Using optical flow for filling the gaps in visual-inertial tracking

During the last decades egomotion tracking has been an often addressed problem. Hybrid approaches evidentially have potential to provide accurate, efficient and robust results. Simultaneous localisation and mapping (SLAM) - in contrast to model-based approaches - is used to enable tracking in unknown environments. However, it also suffers from high computational complexity. Moreover, in many applications, the map itself is not needed and the target environment is partially known, e.g. in a few 3D anchor points. In this paper, rather than using SLAM, optical flow measurements are introduced into a model-based system. With these measurements, a modified visual-inertial tracking method is derived, which in Monte Carlo simulations reduces the need for 3D points and thus allows tracking during extended gaps of 3D point registrations.