COVINS-G：一种面向协作式视觉惯性SLAM的通用后端
COVINS-G: A Generic Back-end for Collaborative Visual-Inertial SLAM
AI总结：
本研究提出基于COVINS框架的协作式视觉惯性SLAM通用后端COVINS-G，支持任意VIO前端接入，采用多相机相对位姿估计实现2D图像回环，精度达顶尖水平且已开源。
摘要：
协作式同步定位与地图构建（SLAM）是多机器人系统感知层的核心技术，它能让机器人团队在统一参考坐标系下实现共同定位，这对机器人之间的任何协作都至关重要。集中式架构的范式已十分成熟：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。尽管这类框架已被证实有效，但其功能和性能高度依赖VIO前端的选型，灵活性因此受限。本研究提出COVINS-G——一种基于COVINS框架构建的通用化后端，可实现服务器后端与任意VIO前端的兼容，例如搭载里程计功能的现成相机（如Realsense T265）。COVINS-G后端采用多相机相对位姿估计算法计算回环约束，使系统可完全基于2D图像数据运行。实验评估中，我们证明该系统的精度与当前最先进的多会话及协作SLAM系统相当，同时通过在同一任务中为协作智能体搭载不同前端，验证了该方法的灵活性与通用性。COVINS-G代码库以及一款通用前端封装器（可让任意现有VIO前端便捷地与所提协作后端搭配使用）已开源。视频链接：https://youtu.be/FoJfXCfaYDw
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
