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

TL;DR

This paper introduces a multi-agent 3D relative pose estimation system using ultra-wideband (UWB) sensors, achieving high accuracy with minimal communication by leveraging local measurements, constraints, and learned bias correction.

Contribution

The novel system uses multiple UWB antennas per agent and event-based constraints to improve accuracy without extensive data transmission, outperforming similar low-communication methods.

Findings

Mean positional error of 0.24m achieved

Mean heading error of 9.5 degrees achieved

19% improvement with bias correction

Abstract

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.