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

翻译：协作SLAM是多机器人系统感知的核心，它使机器人团队能够在公共参考框架中实现协同定位，这对机器人间的任何协调都至关重要。集中式架构的范式已得到广泛认可：机器人（即智能体）在机载端运行视觉惯性里程计（VIO），同时将关键帧（KF）等相关数据传输至中央后端（即服务器），由服务器合并并优化各智能体的联合地图。尽管此类框架已被证实有效，但其性能高度依赖于VIO前端的选择，从而限制了灵活性。本文提出COVINS-G，一种基于COVINS框架的通用化后端，使服务器后端能够兼容任意VIO前端，例如支持即时定位的商用相机（如Realsense T265）。COVINS-G后端采用多相机相对位姿估计算法计算闭环约束，使系统仅依赖二维图像数据工作。实验评估表明，该方法在精度上与当前最先进的多会话与协作SLAM系统持平，同时通过在同一任务中为协作智能体部署不同前端，证明了其灵活性和通用性。本工作开源了COVINS-G代码库及通用前端封装器，使任何现有VIO前端均可与所提出的协作后端即时协同使用。视频链接：https://youtu.be/FoJfXCfaYDw