  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.
