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.

本文介绍了一份在罗马收集的视觉和感知研究数据集，包括 RGB 数据、3D 点云、IMU 和 GPS 数据，并引入了一个新的基准用于视觉里程计和 SLAM，以推进自主机器人和计算机视觉的研究。