We present a synchronized multisensory dataset for accurate and robust indoor localization: the Lund University Vision, Radio, and Audio (LuViRA) Dataset. The dataset includes color images, corresponding depth maps, inertial measurement unit (IMU) readings, channel response between a 5G massive multiple-input and multiple-output (MIMO) testbed and user equipment, audio recorded by 12 microphones, and accurate six degrees of freedom (6DOF) pose ground truth of 0.5 mm. We synchronize these sensors to ensure that all data is recorded simultaneously. A camera, speaker, and transmit antenna are placed on top of a slowly moving service robot, and 89 trajectories are recorded. Each trajectory includes 20 to 50 seconds of recorded sensor data and ground truth labels. Data from different sensors can be used separately or jointly to perform localization tasks, and data from the motion capture (mocap) system is used to verify the results obtained by the localization algorithms. The main aim of this dataset is to enable research on sensor fusion with the most commonly used sensors for localization tasks. Moreover, the full dataset or some parts of it can also be used for other research areas such as channel estimation, image classification, etc. Our dataset is available at: https://github.com/ilaydayaman/LuViRA_Dataset

翻译：我们提出一个用于精确且鲁棒室内定位的同步多传感器数据集：隆德大学视觉、无线电与音频（LuViRA）数据集。该数据集包含彩色图像、对应的深度图、惯性测量单元（IMU）读数、5G大规模多输入多输出（MIMO）测试平台与用户设备之间的信道响应、12个麦克风录制的音频，以及精度达0.5毫米的精确六自由度（6DOF）位姿真值。我们同步这些传感器，确保所有数据同时记录。将摄像头、扬声器和发射天线置于缓慢移动的服务机器人顶部，共记录89条轨迹。每条轨迹包含20至50秒的传感器记录数据及真值标签。来自不同传感器的数据可单独或联合用于执行定位任务，运动捕捉（mocap）系统的数据用于验证定位算法获得的结果。本数据集的主要目标是推动使用定位任务中最常用传感器的传感器融合研究。此外，完整数据集或其部分子集也可用于信道估计、图像分类等其他研究领域。我们的数据集访问地址为：https://github.com/ilaydayaman/LuViRA_Dataset