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：本研究介绍了CitrusFarm数据集，该数据集由在农田中作业的轮式移动机器人采集，包含全面的多模态传感器数据。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，呈现了农业研究中至关重要的多样化光谱响应。此外，数据集还提供一系列导航传感器数据，包括轮式里程计、激光雷达、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数据作为定位真值。该数据集包含在三个柑橘农场采集的七个序列，涵盖不同生长阶段的多类树种、独特的种植模式以及不同的日照条件。数据总运行时长1.7小时，覆盖7.5公里距离，包含1.3 TB数据。我们预期该数据集可促进农业树丛环境中自主机器人系统的研发，特别是定位、建图与作物监测任务。此外，数据集提供的丰富传感模态还可支持一系列机器人学和计算机视觉任务研究，例如地点识别、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资源已在https://github.com/UCR-Robotics/Citrus-Farm-Dataset 公开提供。