In this paper a complete framework is proposed, to deal with trajectory tracking and positioning with an AR.Drone Parrot quadrotor flying in indoor environments. The system runs in a centralized way, in a computer installed in a ground station, and is based on two main structures,namely a Kalman Filter (KF) to track the 3D position of the vehicle and a nonlinear controller to guide it in the accomplishment of its flight missions. The KF is designed aiming at estimating the states of the vehicle, fusing inertial and visual data. The nonlinear controller is designed with basis on a dynamic model of the AR.Drone, with the closed-loop stability proven using the theory of Lyapunov. Finally, experimental results are presented, which demonstrate the effectiveness of the proposed framework.

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This paper presents a framework to deal with outdoor navigation using an AR.Drone Parrot quadrotor. The proposed system runs in a centralized computer, the ground station, responsible for the communication with the unmanned aerial vehicle (UAV) and for synthesizing the control signals during flight missions. The outdoor navigation is performed through using a layered control architecture, where a highlevel control algorithm, designed from the kinematic differential equations describing the movement of the UAV, is used to generate reference signals for a low-level velocity controller. To feedback the controllers, the sensorial data provided by the AR.Drone onboard sensors and a GPS module are fused through a Kalman Filter, allowing getting a more reliable estimate of the UAV state. Finally, experimental results are presented, which demonstrate the effectiveness of the proposed framework.

This work proposes an obstacle avoidance strategy for UAV navigation in indoor environments. The proposal is able to compute the distance among the UAV and the obstacles (which change their position dynamically), and then to select the closest one. When a collision risk is pointed out, the algorithm establish some escape points, whose orientation is aligned tangentially to the obstacle edge or to the UAV normal displacement. Considering that only the desired point is change during the avoidance maneuver, the stability of the whole nonlinear system is demonstrated in the sense of Lyapunov. Information Filter is used to track the 3D positioning of the UAV and the obstacles. Moreover, UAV state variables are given by a Decentralized Information Filter, which fuses information from the Inertial Measurement Unit onboard the aircraft and the depth-camera sensor (RGB-D). The effectiveness of the proposal is demonstrated by simulation results, which take into account the AR.drone rotorcraft dynamic model.

This paper proposes a 3D data capture system, based on the fusion of data coming from an active depth sensor and a inertial measurement unit (IMU), to determine the position of an aerial unmanned vehicle (UAV) in indoor environments, for control purposes. Firstly, the method adopted to detect the vehicle through using a sequence of RGB-D images. After that, the information provided by the active depth sensor is fused with the data provided by the IMU onboard the vehicle, using a Decentralized Kalman Filter (DKF) and a Decentralized Information Filter (DIF), whose performance are compared. In the sequel, a nonlinear controller is used for positioning the UAV. Finally, the performance differences between the DKF and the DIF are highlighted, as well as the divergence between the results of the depth sensor and the inertial one, in experiments involving abrupt maneuvers to induce estimation errors in the inertial unit, to check the effectiveness of the developed 3D data capture system.