TL;DR
COVINS-G introduces a flexible, generalized back-end for collaborative visual-inertial SLAM that can integrate any VIO front-end, enhancing adaptability and maintaining high accuracy in multi-robot systems.
Contribution
It presents a novel, versatile back-end framework compatible with various VIO front-ends, including off-the-shelf cameras, enabling broader application in collaborative SLAM.
Findings
Achieves accuracy comparable to state-of-the-art SLAM systems.
Demonstrates flexibility by integrating different VIO front-ends.
Open-sourced codebase facilitates adoption and further development.
Abstract
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…
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