  This paper presents a vision and perception research dataset collected in
Rome, featuring RGB data, 3D point clouds, IMU, and GPS data. We introduce a
new benchmark targeting visual odometry and SLAM, to advance the research in
autonomous robotics and computer vision. This work complements existing
datasets by simultaneously addressing several issues, such as environment
diversity, motion patterns, and sensor frequency. It uses up-to-date devices
and presents effective procedures to accurately calibrate the intrinsic and
extrinsic of the sensors while addressing temporal synchronization. During
recording, we cover multi-floor buildings, gardens, urban and highway
scenarios. Combining handheld and car-based data collections, our setup can
simulate any robot (quadrupeds, quadrotors, autonomous vehicles). The dataset
includes an accurate 6-dof ground truth based on a novel methodology that
refines the RTK-GPS estimate with LiDAR point clouds through Bundle Adjustment.
All sequences divided in training and testing are accessible through our
website.
