Adaptive Noise Identification in Vision-assisted Motion Estimation for Unmanned Aerial Vehicles

Vision localization methods have been widely used in the motion estimation of unmanned aerial vehicles (UAVs). The noise of the vision location result is usually modeled as a white Gaussian noise so that this location result could be utilized as the observation vector in the Kalman filter to estimate the motion of the vehicle. Since the noise of the vision location result is affected by external environment, the variance of the noise is uncertain. However, in previous researches, the variance is usually set as a fixed empirical value, which will lower the accuracy of the motion estimation. The main contribution of this paper is that we proposed a novel adaptive noise variance identification (ANVI) method, which utilizes the special kinematic properties of the UAV for frequency analysis and then adaptively identifies the variance of the noise. The adaptively identified variance is used in the Kalman filter for more accurate motion estimation. The performance of the proposed method is assessed by simulations and field experiments on a quadrotor system. The results illustrate the effectiveness of the method.