Estimating and controlling UAV position using RGB-D/IMU data fusion with decentralized information/Kalman filter

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.