five

Multimodal Agricultural Aerial and Ground Robotics Simulation Dataset

收藏
NIAID Data Ecosystem2026-05-01 收录
下载链接:
https://zenodo.org/record/10159038
下载链接
链接失效反馈
官方服务:
资源简介:
Dataset description This dataset was generated using an aerial robot and a ground robot in the Webots simulator with the OpenDR agricultural dataset generator tool. It consists of 13980 RGB images and their semantic segmentation counterparts taken at different lighting conditions and robot positions in an agricultural field. It also includes the annotation data comprised of the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box. Furthermore, it includes gps and inertial unit sensor data for UAV and gps, inertial and lidar sensor data for UGV. Folder configuration The dataset contains 4 folders for different lighting conditions: noon cloudy noon stormy dawn cloudy dusk Each contains UAV and UGV folders. UAV folder includes: annotations: contains segmented images in JPG files and annotations in TXT files. camera: contains generated RGB images. gps: contains the three-axis location of global positioning sensor saved in TXT files. inertial unit: contains the inertial unit date in TXT files. UGV folder includes: annotations: contains segmented images in JPG files and annotations in TXT files. front_bottom_camera: contains generated RGB images. Hemisphere_v500: contains the three-axis location of the global positioning sensor saved in TXT files. imu_robotti: contains the inertial unit date in TXT files. velodyne: contains lidar data in PCD files. Data format The dataset includes The inertial measurement TXT files include Euler angles in order of Roll, Pitch, and Yaw. The GPS measurement TXT files include the robot position in x, y, and z order. Object annotation TXT files include the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box at each line for the corresponding frame. File naming convention Each data is named "s_i{_segmented, _annotation}.ext", where: s denotes the simulated time in seconds. i denotes the index counting every 10ms of simulated time. ext denotes the extension, "jpg" for images, "pcd" for lidar, and "txt" for the rest. Labels _segmented and _annotation appended to the name for segmentation image and object annotations, respectively. Each segmented image uses the following RGB color mapping: Tree: 0.1, 0.4, 0.0 Apple Tree: 0.85, 0.49, 0.57 Cow: 0.380, 0.220, 0.137 Sheep: 0.937, 0.921, 0.862 Fox: 0.992, 0.376, 0.086 Barn: 0.625, 0.293, 0.226 Cat: 0.870, 0.580, 0.0 Deer: 0.415, 0.364, 0.302 Human: 1.0, 0.855, 0.672
创建时间:
2023-11-24
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作