  Low-feature environments are one of the main Achilles' heels of geometric
computer vision (CV) algorithms. In most human-built scenes often with low
features, lines can be considered complements to points. In this paper, we
present a multi-robot cooperative visual-inertial navigation system (VINS)
using both point and line features. By utilizing the covariance intersection
(CI) update within the multi-state constraint Kalman filter (MSCKF) framework,
each robot exploits not only its own point and line measurements, but also
constraints of common point and common line features observed by its neighbors.
The line features are parameterized and updated by utilizing the Closest Point
representation. The proposed algorithm is validated extensively in both
Monte-Carlo simulations and a real-world dataset. The results show that the
point-line cooperative visual-inertial odometry (PL-CVIO) outperforms the
independent MSCKF and our previous work CVIO in both low-feature and
rich-feature environments.
