Kalman filtering: theory and practice
Kalman filtering: theory and practice
Digital Control of Dynamic Systems
Digital Control of Dynamic Systems
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Optic flow-based vision system for autonomous 3D localization and control of small aerial vehicles
Robotics and Autonomous Systems
Image-based visual servo control of the translation kinematics of a quadrotor aerial vehicle
IEEE Transactions on Robotics - Special issue on rehabilitation robotics
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
Vision-based unmanned aerial vehicle navigation using geo-referenced information
EURASIP Journal on Advances in Signal Processing - Special issue on signal processing advances in robots and autonomy
Stabilization and Trajectory Tracking of a Quad-Rotor Using Vision
Journal of Intelligent and Robotic Systems
Vision Based Position Control for MAVs Using One Single Circular Landmark
Journal of Intelligent and Robotic Systems
Piecewise constant model predictive control for autonomous helicopters
Robotics and Autonomous Systems
KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera
Proceedings of the 24th annual ACM symposium on User interface software and technology
A Practical Visual Servo Control for an Unmanned Aerial Vehicle
IEEE Transactions on Robotics
Journal of Intelligent and Robotic Systems
Stability Analysis of a Vision-Based UAV Controller
Journal of Intelligent and Robotic Systems
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The requirement that mobile robots become independent of external sensors, such as GPS, and are able to navigate in an environment by themselves, means that designers have few alternative techniques available. An increasingly popular approach is to use computer vision as a source of information about the surroundings. This paper presents an implementation of computer vision to hold a quadrocopter aircraft in a stable hovering position using a low-cost, consumer-grade, video system. However, such a system is not able to stabilize the aircraft on its own and must rely on a data-fusion algorithm that uses additional measurements from on-board inertial sensors. Special techniques had to be implemented to compensate for the increased delay in the closed-loop system with the computer vision system, i.e., video timestamping to determine the exact delay of the vision system and a slight modification of the Kalman filter to account for this delay. At the end, the validation results of the proposed filtering technique are presented along with the results of an autonomous flight as a proof of the proposed concept.