Survey of computer vision algorithms and applications for unmanned aerial vehicles

This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria.This research is supported by the Spanish Government through the CICYT projects (TRA2015-63708-R and TRA2013-48314-C3-1-R)

Guidance, Navigation and Control for UAV Close Formation Flight and Airborne Docking

Unmanned aerial vehicle (UAV) capability is currently limited by the amount of energy that can be stored onboard or the small amount that can be gathered from the environment. This has historically lead to large, expensive vehicles with considerable fuel capacity. Airborne docking, for aerial refueling, is a viable solution that has been proven through decades of implementation with manned aircraft, but had not been successfully tested or demonstrated with UAVs. The prohibitive challenge is the highly accurate and reliable relative positioning performance that is required to dock with a small target, in the air, amidst external disturbances. GNSS-based navigation systems are well suited for reliable absolute positioning, but fall short for accurate relative positioning. Direct, relative sensor measurements are precise, but can be unreliable in dynamic environments. This work proposes an experimentally verified guidance, navigation and control solution that enables a UAV to autonomously rendezvous and dock with a drogue that is being towed by another autonomous UAV. A nonlinear estimation framework uses precise air-to-air visual observations to correct onboard sensor measurements and produce an accurate relative state estimate. The state of the drogue is estimated using known geometric and inertial characteristics and air-to-air observations. Setpoint augmentation algorithms compensate for leader turn dynamics during formation flight, and drogue physical constraints during docking. Vision-aided close formation flight has been demonstrated over extended periods; as close as 4 m; in wind speeds in excess of 25 km/h; and at altitudes as low as 15 m. Docking flight tests achieved numerous airborne connections over multiple flights, including five successful docking manoeuvres in seven minutes of a single flight. To the best of our knowledge, these are the closest formation flights performed outdoors and the first UAV airborne docking

Addressing corner detection issues for machine vision based UAV aerial refueling

The need for developing autonomous aerial refueling capabilities for an Unmanned Aerial Vehicle (UAV) has risen out of the growing importance of UAVs in military and non-military applications. The AAR capabilities would improve the range and the loiter time capabilities of UAVs. A number of AAR techniques have been proposed, based on GPS based measurements and Machine Vision based measurements. The GPS based measurements suffer from distorted data in the wake of the tanker. The MV based techniques proposed the use of optical markers which---when detected---were used to determine relative orientation and position of the tanker and the UAV. The drawback of the MV based techniques is the assumption that all the optical markers are always visible and functional. This research effort proposes an alternative approach where the pose estimation does not depend on optical markers but on Feature Extraction methods. The thesis describes the results of the analysis of specific \u27corner detection\u27 algorithms within a Machine Vision---based approach for the problem of Aerial Refueling for Unmanned Aerial Vehicles. Specifically, the performances of the SUSAN and the Harris corner detection algorithms have been compared. Special emphasis was placed on evaluating their accuracy, the required computational effort, and the robustness of both methods to different sources of noise. Closed loop simulations were performed using a detailed SimulinkRTM -based simulation environment to reproduce docking maneuvers, using the US Air Force refueling boom

Vision-Based navigation system for unmanned aerial vehicles

Mención Internacional en el título de doctorThe main objective of this dissertation is to provide Unmanned Aerial Vehicles (UAVs) with a robust navigation system; in order to allow the UAVs to perform complex tasks autonomously and in real-time. The proposed algorithms deal with solving the navigation problem for outdoor as well as indoor environments, mainly based on visual information that is captured by monocular cameras. In addition, this dissertation presents the advantages of using the visual sensors as the main source of data, or complementing other sensors in providing useful information; in order to improve the accuracy and the robustness of the sensing purposes. The dissertation mainly covers several research topics based on computer vision techniques: (I) Pose Estimation, to provide a solution for estimating the 6D pose of the UAV. This algorithm is based on the combination of SIFT detector and FREAK descriptor; which maintains the performance of the feature points matching and decreases the computational time. Thereafter, the pose estimation problem is solved based on the decomposition of the world-to-frame and frame-to-frame homographies. (II) Obstacle Detection and Collision Avoidance, in which, the UAV is able to sense and detect the frontal obstacles that are situated in its path. The detection algorithm mimics the human behaviors for detecting the approaching obstacles; by analyzing the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. Then, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, the algorithm extracts the collision-free zones around the obstacle, and combining with the tracked waypoints, the UAV performs the avoidance maneuver. (III) Navigation Guidance, which generates the waypoints to determine the flight path based on environment and the situated obstacles. Then provide a strategy to follow the path segments and in an efficient way and perform the flight maneuver smoothly. (IV) Visual Servoing, to offer different control solutions (Fuzzy Logic Control (FLC) and PID), based on the obtained visual information; in order to achieve the flight stability as well as to perform the correct maneuver; to avoid the possible collisions and track the waypoints. All the proposed algorithms have been verified with real flights in both indoor and outdoor environments, taking into consideration the visual conditions; such as illumination and textures. The obtained results have been validated against other systems; such as VICON motion capture system, DGPS in the case of pose estimate algorithm. In addition, the proposed algorithms have been compared with several previous works in the state of the art, and are results proves the improvement in the accuracy and the robustness of the proposed algorithms. Finally, this dissertation concludes that the visual sensors have the advantages of lightweight and low consumption and provide reliable information, which is considered as a powerful tool in the navigation systems to increase the autonomy of the UAVs for real-world applications.El objetivo principal de esta tesis es proporcionar Vehiculos Aereos no Tripulados (UAVs) con un sistema de navegacion robusto, para permitir a los UAVs realizar tareas complejas de forma autonoma y en tiempo real. Los algoritmos propuestos tratan de resolver problemas de la navegacion tanto en ambientes interiores como al aire libre basandose principalmente en la informacion visual captada por las camaras monoculares. Ademas, esta tesis doctoral presenta la ventaja de usar sensores visuales bien como fuente principal de datos o complementando a otros sensores en el suministro de informacion util, con el fin de mejorar la precision y la robustez de los procesos de deteccion. La tesis cubre, principalmente, varios temas de investigacion basados en tecnicas de vision por computador: (I) Estimacion de la Posicion y la Orientacion (Pose), para proporcionar una solucion a la estimacion de la posicion y orientacion en 6D del UAV. Este algoritmo se basa en la combinacion del detector SIFT y el descriptor FREAK, que mantiene el desempeno del a funcion de puntos de coincidencia y disminuye el tiempo computacional. De esta manera, se soluciona el problema de la estimacion de la posicion basandose en la descomposicion de las homografias mundo a imagen e imagen a imagen. (II) Deteccion obstaculos y elusion colisiones, donde el UAV es capaz de percibir y detectar los obstaculos frontales que se encuentran en su camino. El algoritmo de deteccion imita comportamientos humanos para detectar los obstaculos que se acercan, mediante el analisis de la magnitud del cambio de los puntos caracteristicos detectados de referencia, combinado con los ratios de expansion de los contornos convexos construidos alrededor de los puntos caracteristicos detectados en frames consecutivos. A continuacion, comparando la proporcion del area del obstaculo y la posicion del UAV, el metodo decide si el obstaculo detectado puede provocar una colision. Por ultimo, el algoritmo extrae las zonas libres de colision alrededor del obstaculo y combinandolo con los puntos de referencia, elUAV realiza la maniobra de evasion. (III) Guiado de navegacion, que genera los puntos de referencia para determinar la trayectoria de vuelo basada en el entorno y en los obstaculos detectados que encuentra. Proporciona una estrategia para seguir los segmentos del trazado de una manera eficiente y realizar la maniobra de vuelo con suavidad. (IV) Guiado por Vision, para ofrecer soluciones de control diferentes (Control de Logica Fuzzy (FLC) y PID), basados en la informacion visual obtenida con el fin de lograr la estabilidad de vuelo, asi como realizar la maniobra correcta para evitar posibles colisiones y seguir los puntos de referencia. Todos los algoritmos propuestos han sido verificados con vuelos reales en ambientes exteriores e interiores, tomando en consideracion condiciones visuales como la iluminacion y las texturas. Los resultados obtenidos han sido validados con otros sistemas: como el sistema de captura de movimiento VICON y DGPS en el caso del algoritmo de estimacion de la posicion y orientacion. Ademas, los algoritmos propuestos han sido comparados con trabajos anteriores recogidos en el estado del arte con resultados que demuestran una mejora de la precision y la robustez de los algoritmos propuestos. Esta tesis doctoral concluye que los sensores visuales tienen las ventajes de tener un peso ligero y un bajo consumo y, proporcionar informacion fiable, lo cual lo hace una poderosa herramienta en los sistemas de navegacion para aumentar la autonomia de los UAVs en aplicaciones del mundo real.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Carlo Regazzoni.- Secretario: Fernando García Fernández.- Vocal: Pascual Campoy Cerver

Evaluation of machine vision techniques for use within flight control systems

In this thesis, two of the main technical limitations for a massive deployment of Unmanned Aerial Vehicle (UAV) have been considered.;The Aerial Refueling problem is analyzed in the first section. A solution based on the integration of \u27conventional\u27 GPS/INS and Machine Vision sensor is proposed with the purpose of measuring the relative distance between a refueling tanker and UAV. In this effort, comparisons between Point Matching (PM) algorithms and Pose Estimation (PE) algorithms have been developed in order to improve the performance of the Machine Vision sensor. A method of integration based on Extended Kalman Filter (EKF) between GPS/INS and Machine Vision system is also developed with the goal of reducing the tracking error in the \u27pre-contact\u27 to contact and refueling phases.;In the second section of the thesis the issue of Collision Identification (CI) is addressed. A proposed solution consists on the use of Optical Flow (OF) algorithms for the detection of possible collisions in the range of vision of a single camera. The effort includes a study of the performance of different Optical Flow algorithms in different scenarios as well as a method to compute the ideal optical flow with the aim of evaluating the algorithms. An analysis on the suitability for a future real time implementation is also performed for all the analyzed algorithms.;Results of the tests show that the Machine Vision technology can be used to improve the performance in the Aerial Refueling problem. In the Collision Identification problem, the Machine Vision has to be integrated with standard sensors in order to be used inside the Flight Control System

Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001

An Image Based Visual Servo Method for Probe-and-Drogue Autonomous Aerial Refueling

With the high focus on autonomous aerial refueling recently, it becomes increasingly urgent to design efficient methods or algorithms to solve AAR problems in complicated aerial environments. Apart from the complex aerodynamic disturbance, another problem is the pose estimation error caused by the camera calibration error, installation error, or 3D object modeling error, which may not satisfy the highly accurate docking. The main objective of the effort described in this paper is the implementation of an image-based visual servo control method, which contains the establishment of an image-based visual servo model involving the receiver's dynamics and the design of the corresponding controller. Simulation results indicate that the proposed method can make the system dock successfully under complicated conditions and improve the robustness against pose estimation error

Optical Tracking for Relative Positioning in Automated Aerial Refueling

An algorithm is designed to extract features from video of an air refueling tanker for use in determining the precise relative position of a receiver aircraft. The algorithm is based on receiving a known estimate of the tanker aircraft\u27s position and attitude. The algorithm then uses a known feature model of the tanker to predict the location of those features on a video frame. A corner detector is used to extract features from the video. The measured corners are then associated with known features and tracked from frame to frame. For each frame, the associated features are used to calculate three dimensional pointing vectors to the features of the tanker. These vectors are passed to a navigation algorithm which uses extended Kalman filters, as well as data-linked INS data to solve for the relative position of the tanker. The algorithms were tested using data from a flight test accomplished by the USAF Test Pilot School using a C-12C as a simulated tanker and a Learjet LJ-24 as the simulated receiver. The system was able to provide at least a dozen useful measurements per frame, with and without projection error