Ultra-wideband (UWB) time-difference-of-arrival (TDOA)-based localization has emerged as a promising, low-cost, and scalable indoor localization solution, which is especially suited for multi-robot applications. However, there is a lack of public datasets to study and benchmark UWB TDOA positioning technology in cluttered indoor environments. We fill in this gap by presenting a comprehensive dataset using Decawave's DWM1000 UWB modules. To characterize the UWB TDOA measurement performance under various line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, we collected signal-to-noise ratio (SNR), power difference values, and raw UWB TDOA measurements during the identification experiments. We also conducted a cumulative total of around 150 minutes of real-world flight experiments on a customized quadrotor platform to benchmark the UWB TDOA localization performance for mobile robots. The quadrotor was commanded to fly with an average speed of 0.45 m/s in both obstacle-free and cluttered environments using four different UWB anchor constellations. Raw sensor data including UWB TDOA, inertial measurement unit (IMU), optical flow, time-of-flight (ToF) laser altitude, and millimeter-accurate ground truth robot poses were collected during the flights. The dataset and development kit are available at https://utiasdsl.github.io/util-uwb-dataset/.

翻译：超宽带（UWB）到达时间差（TDOA）定位技术已成为一种有前景、低成本且可扩展的室内定位方案，特别适用于多机器人应用场景。然而，目前缺乏用于研究和评估室内复杂环境中UWB TDOA定位技术的公开数据集。为此，我们利用Decawave的DWM1000 UWB模块构建了综合性数据集，填补了这一空白。为表征不同视距（LOS）和非视距（NLOS）条件下的UWB TDOA测量性能，我们在识别实验中采集了信噪比（SNR）、功率差值以及原始UWB TDOA测量值。此外，我们在定制四旋翼飞行器平台上开展了累计约150分钟的真实飞行实验，以评估移动机器人的UWB TDOA定位性能。飞行器以平均0.45米/秒的速度，在无障碍和复杂环境中采用四种不同UWB锚点布局进行飞行。飞行过程中采集的原始传感器数据包括：UWB TDOA、惯性测量单元（IMU）、光流、飞行时间（ToF）激光高度以及毫米级精度的机器人真实位姿。数据集与开发工具包已发布于https://utiasdsl.github.io/util-uwb-dataset/。