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 (e.g., 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 event-based detections of when said constraints are violated. The addition of our learned mean ranging bias correction improves our approach by an additional 19% positional error, and gives us an overall 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条件下运行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姿态估计系统，其中每个参与智能体配备多个超宽带（UWB）测距标签。先前的工作通常用额外连续传输的数据（例如里程计）来补充含噪声的UWB测距测量值，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潜在扩展性问题。通过为每个智能体配备多个UWB天线，我们的方法仅利用本地采集的UWB测距测量值、先验状态约束以及这些约束被违反时基于事件的检测来解决这些问题。加入学习得到的平均测距偏差校正后，我们的方法在位置误差上额外降低了19%，总体实验平均绝对位置误差和航向误差分别为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，展现了优于同类系统的性能。此外，我们公开了所用数据集。