MURP: Multi-Agent Ultra-Wideband Relative Pose Estimation with Constrained Communications in 3D Environments
CoRR(2023)
摘要
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, such as
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
detections of when said constraints are violated. Leveraging our learned mean
ranging bias correction, we gain a 19
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.
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