Detecting and dealing with hovering maneuvers in vision-aided inertial navigation systems

In this paper, we study the problem of hovering (i.e., absence of translational motion) detection and compensation in Vision-aided Inertial Navigation Systems (VINS). We examine the system´s unobservable directions for two common hovering conditions (with and without rotational motion) and propose a robust motion-classification algorithm, based on both visual and inertial measurements. By leveraging our observability analysis and the proposed motion classifier, we modify existing state-of-the-art filtering algorithms, so as to ensure that the number of the system´s unobservable directions is minimized. Finally, we validate experimentally the proposed modified sliding window filter, by demonstrating its robustness on a quadrotor with rapid transitions between hovering and forward motions, within an indoor environment.