发表机构
North Carolina State University; University of Massachusetts Amherst（北卡罗来纳州立大学; 马萨诸塞大学阿默斯特分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现实中机器人短交互窗口及有限通信带宽下的相对位姿估计难题，提出通信高效相对位姿估计框架CERPE，利用固定大小描述符减少原始观测交换，通过自身运动传播相对位姿，提升了短暂协作感知中的6自由度相对位姿估计。
AI 中文摘要
相对位姿估计是多机器人系统中协作感知与协调的基本能力。然而，现实环境中相遇的机器人常处于短交互窗口，且在有限通信带宽下运行，存在遮挡或视野受限导致的视觉重叠间歇性或缺失。现有方法有局限。为此引入通信高效相对位姿估计（CERPE）系统框架，通过连续共享固定大小描述符减少不必要原始观测交换，独立于位姿估计触发原始图像请求。通过度量缩放自身运动传播机器人间相对位姿处理非重叠相遇。仿真和实际机器人实验表明，CERPE在短暂协作感知中提升了6自由度相对位姿估计。
英文摘要
Relative pose estimation is a fundamental capability for collaborative perception and coordination in multi-robot systems. However, robots encountering each other in real-world environments often operate in short interaction windows and must operate under limited communication bandwidth with intermittent or missing visual overlap caused by occlusions or limited fields of view. Existing approaches typically rely on global reference frames, assume sustained view overlap, or incur prohibitive communication costs, thereby limiting their applicability to ephemeral collaborative perception. To address these challenges, we introduce communication-efficient relative pose estimation (CERPE), a system-level framework that coordinates vision foundation models to jointly estimate ego-motion and inter-robot relative pose. CERPE reduces unnecessary raw-observation exchange by using continuously shared fixed-size descriptors to gate event-triggered raw-image requests independently of pose estimation. Non-overlapping encounters are handled by propagating inter-robot relative poses through metrically scaled ego-motion, thus maintaining relative pose estimates even in the absence of visual overlap. Experiments in simulation and real-world robots show that CERPE improves 6-DoF relative pose estimation over selected baselines in ephemeral collaborative perception.
Comments8 pages, 6 figures
