The Rosario Dataset v2: Multimodal Dataset for Agricultural Robotics
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912559022473216 |
|---|---|
| author | Soncini, Nicolas Cremona, Javier Vidal, Erica García, Maximiliano Castro, Gastón Pire, Taihú |
| author_facet | Soncini, Nicolas Cremona, Javier Vidal, Erica García, Maximiliano Castro, Gastón Pire, Taihú |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_21635 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Rosario Dataset v2: Multimodal Dataset for Agricultural Robotics Soncini, Nicolas Cremona, Javier Vidal, Erica García, Maximiliano Castro, Gastón Pire, Taihú Robotics Computer Vision and Pattern Recognition Systems and Control I.2.9 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/. |
| title | The Rosario Dataset v2: Multimodal Dataset for Agricultural Robotics |
| topic | Robotics Computer Vision and Pattern Recognition Systems and Control I.2.9 |
| url | https://arxiv.org/abs/2508.21635 |