Overlapping Free: Anchorless UWB-Assisted Relative Pose Estimation for Multi-Robot Systems

Abstract

Accurate Relative Pose Estimation (RPE) is critical for effective collaboration of multi-robot systems. Traditional methods using cameras or LiDARs heavily rely on overlapping Fields of View (FoV) between robots, which is highly demanding in practical applications and may hinder collaboration efficiency. To accommodate this issue, we propose Anchorless UWB-Assisted Relative Pose Estimation (AURPE), a novel approach that leverages ultra-wideband (UWB) technology in an anchorless setup to achieve multi-robot RPE without requiring overlapping FoVs or external infrastructure. AURPE first estimates the initial relative poses between robots using inter-robot UWB ranging combined with a Bayesian framework and constrained optimization. During robot operation, AURPE continuously refines the relative poses by integrating UWB measurements with LiDAR-inertial odometry (LIO) and employs a consensus voting mechanism to identify the most reliable pose estimates. Additionally, a pose graph-based backend optimization is incorporated to enhance the accuracy of both initial and real-time relative pose. Extensive simulations and real-world experiments demonstrate that AURPE achieves accurate RPE even in non-overlapping scenarios where traditional methods fail. Compared to state-of-the-art point cloud registration methods, AURPE shows superior performance in both accuracy and robustness, highlighting its potential to significantly enhance cooperative tasks in multi-robot systems operating in complex environments.