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

arXiv: https://arxiv.org/abs/2309.15332 · HTML: https://mlsanity.com/paper/2309.15332/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 27 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [The Rosario Dataset: Multisensor Data for Localization and Mapping in Agricultural Environments](https://mlsanity.com/paper/1809.06413/) (2018)
- [Optimising the selection of samples for robust lidar camera calibration](https://mlsanity.com/paper/2103.12287/) (2021)
- [An Integrated Actuation-Perception Framework for Robotic Leaf Retrieval: Detection, Localization, and Cutting](https://mlsanity.com/paper/2208.05032/) (2022)
- [A Portable Agricultural Robot for Continuous Apparent Soil ElectricalConductivity Measurements to Improve Irrigation Practices](https://mlsanity.com/paper/2107.09219/) (2021)

## Built on by (most cited first, 6 of 6)

- [MineInsight: A Multi-sensor Dataset for Humanitarian Demining Robotics in Off-Road Environments](https://mlsanity.com/paper/2506.04842/) (2025)
- [ROVER: A Multi-Season Dataset for Visual SLAM](https://mlsanity.com/paper/2412.02506/) (2024)
- [Low Cost, High Efficiency: LiDAR Place Recognition in Vineyards with Matryoshka Representation Learning](https://mlsanity.com/paper/2601.18714/) (2026)
- [Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey](https://mlsanity.com/paper/2601.22198/) (2026)
- [HiddenObject: Modality-Agnostic Fusion for Multimodal Hidden Object Detection](https://mlsanity.com/paper/2508.21135/) (2025)
- [Autonomous Agricultural Tractor: Integrated Weed Detection and LiDAR Navigation for Precision Paddy Farming](https://mlsanity.com/paper/2608.19004/) (2026)

## Abstract (arXiv)

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.