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