  Collaborative SLAM is at the core of perception in multi-robot systems as it
enables the co-localization of the team of robots in a common reference frame,
which is of vital importance for any coordination amongst them. The paradigm of
a centralized architecture is well established, with the robots (i.e. agents)
running Visual-Inertial Odometry (VIO) onboard while communicating relevant
data, such as e.g. Keyframes (KFs), to a central back-end (i.e. server), which
then merges and optimizes the joint maps of the agents. While these frameworks
have proven to be successful, their capability and performance are highly
dependent on the choice of the VIO front-end, thus limiting their flexibility.
In this work, we present COVINS-G, a generalized back-end building upon the
COVINS framework, enabling the compatibility of the server-back-end with any
arbitrary VIO front-end, including, for example, off-the-shelf cameras with
odometry capabilities, such as the Realsense T265. The COVINS-G back-end
deploys a multi-camera relative pose estimation algorithm for computing the
loop-closure constraints allowing the system to work purely on 2D image data.
In the experimental evaluation, we show on-par accuracy with state-of-the-art
multi-session and collaborative SLAM systems, while demonstrating the
flexibility and generality of our approach by employing different front-ends
onboard collaborating agents within the same mission. The COVINS-G codebase
along with a generalized front-end wrapper to allow any existing VIO front-end
to be readily used in combination with the proposed collaborative back-end is
open-sourced. Video: https://youtu.be/FoJfXCfaYDw
