of interest despite the distortions that come with the object image, due to the fact that the expected distortion was compensated for using the training images. When compared with the 6 control points in the digital globe the accuracy of the two-dimension DSM gave a misalignment error of between 2 and 3 metres

Estabilización de vídeo en tiempo real : aplicaciones en teleoperación de micro vehículos aéreos de ala rotativa

Micro Aerial Vehicles (MAVs), a subset of Unmanned Aerial Vehicles (UAVs), also known as drones, are becoming popular for several applications and gaining interest due to advantages as manufacturing and maintenance cost, size and weight, energy consumption, and flight maneuverability. Required skills for drone teleoperators being lower than for aircraft pilots, however their training process can last several weeks or months depending on the target at hands. In particular, this process is harder when teleoperators cannot observe directly the vehicle, depending only on onboard sensors and cameras. The presence of oscillations in the captured video is a major problem with cameras on UAVs. It is even more complex for MAVs because the external disturbances increase the instability. There exists mechanical video stabilizers that reduce camera oscillations, however this mechanical device adds weight and increases the manufacturing cost, energy consumption, size, weight, and the system becomes less safe for people. In this thesis, we propose to develop video stabilization software algorithms, without additional mechanical elements in the system, to be applied in real-time during the UAV navigation. In the literature, there are a few video stabilization algorithms able to be applied in real-time, but most of them generate false motion (phantom movements) in the stabilized image. Our algorithm represents a good tradeoff between stable video recording and simultaneously keeping UAV real motion. Several experiments with MAVs have been performed and the employed measurements demonstrate the good performance of the introduced algorithm.Los micro vehículos aéreos (MAVs), un subconjunto de vehículos aéreos no tripulados (UAVs), también llamados drones, han ganado popularidad en múltiples aplicaciones y un creciente interés debido a sus ventajas como costo de fabricación y mantenimiento, volumen, peso del vehículo, gasto energético, y maniobrabilidad de vuelo. La destreza requerida para un teleoperador de drones es inferior a la de un piloto de aeronaves de mayor dimensión, no obstante, su proceso de entrenamiento puede durar varias semanas o incluso meses dependiendo del objetivo que se persiga. Este proceso se dificulta cuando el teleoperador no puede observar de forma directa al vehículo y depende únicamente de los sensores y cámaras a bordo del sistema. Uno de los principales problemas con cámaras a bordo de drones es la oscilación presente en los vídeos capturados. Este inconveniente es más complejo para los MAVs porque las perturbaciones externas provocan mayor inestabilidad. Existen dispositivos mecánicos de estabilización de vídeo que reducen las oscilaciones en la cámara. Sin embargo, estos mecanismos implican una carga adicional al sistema y aumentan el costo de producción, gasto energético y el riesgo para las personas que se encuentren cerca en caso de accidente. En la presente tesis se propone el desarrollo de algoritmos de estabilización de vídeo por software sin elementos mecánicos adicionales en el sistema, a ser utilizados en tiempo real durante la navegación de los UAVs. En la literatura existen pocos algoritmos de estabilización de video aplicables en tiempo real, los cuales generan falsos movimientos (movimientos fantasma) en la imagen estabilizada. El algoritmo desarrollado es capaz de obtener una imagen estable y simultáneamente mantener los movimientos reales. Se han llevado a cabo múltiples experimentos con MAVs y las métricas de evaluación utilizadas evidencian el buen desempeño del algoritmo introducido

Wide-Area Surveillance System using a UAV Helicopter Interceptor and Sensor Placement Planning Techniques

This project proposes and describes the implementation of a wide-area surveillance system comprised of a sensor/interceptor placement planning and an interceptor unmanned aerial vehicle (UAV) helicopter. Given the 2-D layout of an area, the planning system optimally places perimeter cameras based on maximum coverage and minimal cost. Part of this planning system includes the MATLAB implementation of Erdem and Sclaroff’s Radial Sweep algorithm for visibility polygon generation. Additionally, 2-D camera modeling is proposed for both fixed and PTZ cases. Finally, the interceptor is also placed to minimize shortest-path flight time to any point on the perimeter during a detection event. Secondly, a basic flight control system for the UAV helicopter is designed and implemented. The flight control system’s primary goal is to hover the helicopter in place when a human operator holds an automatic-flight switch. This system represents the first step in a complete waypoint-navigation flight control system. The flight control system is based on an inertial measurement unit (IMU) and a proportional-integral-derivative (PID) controller. This system is implemented using a general-purpose personal computer (GPPC) running Windows XP and other commercial off-the-shelf (COTS) hardware. This setup differs from other helicopter control systems which typically use custom embedded solutions or micro-controllers. Experiments demonstrate the sensor placement planning achieving \u3e90% coverage at optimized-cost for several typical areas given multiple camera types and parameters. Furthermore, the helicopter flight control system experiments achieve hovering success over short flight periods. However, the final conclusion is that the COTS IMU is insufficient for high-speed, high-frequency applications such as a helicopter control system

Map-Based Localization for Unmanned Aerial Vehicle Navigation

Unmanned Aerial Vehicles (UAVs) require precise pose estimation when navigating in indoor and GNSS-denied / GNSS-degraded outdoor environments. The possibility of crashing in these environments is high, as spaces are confined, with many moving obstacles. There are many solutions for localization in GNSS-denied environments, and many different technologies are used. Common solutions involve setting up or using existing infrastructure, such as beacons, Wi-Fi, or surveyed targets. These solutions were avoided because the cost should be proportional to the number of users, not the coverage area. Heavy and expensive sensors, for example a high-end IMU, were also avoided. Given these requirements, a camera-based localization solution was selected for the sensor pose estimation. Several camera-based localization approaches were investigated. Map-based localization methods were shown to be the most efficient because they close loops using a pre-existing map, thus the amount of data and the amount of time spent collecting data are reduced as there is no need to re-observe the same areas multiple times. This dissertation proposes a solution to address the task of fully localizing a monocular camera onboard a UAV with respect to a known environment (i.e., it is assumed that a 3D model of the environment is available) for the purpose of navigation for UAVs in structured environments. Incremental map-based localization involves tracking a map through an image sequence. When the map is a 3D model, this task is referred to as model-based tracking. A by-product of the tracker is the relative 3D pose (position and orientation) between the camera and the object being tracked. State-of-the-art solutions advocate that tracking geometry is more robust than tracking image texture because edges are more invariant to changes in object appearance and lighting. However, model-based trackers have been limited to tracking small simple objects in small environments. An assessment was performed in tracking larger, more complex building models, in larger environments. A state-of-the art model-based tracker called ViSP (Visual Servoing Platform) was applied in tracking outdoor and indoor buildings using a UAVs low-cost camera. The assessment revealed weaknesses at large scales. Specifically, ViSP failed when tracking was lost, and needed to be manually re-initialized. Failure occurred when there was a lack of model features in the cameras field of view, and because of rapid camera motion. Experiments revealed that ViSP achieved positional accuracies similar to single point positioning solutions obtained from single-frequency (L1) GPS observations standard deviations around 10 metres. These errors were considered to be large, considering the geometric accuracy of the 3D model used in the experiments was 10 to 40 cm. The first contribution of this dissertation proposes to increase the performance of the localization system by combining ViSP with map-building incremental localization, also referred to as simultaneous localization and mapping (SLAM). Experimental results in both indoor and outdoor environments show sub-metre positional accuracies were achieved, while reducing the number of tracking losses throughout the image sequence. It is shown that by integrating model-based tracking with SLAM, not only does SLAM improve model tracking performance, but the model-based tracker alleviates the computational expense of SLAMs loop closing procedure to improve runtime performance. Experiments also revealed that ViSP was unable to handle occlusions when a complete 3D building model was used, resulting in large errors in its pose estimates. The second contribution of this dissertation is a novel map-based incremental localization algorithm that improves tracking performance, and increases pose estimation accuracies from ViSP. The novelty of this algorithm is the implementation of an efficient matching process that identifies corresponding linear features from the UAVs RGB image data and a large, complex, and untextured 3D model. The proposed model-based tracker improved positional accuracies from 10 m (obtained with ViSP) to 46 cm in outdoor environments, and improved from an unattainable result using VISP to 2 cm positional accuracies in large indoor environments. The main disadvantage of any incremental algorithm is that it requires the camera pose of the first frame. Initialization is often a manual process. The third contribution of this dissertation is a map-based absolute localization algorithm that automatically estimates the camera pose when no prior pose information is available. The method benefits from vertical line matching to accomplish a registration procedure of the reference model views with a set of initial input images via geometric hashing. Results demonstrate that sub-metre positional accuracies were achieved and a proposed enhancement of conventional geometric hashing produced more correct matches - 75% of the correct matches were identified, compared to 11%. Further the number of incorrect matches was reduced by 80%