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In this work we introduce the CitrusFarm dataset, a comprehensive multimodalsensory dataset collected by a wheeled mobile robot operating in agriculturalfields. The dataset offers stereo RGB images with depth information, as well asmonochrome, near-infrared and thermal images, presenting diverse spectralresponses crucial for agricultural research. Furthermore, it provides a rangeof navigational sensor data encompassing wheel odometry, LiDAR, inertialmeasurement unit (IMU), and GNSS with Real-Time Kinematic (RTK) as thecentimeter-level positioning ground truth. The dataset comprises sevensequences collected in three fields of citrus trees, featuring various treespecies at different growth stages, distinctive planting patterns, as well asvarying 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 thatthis dataset can facilitate the development of autonomous robot systemsoperating in agricultural tree environments, especially for localization,mapping and crop monitoring tasks. Moreover, the rich sensing modalitiesoffered in this dataset can also support research in a range of robotics andcomputer vision tasks, such as place recognition, scene understanding, objectdetection and segmentation, and multimodal learning. The dataset, inconjunction with related tools and resources, is made publicly available athttps://github.com/UCR-Robotics/Citrus-Farm-Dataset.

Multimodal Dataset for Localization, Mapping and Crop Monitoring in Citrus Tree Farms