  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.
