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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.