Efficient Planar Pose Estimation via UWB Measurements

TL;DR

This paper introduces a novel UWB-based planar pose estimation method that achieves high accuracy using a two-step estimator refined by Gauss-Newton iteration, suitable for real-time autonomous systems.

Contribution

It demonstrates the statistical efficiency of a two-step UWB-based estimator and designs the GN-ULS method for accurate, stand-alone pose estimation in robotics.

Findings

Achieves millimeter and sub-degree accuracy on static datasets.

Attains centimeter and degree accuracy on dynamic datasets.

Proves the asymptotic efficiency of the two-step estimation scheme.

Abstract

State estimation is an essential part of autonomous systems. Integrating the Ultra-Wideband(UWB) technique has been shown to correct the long-term estimation drift and bypass the complexity of loop closure detection. However, few works on robotics adopt UWB as a stand-alone state estimation solution. The primary purpose of this work is to investigate planar pose estimation using only UWB range measurements and study the estimator's statistical efficiency. We prove the excellent property of a two-step scheme, which says that we can refine a consistent estimator to be asymptotically efficient by one step of Gauss-Newton iteration. Grounded on this result, we design the GN-ULS estimator and evaluate it through simulations and collected datasets. GN-ULS attains millimeter and sub-degree level accuracy on our static datasets and attains centimeter and degree level accuracy on our dynamic datasets, presenting the possibility of using only UWB for real-time state estimation.