In this work, we propose a factor graph optimization (FGO) framework to simultaneously solve the calibration problem for Ultra-WideBand (UWB) anchors and the robot localization problem. Calibrating UWB anchors manually can be time-consuming and even impossible in emergencies or those situations without special calibration tools. Therefore, automatic estimation of the anchor positions becomes a necessity. The proposed method enables the creation of a soft sensor providing the position information of the anchors in a UWB network. This soft sensor requires only UWB and LiDAR measurements measured from a moving robot. The proposed FGO framework is suitable for the calibration of an extendable large UWB network. Moreover, the anchor calibration problem and robot localization problem can be solved simultaneously, which saves time for UWB network deployment. The proposed framework also helps to avoid artificial errors in the UWB-anchor position estimation and improves the accuracy and robustness of the robot-pose. The experimental results of the robot localization using LiDAR and a UWB network in a 3D environment are discussed, demonstrating the performance of the proposed method. More specifically, the anchor calibration problem with four anchors and the robot localization problem can be solved simultaneously and automatically within 30 seconds by the proposed framework. The supplementary video and codes can be accessed via https://github.com/LiuxhRobotAI/Simultaneous_calibration_localization.

翻译：本研究提出了一种因子图优化（FGO）框架，用于同步解决超宽带（UWB）锚点的标定问题与机器人定位问题。在紧急情况或缺乏专用标定工具的场景中，手动标定UWB锚点可能耗时且难以实现。因此，自动估计锚点位置成为必要。所提方法能够创建一个软传感器，提供UWB网络中锚点的位置信息。该软传感器仅需从移动机器人采集的UWB与激光雷达（LiDAR）测量数据。所提出的FGO框架适用于可扩展的大型UWB网络的标定。此外，锚点标定问题与机器人定位问题可同步求解，从而节省了UWB网络部署的时间。该框架还有助于避免UWB锚点位置估计中的人为误差，并提高了机器人位姿估计的精度与鲁棒性。本文讨论了在三维环境中使用LiDAR与UWB网络进行机器人定位的实验结果，验证了所提方法的性能。具体而言，所提框架可在30秒内自动同步完成包含四个锚点的标定问题与机器人定位问题。补充视频与代码可通过 https://github.com/LiuxhRobotAI/Simultaneous_calibration_localization 获取。