RVIO: An Effective Localization Algorithm for Range-Aided Visual-Inertial Odometry System

This paper presents an efficient and accurate range-aided visual-inertial odometry (RVIO) system for the global positioning system denied environment. In particular, the ultra-wideband (UWB) measurements are integrated to reduce the long-term drift of the visual-inertial odometry (VIO) system. Our approach starts with a filter-based scheme to localize the unknown UWB anchor in the local world frame. In particular, a novel surface-based particle filter is proposed to localize the UWB anchors efficiently. When the initialization is complete, the UWB location information is utilized to support the subsequent long-term robot positioning. An observability-constrained optimization approach is developed to combine the visual, inertial, and UWB range measurements. Such a framework takes advantage of both VIO and UWB measurements and is feasible even when the number of observed UWB anchors is below four. Experiments on both simulated and real-world scenes demonstrate the validity and superiority of the proposed system.