VBR: A Vision Benchmark in Rome
This dataset addresses the need for diverse, high-quality benchmarks in autonomous robotics and computer vision, though it is incremental by complementing existing datasets.
The paper introduces a new vision and perception dataset collected in Rome, featuring diverse sensor data and environments, to benchmark visual odometry and SLAM, with ground truth refined using a novel RTK-GPS and LiDAR-based method.
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.