This paper presents a framework for the real-time initialization of unknown Ultra-Wideband (UWB) anchors in UWB-aided navigation systems. The method is designed for localization solutions where UWB modules act as supplementary sensors. Our approach enables the automatic detection and calibration of previously unknown anchors during operation, removing the need for manual setup. By combining an online Positional Dilution of Precision (PDOP) estimation, a lightweight outlier detection method, and an adaptive robust kernel for non-linear optimization, our approach significantly improves robustness and suitability for real-world applications compared to state-of-the-art. In particular, we show that our metric which triggers an initialization decision is more conservative than current ones commonly based on initial linear or non-linear initialization guesses. This allows for better initialization geometry and subsequently lower initialization errors. We demonstrate the proposed approach on two different mobile robots: an autonomous forklift and a quadcopter equipped with a UWB-aided Visual-Inertial Odometry (VIO) framework. The results highlight the effectiveness of the proposed method with robust initialization and low positioning error. We open-source our code in a C++ library including a ROS wrapper.

翻译：本文提出了一种用于UWB辅助导航系统中未知超宽带（UWB）锚点实时初始化的框架。该方法专为将UWB模块作为辅助传感器的定位解决方案设计。我们的方法能够在运行过程中自动检测并校准先前未知的锚点，无需人工配置。通过结合在线位置精度因子（PDOP）估计、轻量级异常值检测方法以及用于非线性优化的自适应鲁棒核函数，与现有技术相比，该方法显著提升了鲁棒性和实际应用适用性。特别地，我们证明了触发初始化决策的度量标准比当前普遍基于初始线性或非线性初始化猜测的方法更为保守，从而能获得更优的初始化几何构型，进而降低初始化误差。我们在两个不同的移动机器人上演示了所提出的方法：一台自动叉车和一台配备UWB辅助视觉惯性里程计（VIO）框架的四旋翼飞行器。实验结果凸显了该方法在鲁棒初始化和低定位误差方面的有效性。我们已将代码以C++库形式开源，并附带ROS封装。