  Collaborative SLAM enables a group of agents to simultaneously co-localize
and jointly map an environment, thus paving the way to wide-ranging
applications of multi-robot perception and multi-user AR experiences by
eliminating the need for external infrastructure or pre-built maps. This
article presents COVINS, a novel collaborative SLAM system, that enables
multi-agent, scalable SLAM in large environments and for large teams of more
than 10 agents. The paradigm here is that each agent runs visual-inertial
odomety independently onboard in order to ensure its autonomy, while sharing
map information with the COVINS server back-end running on a powerful local PC
or a remote cloud server. The server back-end establishes an accurate
collaborative global estimate from the contributed data, refining the joint
estimate by means of place recognition, global optimization and removal of
redundant data, in order to ensure an accurate, but also efficient SLAM
process. A thorough evaluation of COVINS reveals increased accuracy of the
collaborative SLAM estimates, as well as efficiency in both removing redundant
information and reducing the coordination overhead, and demonstrates successful
operation in a large-scale mission with 12 agents jointly performing SLAM.
