  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.
