Ultra-wideband (UWB) positioning has emerged as a low-cost and dependable localization solution for multiple use cases, from mobile robots to asset tracking within the Industrial IoT. The technology is mature and the scientific literature contains multiple datasets and methods for localization based on fixed UWB nodes. At the same time, research in UWB-based relative localization and infrastructure-free localization is gaining traction, further domains. tools and datasets in this domain are scarce. Therefore, we introduce in this paper a novel dataset for benchmarking infrastructure-free relative localization targeting the domain of multi-robot systems. Compared to previous datasets, we analyze the performance of different relative localization approaches for a much wider variety of scenarios with varying numbers of fixed and mobile nodes. A motion capture system provides ground truth data, are multi-modal and include inertial or odometry measurements for benchmarking sensor fusion methods. Additionally, the dataset contains measurements of ranging accuracy based on the relative orientation of antennas and a comprehensive set of measurements for ranging between a single pair of nodes. Our experimental analysis shows that high accuracy can be localization, but the variability of the ranging error is significant across different settings and setups.

翻译：超宽带（UWB）定位已成为从移动机器人到工业物联网资产追踪等多种应用场景中低成本且可靠的定位解决方案。该技术已趋成熟，科学文献中也包含大量基于固定UWB节点的定位数据集与方法。与此同时，基于UWB的相对定位和无基础设施定位研究正获得广泛关注，但该领域的工具和数据集仍较为匮乏。因此，本文提出了一种面向多机器人系统领域的新型无基础设施相对定位基准测试数据集。相比现有数据集，我们在包含不同数量固定节点与移动节点的更广泛场景中，分析了多种相对定位方法的性能。运动捕捉系统提供了真实数据，该数据集是多模态的，包含惯性测量或里程计数据以支持传感器融合方法的基准测试。此外，数据集还包含了基于天线相对方向的测距精度测量结果，以及单节点对间测距的全方位测量数据。实验分析表明，定位可实现高精度，但测距误差的变异性在不同设置和配置条件下差异显著。