In this work we introduce the CitrusFarm dataset, a comprehensive multimodal sensory dataset collected by a wheeled mobile robot operating in agricultural fields. The dataset offers stereo RGB images with depth information, as well as monochrome, near-infrared and thermal images, presenting diverse spectral responses crucial for agricultural research. Furthermore, it provides a range of navigational sensor data encompassing wheel odometry, LiDAR, inertial measurement unit (IMU), and GNSS with Real-Time Kinematic (RTK) as the centimeter-level positioning ground truth. The dataset comprises seven sequences collected in three fields of citrus trees, featuring various tree species at different growth stages, distinctive planting patterns, as well as varying daylight conditions. It spans a total operation time of 1.7 hours, covers a distance of 7.5 km, and constitutes 1.3 TB of data. We anticipate that this dataset can facilitate the development of autonomous robot systems operating in agricultural tree environments, especially for localization, mapping and crop monitoring tasks. Moreover, the rich sensing modalities offered in this dataset can also support research in a range of robotics and computer vision tasks, such as place recognition, scene understanding, object detection and segmentation, and multimodal learning. The dataset, in conjunction with related tools and resources, is made publicly available at https://github.com/UCR-Robotics/Citrus-Farm-Dataset.

翻译：本文介绍了CitrusFarm数据集，这是一个由轮式移动机器人在农业田间采集的综合性多模态传感数据集。该数据集提供带有深度信息的立体RGB图像，以及单色、近红外和热成像图像，呈现出对农业研究至关重要的多样化光谱响应。此外，数据集还提供了多种导航传感器数据，包括轮式里程计、LiDAR、惯性测量单元（IMU）以及采用实时动态差分（RTK）技术实现厘米级定位精度的GNSS作为地面真值。数据集由在三个柑橘树田间采集的七个序列组成，涵盖了不同生长阶段的不同树种、独特的种植模式以及多变的日光条件。总运行时间为1.7小时，覆盖距离7.5公里，数据量达1.3 TB。我们预期该数据集能够促进在农业树环境中运行的自主机器人系统的开发，尤其适用于定位、建图和作物监测任务。此外，该数据集提供的丰富传感模态还可支持一系列机器人和计算机视觉任务的研究，例如位置识别、场景理解、目标检测与分割以及多模态学习。该数据集及相关工具与资源已在https://github.com/UCR-Robotics/Citrus-Farm-Dataset上公开发布。