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解决方案，尤其适用于多机器人应用。然而，当前缺乏用于研究和评估复杂室内环境中UWB TDOA定位技术的公开数据集。为填补这一空白，我们采用Decawave公司的DWM1000 UWB模块构建了一个综合数据集。为表征不同视距（LOS）与非视距（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）、功率差值及原始UWB TDOA测量数据。此外，我们还在定制四旋翼平台上进行了总计约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。飞行过程中，四旋翼以平均0.45米/秒的速度在无障碍和复杂环境中分别采用四种不同UWB锚点配置执行飞行任务。实验同步采集了原始传感器数据，包括UWB TDOA、惯性测量单元（IMU）、光流、飞行时间（ToF）激光高度计数据，以及毫米级精度的机器人真实位姿信息。数据集及开发工具包可通过https://utiasdsl.github.io/util-uwb-dataset/获取。