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.