TY - JOUR
T1 - MAgro dataset
T2 - A dataset for simultaneous localization and mapping in agricultural environments
AU - Marzoa Tanco, Mercedes
AU - Trinidad Barnech, Guillermo
AU - Andrade, Federico
AU - Baliosian, Javier
AU - LLofriu, Martin
AU - Di Martino, J. M.
AU - Tejera, Gonzalo
N1 - Publisher Copyright:
© The Author(s) 2023.
PY - 2024/4
Y1 - 2024/4
N2 - The agricultural industry is being transformed, thanks to recent innovations in computer vision and deep learning. However, the lack of specific datasets collected in natural agricultural environments is, arguably, the main bottleneck for novel discoveries and benchmarking. The present work provides a novel dataset, Magro, and a framework to expand data collection. We present the first version of the Magro Dataset V1.0, consisting of nine ROS bags (and the corresponding raw data) containing data collected in apple and pear crops. Data were gathered, repeating a fixed trajectory on different days under different illumination and weather conditions. To support the evaluation of loop closure algorithms, the trajectories are designed to have loop closures, revisiting some places from different viewpoints. We use a Clearpath’s Jackal robot equipped with stereo cameras pointing to the front and left side, a 3D LIDAR, three inertial measurement units (IMU), and wheel encoders. Additionally, we provide calibrated RTK GPS data that can be used as ground truth. Our dataset is openly available, and it will be updated to have more data and variability. Finally, we tested two existing state-of-the-art algorithms for vision and point cloud-based localization and mapping on our novel dataset to validate the dataset’s usability.
AB - The agricultural industry is being transformed, thanks to recent innovations in computer vision and deep learning. However, the lack of specific datasets collected in natural agricultural environments is, arguably, the main bottleneck for novel discoveries and benchmarking. The present work provides a novel dataset, Magro, and a framework to expand data collection. We present the first version of the Magro Dataset V1.0, consisting of nine ROS bags (and the corresponding raw data) containing data collected in apple and pear crops. Data were gathered, repeating a fixed trajectory on different days under different illumination and weather conditions. To support the evaluation of loop closure algorithms, the trajectories are designed to have loop closures, revisiting some places from different viewpoints. We use a Clearpath’s Jackal robot equipped with stereo cameras pointing to the front and left side, a 3D LIDAR, three inertial measurement units (IMU), and wheel encoders. Additionally, we provide calibrated RTK GPS data that can be used as ground truth. Our dataset is openly available, and it will be updated to have more data and variability. Finally, we tested two existing state-of-the-art algorithms for vision and point cloud-based localization and mapping on our novel dataset to validate the dataset’s usability.
KW - agricultural automation
KW - dataset
KW - simultaneous localization and mapping
UR - http://www.scopus.com/inward/record.url?scp=85174963548&partnerID=8YFLogxK
U2 - 10.1177/02783649231210011
DO - 10.1177/02783649231210011
M3 - Artículo
AN - SCOPUS:85174963548
SN - 0278-3649
VL - 43
SP - 591
EP - 601
JO - International Journal of Robotics Research
JF - International Journal of Robotics Research
IS - 5
ER -