Inter-agent relative localization is critical for many multi-robot systems operating in the absence of external positioning infrastructure or prior environmental knowledge. We propose a novel inter-agent relative 3D pose estimation system where each participating agent is equipped with several ultra-wideband (UWB) ranging tags. Prior work typically supplements noisy UWB range measurements with additional continuously transmitted data, such as odometry, leading to potential scaling issues with increased team size and/or decreased communication network capability. By equipping each agent with multiple UWB antennas, our approach addresses these concerns by using only locally collected UWB range measurements, a priori state constraints, and detections of when said constraints are violated. Leveraging our learned mean ranging bias correction, we gain a 19% positional error improvement giving us experimental mean absolute position and heading errors of 0.24m and 9.5 degrees respectively. When compared to other state-of-the-art approaches, our work demonstrates improved performance over similar systems, while remaining competitive with methods that have significantly higher communication costs. Additionally, we make our datasets available.

翻译：智能体间相对定位对于许多在缺乏外部定位基础设施或先验环境知识的多机器人系统至关重要。我们提出了一种新颖的智能体间三维相对位姿估计系统，其中每个参与智能体配备多个超宽带测距标签。先前的工作通常用额外连续传输的数据（如里程计）来补充噪声超宽带测距测量，这可能导致随着团队规模增大和/或通信网络能力下降而出现扩展性问题。通过为每个智能体配备多个超宽带天线，我们的方法仅使用本地收集的超宽带测距测量、先验状态约束以及违反这些约束的检测结果来解决这些问题。利用我们学习的平均测距偏差校正，我们将位置误差提升了19%，实验平均绝对位置误差和航向角误差分别达到0.24米和9.5度。与其他最先进方法相比，我们的工作在保持与通信成本显著更高的方法竞争力同时，展现出比同类系统更优的性能。此外，我们公开了所使用的数据集。