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)

Unmanned Aerial Systems: Research, Development, Education & Training at Embry-Riddle Aeronautical University

With technological breakthroughs in miniaturized aircraft-related components, including but not limited to communications, computer systems and sensors, state-of-the-art unmanned aerial systems (UAS) have become a reality. This fast-growing industry is anticipating and responding to a myriad of societal applications that will provide new and more cost-effective solutions that previous technologies could not, or will replace activities that involved humans in flight with associated risks. Embry-Riddle Aeronautical University has a long history of aviation-related research and education, and is heavily engaged in UAS activities. This document provides a summary of these activities, and is divided into two parts. The first part provides a brief summary of each of the various activities, while the second part lists the faculty associated with those activities. Within the first part of this document we have separated UAS activities into two broad areas: Engineering and Applications. Each of these broad areas is then further broken down into six sub-areas, which are listed in the Table of Contents. The second part lists the faculty, sorted by campus (Daytona Beach-D, Prescott-P and Worldwide-W) associated with the UAS activities. The UAS activities and the corresponding faculty are cross-referenced. We have chosen to provide very short summaries of the UAS activities rather than lengthy descriptions. If more information is desired, please contact me directly, or visit our research website (https://erau.edu/research), or contact the appropriate faculty member using their e-mail address provided at the end of this document

Unmanned Aerial Systems Research, Development, Education and Training at Embry-Riddle Aeronautical University

With technological breakthroughs in miniaturized aircraft-related components, including but not limited to communications, computer systems and sensors and, state-of-the-art unmanned aerial systems (UAS) have become a reality. This fast growing industry is anticipating and responding to a myriad of societal applications that will provide either new or more cost effective solutions that previous technologies could not, or will replace activities that involved humans in flight with associated risks. Embry-Riddle Aeronautical University has a long history of aviation related research and education, and is heavily engaged in UAS activities. This document provides a summary of these activities. The document is divided into two parts. The first part provides a brief summary of each of the various activities while the second part lists the faculty associated with those activities. Within the first part of this document we have separated the UAS activities into two broad areas: Engineering and Applications. Each of these broad areas is then further broken down into six sub-areas, which are listed in the Table of Contents. The second part lists the faculty, sorted by campus (Daytona Beach---D, Prescott---P and Worldwide--W) associated with the UAS activities. The UAS activities and the corresponding faculty are cross-referenced. We have chosen to provide very short summaries of the UAS activities rather than lengthy descriptions. Should more information be desired, please contact me directly or alternatively visit our research web pages (http://research.erau.edu) and contact the appropriate faculty member directly

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

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

A framework for autonomous mission and guidance control of unmanned aerial vehicles based on computer vision techniques

A computação visual é uma área do conhecimento que estuda o desenvolvimento de sistemas artificiais capazes de detectar e desenvolver a percepção do meio ambiente através de informações de imagem ou dados multidimensionais. A percepção visual e a manipulação são combinadas em sistemas robóticos através de duas etapas "olhar"e depois "movimentar-se", gerando um laço de controle de feedback visual. Neste contexto, existe um interesse crescimente no uso dessas técnicas em veículos aéreos não tripulados (VANTs), também conhecidos como drones. Essas técnicas são aplicadas para posicionar o drone em modo de vôo autônomo, ou para realizar a detecção de regiões para vigilância aérea ou pontos de interesse. Os sistemas de computação visual geralmente tomam três passos em sua operação, que são: aquisição de dados em forma numérica, processamento de dados e análise de dados. A etapa de aquisição de dados é geralmente realizada por câmeras e sensores de proximidade. Após a aquisição de dados, o computador embarcado realiza o processamento de dados executando algoritmos com técnicas de medição (variáveis, índice e coeficientes), detecção (padrões, objetos ou áreas) ou monitoramento (pessoas, veículos ou animais). Os dados processados são analisados e convertidos em comandos de decisão para o controle para o sistema robótico autônomo Visando realizar a integração dos sistemas de computação visual com as diferentes plataformas de VANTs, este trabalho propõe o desenvolvimento de um framework para controle de missão e guiamento de VANTs baseado em visão computacional. O framework é responsável por gerenciar, codificar, decodificar e interpretar comandos trocados entre as controladoras de voo e os algoritmos de computação visual. Como estudo de caso, foram desenvolvidos dois algoritmos destinados à aplicação em agricultura de precisão. O primeiro algoritmo realiza o cálculo de um coeficiente de reflectância visando a aplicação auto-regulada e eficiente de agroquímicos, e o segundo realiza a identificação das linhas de plantas para realizar o guiamento dos VANTs sobre a plantação. O desempenho do framework e dos algoritmos propostos foi avaliado e comparado com o estado da arte, obtendo resultados satisfatórios na implementação no hardware embarcado.Cumputer Vision is an area of knowledge that studies the development of artificial systems capable of detecting and developing the perception of the environment through image information or multidimensional data. Nowadays, vision systems are widely integrated into robotic systems. Visual perception and manipulation are combined in two steps "look" and then "move", generating a visual feedback control loop. In this context, there is a growing interest in using computer vision techniques in unmanned aerial vehicles (UAVs), also known as drones. These techniques are applied to position the drone in autonomous flight mode, or to perform the detection of regions for aerial surveillance or points of interest. Computer vision systems generally take three steps to the operation, which are: data acquisition in numerical form, data processing and data analysis. The data acquisition step is usually performed by cameras or proximity sensors. After data acquisition, the embedded computer performs data processing by performing algorithms with measurement techniques (variables, index and coefficients), detection (patterns, objects or area) or monitoring (people, vehicles or animals). The resulting processed data is analyzed and then converted into decision commands that serve as control inputs for the autonomous robotic system In order to integrate the visual computing systems with the different UAVs platforms, this work proposes the development of a framework for mission control and guidance of UAVs based on computer vision. The framework is responsible for managing, encoding, decoding, and interpreting commands exchanged between flight controllers and visual computing algorithms. As a case study, two algorithms were developed to provide autonomy to UAVs intended for application in precision agriculture. The first algorithm performs the calculation of a reflectance coefficient used to perform the punctual, self-regulated and efficient application of agrochemicals. The second algorithm performs the identification of crop lines to perform the guidance of the UAVs on the plantation. The performance of the proposed framework and proposed algorithms was evaluated and compared with the state of the art, obtaining satisfactory results in the implementation of embedded hardware

Towards an autonomous vision-based unmanned aerial system againstwildlife poachers

Poaching is an illegal activity that remains out of control in many countries. Based on the 2014 report of the United Nations and Interpol, the illegal trade of global wildlife and natural resources amounts to nearly $213 billion every year, which is even helping to fund armed conflicts. Poaching activities around the world are further pushing many animal species on the brink of extinction. Unfortunately, the traditional methods to fight against poachers are not enough, hence the new demands for more efficient approaches. In this context, the use of new technologies on sensors and algorithms, as well as aerial platforms is crucial to face the high increase of poaching activities in the last few years. Our work is focused on the use of vision sensors on UAVs for the detection and tracking of animals and poachers, as well as the use of such sensors to control quadrotors during autonomous vehicle following and autonomous landing.Peer Reviewe

Towards an autonomous vision-based unmanned aerial system against wildlife poachers.

Poaching is an illegal activity that remains out of control in many countries. Based on the 2014 report of the United Nations and Interpol, the illegal trade of global wildlife and natural resources amounts to nearly $ 213 billion every year, which is even helping to fund armed conflicts. Poaching activities around the world are further pushing many animal species on the brink of extinction. Unfortunately, the traditional methods to fight against poachers are not enough, hence the new demands for more efficient approaches. In this context, the use of new technologies on sensors and algorithms, as well as aerial platforms is crucial to face the high increase of poaching activities in the last few years. Our work is focused on the use of vision sensors on UAVs for the detection and tracking of animals and poachers, as well as the use of such sensors to control quadrotors during autonomous vehicle following and autonomous landing

Twin Delayed Deep Deterministic Policy Gradient-Based Target Tracking for Unmanned Aerial Vehicle with Achievement Rewarding and Multistage Training

Target tracking using an unmanned aerial vehicle (UAV) is a challenging robotic problem. It requires handling a high level of nonlinearity and dynamics. Model-free control effectively handles the uncertain nature of the problem, and reinforcement learning (RL)-based approaches are a good candidate for solving this problem. In this article, the Twin Delayed Deep Deterministic Policy Gradient Algorithm (TD3), as recent and composite architecture of RL, was explored as a tracking agent for the UAV-based target tracking problem. Several improvements on the original TD3 were also performed. First, the proportional-differential controller was used to boost the exploration of the TD3 in training. Second, a novel reward formulation for the UAV-based target tracking enabled a careful combination of the various dynamic variables in the reward functions. This was accomplished by incorporating two exponential functions to limit the effect of velocity and acceleration to prevent the deformation in the policy function approximation. In addition, the concept of multistage training based on the dynamic variables was proposed as an opposing concept to one-stage combinatory training. Third, an enhancement of the rewarding function by including piecewise decomposition was used to enable more stable learning behaviour of the policy and move out from the linear reward to the achievement formula. The training was conducted based on fixed target tracking followed by moving target tracking. The flight testing was conducted based on three types of target trajectories: fixed, square, and blinking. The multistage training achieved the best performance with both exponential and achievement rewarding for the fixed trained agent with the fixed and square moving target and for the combined agent with both exponential and achievement rewarding for a fixed trained agent in the case of a blinking target. With respect to the traditional proportional differential controller, the maximum error reduction rate is 86%. The developed achievement rewarding and the multistage training opens the door to various applications of RL in target tracking