Robotics and Mechatronics Lab, Andalucía Tech, Universidad de Málaga, 29071 Málaga, Spain.
Sensors (Basel). 2022 Jul 26;22(15):5599. doi: 10.3390/s22155599.
This paper presents a new synthetic dataset obtained from Gazebo simulations of an Unmanned Ground Vehicle (UGV) moving on different natural environments. To this end, a Husky mobile robot equipped with a tridimensional (3D) Light Detection and Ranging (LiDAR) sensor, a stereo camera, a Global Navigation Satellite System (GNSS) receiver, an Inertial Measurement Unit (IMU) and wheel tachometers has followed several paths using the Robot Operating System (ROS). Both points from LiDAR scans and pixels from camera images, have been automatically labeled into their corresponding object class. For this purpose, unique reflectivity values and flat colors have been assigned to each object present in the modeled environments. As a result, a public dataset, which also includes 3D pose ground-truth, is provided as ROS bag files and as human-readable data. Potential applications include supervised learning and benchmarking for UGV navigation on natural environments. Moreover, to allow researchers to easily modify the dataset or to directly use the simulations, the required code has also been released.
本文提出了一个新的合成数据集，该数据集是通过在 Gazebo 中对无人地面车辆（UGV）在不同自然环境下的移动进行模拟得到的。为此，配备了三维（3D）激光雷达（LiDAR）传感器、立体相机、全球导航卫星系统（GNSS）接收器、惯性测量单元（IMU）和车轮转速计的 Husky 移动机器人使用机器人操作系统（ROS）遵循了几条路径。LiDAR 扫描的点和相机图像的像素都已自动标记为它们对应的对象类。为此，为模型环境中的每个对象分配了独特的反射率值和平坦颜色。作为结果，提供了一个公共数据集，其中还包括 3D 姿态地面真实值，以 ROS 数据包和人类可读数据的形式提供。潜在的应用包括对 UGV 在自然环境中的导航进行监督学习和基准测试。此外，为了允许研究人员轻松修改数据集或直接使用模拟，还发布了所需的代码。
