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:协同 SLAM 是多机器人系统感知的核心,因为它可以使机器人团队在一个共同的参考帧中共同定位,这对它们之间的任何协调都非常重要。集中式架构范例已经得到了很好的建立,机器人(即代理)在设备上运行视觉惯性测量(VIO),同时向中央后端(即服务器)通信相关数据,例如关键帧(KFs),然后合并并优化代理的联合地图。虽然这些框架已被证明是成功的,但它们的能力和性能高度依赖于VIO前端的选择,从而限制了它们的灵活性。在这项工作中,我们提出了COVINS-G,这是一种通用的后端,基于COVINS框架构建,使服务器后端与任何任意的VIO前端兼容,包括例如具有测距能力的现成相机(如Realsense T265)。COVINS-G后端使用多相机相对姿态估计算法,用于计算闭环约束,从而使系统能够纯粹地在2D图像数据上工作。在实验评估中,我们展示了与最先进的多会话协同SLAM系统相当的准确性,同时通过在同一任务中在协作代理上使用不同的前端,展示了我们方法的灵活性和通用性。COVINS-G代码库以及通用前端包装器,以允许任何现有的VIO前端与所提出的协同后端轻松结合使用,已经开源。视频:https://youtu.be/FoJfXCfaYDw