  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
