971 research outputs found
Cooperative monocular-based SLAM for multi-UAV systems in GPS-denied environments
This work presents a cooperative monocular-based SLAM approach for multi-UAV systems that can operate in GPS-denied environments. The main contribution of the work is to show that, using visual information obtained from monocular cameras mounted onboard aerial vehicles flying in formation, the observability properties of the whole system are improved. This fact is especially notorious when compared with other related visual SLAM configurations. In order to improve the observability properties, some measurements of the relative distance between the UAVs are included in the system. These relative distances are also obtained from visual information. The proposed approach is theoretically validated by means of a nonlinear observability analysis. Furthermore, an extensive set of computer simulations is presented in order to validate the proposed approach. The numerical simulation results show that the proposed system is able to provide a good position and orientation estimation of the aerial vehicles flying in formation.Peer ReviewedPostprint (published version
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)
Vision-Based navigation system for unmanned aerial vehicles
MenciĂłn Internacional en el tĂtulo 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 IngenierĂa ElĂ©ctrica, ElectrĂłnica y AutomáticaPresidente: Carlo Regazzoni.- Secretario: Fernando GarcĂa Fernández.- Vocal: Pascual Campoy Cerver
Bio-inspired vision-based leader-follower formation flying in the presence of delays
Flocking starlings at dusk are known for the mesmerizing and intricate shapes they generate, as well as how fluid these shapes change. They seem to do this effortlessly. Real-life vision-based flocking has not been achieved in micro-UAVs (micro Unmanned Aerial Vehicles) to date. Towards this goal, we make three contributions in this paper: (i) we used a computational approach to develop a bio-inspired architecture for vision-based Leader-Follower formation flying on two micro-UAVs. We believe that the minimal computational cost of the resulting algorithm makes it suitable for object detection and tracking during high-speed flocking; (ii) we show that provided delays in the control loop of a micro-UAV are below a critical value, Kalman filter-based estimation algorithms are not required to achieve Leader-Follower formation flying; (iii) unlike previous approaches, we do not use external observers, such as GPS signals or synchronized communication with flock members. These three contributions could be useful in achieving vision-based flocking in GPS-denied environments on computationally-limited agents
Flexible Stereo: Constrained, Non-rigid, Wide-baseline Stereo Vision for Fixed-wing Aerial Platforms
This paper proposes a computationally efficient method to estimate the
time-varying relative pose between two visual-inertial sensor rigs mounted on
the flexible wings of a fixed-wing unmanned aerial vehicle (UAV). The estimated
relative poses are used to generate highly accurate depth maps in real-time and
can be employed for obstacle avoidance in low-altitude flights or landing
maneuvers. The approach is structured as follows: Initially, a wing model is
identified by fitting a probability density function to measured deviations
from the nominal relative baseline transformation. At run-time, the prior
knowledge about the wing model is fused in an Extended Kalman filter~(EKF)
together with relative pose measurements obtained from solving a relative
perspective N-point problem (PNP), and the linear accelerations and angular
velocities measured by the two inertial measurement units (IMU) which are
rigidly attached to the cameras. Results obtained from extensive synthetic
experiments demonstrate that our proposed framework is able to estimate highly
accurate baseline transformations and depth maps.Comment: Accepted for publication in IEEE International Conference on Robotics
and Automation (ICRA), 2018, Brisban
Virtual Testbed for Monocular Visual Navigation of Small Unmanned Aircraft Systems
Monocular visual navigation methods have seen significant advances in the
last decade, recently producing several real-time solutions for autonomously
navigating small unmanned aircraft systems without relying on GPS. This is
critical for military operations which may involve environments where GPS
signals are degraded or denied. However, testing and comparing visual
navigation algorithms remains a challenge since visual data is expensive to
gather. Conducting flight tests in a virtual environment is an attractive
solution prior to committing to outdoor testing.
This work presents a virtual testbed for conducting simulated flight tests
over real-world terrain and analyzing the real-time performance of visual
navigation algorithms at 31 Hz. This tool was created to ultimately find a
visual odometry algorithm appropriate for further GPS-denied navigation
research on fixed-wing aircraft, even though all of the algorithms were
designed for other modalities. This testbed was used to evaluate three current
state-of-the-art, open-source monocular visual odometry algorithms on a
fixed-wing platform: Direct Sparse Odometry, Semi-Direct Visual Odometry, and
ORB-SLAM2 (with loop closures disabled)
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