  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.
