无需手动设置，实时自动初始化未知超宽带锚点。
Real-Time Initialization of Unknown Anchors for UWB-aided Navigation
- 结合在线定位精度评估与轻量级异常检测，实现动态锚点校准。
- 初始化误差更低，定位精度优于现有方法。
- 适用于无人机和无人叉车等移动机器人系统。
本文提出一种在超宽带（UWB）辅助导航系统中实时初始化未知锚点的框架。该方法适用于将UWB模块作为补充传感器的定位方案，可在运行过程中自动检测并校准未知锚点，无需人工部署。通过融合在线位置精度稀释因子（PDOP）估计、轻量级异常检测方法以及自适应鲁棒核函数进行非线性优化，显著提升了系统在真实场景中的鲁棒性与实用性。特别地，所提出的初始化触发指标比当前基于初始线性或非线性估计的方法更为保守，有助于获得更优的初始化几何结构，从而降低初始化误差。我们在两种不同移动机器人上验证了该方法：一台自主叉车和一架搭载UWB辅助视觉惯性里程计（VIO）框架的四旋翼无人机。实验结果表明，该方法实现了稳健的初始化与低定位误差。代码已开源，包含C++库及ROS封装。
原文摘要 · Abstract (English)
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.
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