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.

翻译：状态估计是自主系统的核心组成部分。研究表明，引入超宽带技术可有效校正长期估计漂移，并规避闭环检测的复杂性。然而，现有机器人研究中鲜少将UWB作为独立的状态估计方案。本文旨在探究仅利用UWB距离测量进行平面位姿估计的方法，并分析该估计器的统计效率。我们证明了两步法方案的優良性——通过单次高斯-牛顿迭代即可将一致性估计器优化为渐近有效估计器。基于该理论成果，我们设计了GN-ULS估计器，并通过仿真实验与实测数据集进行性能评估。在静态数据集上，GN-ULS实现毫米级与亚度级精度；在动态数据集上达到厘米级与度级精度，验证了仅依赖UWB实现实时状态估计的可行性。