Abstract
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.
Abstract (translated)
本文介绍了一个收集自罗马的数据集，包括RGB数据、3D点云、IMU和GPS数据。我们提出了一个新的基准，针对视觉导航和SLAM，以促进自主机器人和计算机视觉的研究。这项工作通过同时解决多个问题，如环境多样性、运动模式和传感器频率，补充了现有的数据集。它使用最先进的设备，并提供了准确校准传感器固有和外在参数的有效方法，同时解决时间同步问题。在记录过程中，我们覆盖了多层建筑、花园和城市和高速公路场景。结合手持和车载数据收集，我们的系统可以模拟任何机器人（四足、四旋翼、自动驾驶车辆）。该数据集基于一种新方法，通过利用激光雷达点云通过束调整对RTK-GPS估计进行优化。所有训练和测试序列都可以通过我们的网站访问。
URL
https://arxiv.org/abs/2404.11322
