Integrated Position Estimation Using Aerial Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized image matching by the method of differences
Generalized image matching by the method of differences
Optic flow-based vision system for autonomous 3D localization and control of small aerial vehicles
Robotics and Autonomous Systems
Accurate Modeling and Robust Hovering Control for a Quad---rotor VTOL Aircraft
Journal of Intelligent and Robotic Systems
Flyphone: Visual Self-Localisation Using a Mobile Phone as Onboard Image Processor on a Quadrocopter
Journal of Intelligent and Robotic Systems
Low-Cost Visual Tracking of a Landing Place and Hovering Flight Control with a Microcontroller
Journal of Intelligent and Robotic Systems
Performing and extending aggressive maneuvers using iterative learning control
Robotics and Autonomous Systems
Stabilization and Trajectory Tracking of a Quad-Rotor Using Vision
Journal of Intelligent and Robotic Systems
Survey paper: A survey on industrial applications of fuzzy control
Computers in Industry
Journal of Intelligent and Robotic Systems
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A Practical Visual Servo Control for an Unmanned Aerial Vehicle
IEEE Transactions on Robotics
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In recent years, Unmanned Aerial Vehicles (UAVs) have gained increasing popularity. These vehicles are employed in many applications, from military operations to civilian tasks. One of the main fields of UAV research is the vehicle positioning problem. Fully autonomous vehicles are required to be as self-sustained as possible in terms of external sensors. To achieve this in situations where the global positioning system (GPS) does not function, computer vision can be used. This paper presents an implementation of computer vision to hold a quadrotor aircraft in a stable hovering position using a low-cost, consumer-grade, video system. The successful implementation of this system required the development of a data-fusion algorithm that uses both inertial sensors and visual system measurements for the purpose of positioning. The system design is unique in its ability to successfully handle missing and considerably delayed video system data. Finally, a control algorithm was implemented and the whole system was tested experimentally. The results suggest the successful continuation of research in this field.