# The Rosario Dataset v2: Multimodal Dataset for Agricultural Robotics

arXiv: https://arxiv.org/abs/2508.21635 · HTML: https://mlsanity.com/paper/2508.21635/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming](https://mlsanity.com/paper/1808.00100/) (2018)
- [BotanicGarden: A High-Quality Dataset for Robot Navigation in Unstructured Natural Environments](https://mlsanity.com/paper/2306.14137/) (2023)
- [The Rosario Dataset: Multisensor Data for Localization and Mapping in Agricultural Environments](https://mlsanity.com/paper/1809.06413/) (2018)
- [FieldSAFE: Dataset for Obstacle Detection in Agriculture](https://mlsanity.com/paper/1709.03526/) (2017)
- [Experimental Evaluation of Visual-Inertial Odometry Systems for Arable Farming](https://mlsanity.com/paper/2206.05066/) (2022)
- [The GREENBOT dataset: Multimodal mobile robotic dataset for a typical Mediterranean greenhouse](https://mlsanity.com/paper/2402.00438/) (2024)
- [GNSS-stereo-inertial SLAM for arable farming](https://mlsanity.com/paper/2307.12836/) (2023)
- [Addressing the challenges of loop detection in agricultural environments](https://mlsanity.com/paper/2408.15761/) (2024)

## Built on by (most cited first, 2 of 2)

- [Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey](https://mlsanity.com/paper/2601.22198/) (2026)
- [CropCraft: A Procedural World Generator for Robotic Simulation of Agricultural Tasks](https://mlsanity.com/paper/2511.02417/) (2025)

## Abstract (arXiv)

We present a multi-modal dataset collected in a soybean crop field, comprising over two hours of recorded data from sensors such as stereo infrared camera, color camera, accelerometer, gyroscope, magnetometer, GNSS (Single Point Positioning, Real-Time Kinematic and Post-Processed Kinematic), and wheel odometry. This dataset captures key challenges inherent to robotics in agricultural environments, including variations in natural lighting, motion blur, rough terrain, and long, perceptually aliased sequences. By addressing these complexities, the dataset aims to support the development and benchmarking of advanced algorithms for localization, mapping, perception, and navigation in agricultural robotics. The platform and data collection system is designed to meet the key requirements for evaluating multi-modal SLAM systems, including hardware synchronization of sensors, 6-DOF ground truth and loops on long trajectories. We run multimodal state-of-the art SLAM methods on the dataset, showcasing the existing limitations in their application on agricultural settings. The dataset and utilities to work with it are released on https://cifasis.github.io/rosariov2/.