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Monocular cameras coupled with inertial measurements generally give highperformance visual inertial odometry. However, drift can be significant withlong trajectories, especially when the environment is visually challenging. Inthis paper, we propose a system that leverages ultra-wideband ranging with onestatic anchor placed in the environment to correct the accumulated errorwhenever the anchor is visible. We also use this setup for collaborative SLAM:different robots use mutual ranging (when available) and the common anchor toestimate the transformation between each other, facilitating map fusion Oursystem consists of two modules: a double layer ranging, visual, and inertialodometry for single robots, and a transformation estimation module forcollaborative SLAM. We test our system on public datasets by simulating anultra-wideband sensor as well as on real robots. Experiments show our methodcan outperform state-of-the-art visual-inertial odometry by more than 20%. Forvisually challenging environments, our method works even the visual-inertialodometry has significant drift Furthermore, we can compute the collaborativeSLAM transformation matrix at almost no extra computation cost.

VIR-SLAM: Visual, Inertial, and Ranging SLAM for single and multi-robot systems