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Inter-agent relative localization is critical for many multi-robot systemsoperating in the absence of external positioning infrastructure or priorenvironmental knowledge. We propose a novel inter-agent relative 3D poseestimation system where each participating agent is equipped with severalultra-wideband (UWB) ranging tags. Prior work typically supplements noisy UWBrange measurements with additional continuously transmitted data, such asodometry, leading to potential scaling issues with increased team size and/ordecreased communication network capability. By equipping each agent withmultiple UWB antennas, our approach addresses these concerns by using onlylocally collected UWB range measurements, a priori state constraints, anddetections of when said constraints are violated. Leveraging our learned meanranging bias correction, we gain a 19% positional error improvement giving usexperimental mean absolute position and heading errors of 0.24m and 9.5 degreesrespectively. When compared to other state-of-the-art approaches, our workdemonstrates improved performance over similar systems, while remainingcompetitive with methods that have significantly higher communication costs.Additionally, we make our datasets available.

MURP: Multi-Agent Ultra-Wideband Relative Pose Estimation with Constrained Communications in 3D Environments