List of Datasets

UGV

Rosario

Agricultural

Outdoor agricultural ground-robot dataset with RGB stereo imagery, IMU, RTK, and wheel encoder odometry.

Metadata: Complete Metadata source documented Released 2019

Primary places to inspect, download, read about, or reproduce the dataset.

Website

Not reported

Download

Not reported

Paper

Not reported

Code

Not reported

Supplementary

Not reported

Quick facts

Vehicle type
UGV
Environment
Outdoor
Data origin
Real-world
Sensor count
7
Ground truth
Available
Calibration
Parameters Only
Annotations
Not reported

Sensor overview

High-level sensor availability before the detailed sensor records below.

Cameras

2 reported

IMUs

1 reported

GNSS

1 reported

LiDAR

Not reported

Magnetometers

Not reported

Barometers

Not reported

UWB

Not reported

Ground truth

Reference scope, method, rate, coverage, and provenance.

Scope Method Reference system Count Rate Coverage Reference provenance
External
Measured using equipment independent of the platform’s normal onboard sensor pipeline.
Derived
Computed primarily from the dataset’s own recorded sensors.
Hybrid
Combines independent reference equipment with onboard measurements.
3D position RTK GNSS Not reported Not reported 5 Hz Full Reference provenance
External
Measured using equipment independent of the platform’s normal onboard sensor pipeline.
Derived
Computed primarily from the dataset’s own recorded sensors.
Hybrid
Combines independent reference equipment with onboard measurements.
External
Accuracy and notes
Reference notes
GPS-RTK provides positional ground truth for the six released sequences. No global orientation is supplied because the IMU has no magnetometer.

Calibration and synchronization

Reported calibration level, reproducibility signals, and supporting notes.

Level
Parameters Only
Processed parameters
Available
Raw calibration data
Not available
Calibration targets
Not reported

Parameters only

Processed calibration parameters are reported, but raw calibration data is not reported.

Notes

Each sequence includes camera and IMU intrinsics plus transformations between the sensors. Camera-IMU calibration used Kalibr; other extrinsics used measurement, visual SLAM, and AprilTag-based procedures.

Known or intentional limitations

Reported constraints and characteristics to check before using the dataset.

The low-cost ZED stereo camera uses a rolling shutter, adding distortion during bumpy motion.

Agricultural scenes contain repetitive or insufficient texture, non-rigid crop motion, uneven terrain, changing illumination, occasional people, and direct-sunlight image burn.

GPS-RTK supplies position but no global orientation ground truth.

Cameras

stereo-rgb

Model
ZED
Setup
Stereo
Count
1
Effective cameras
2
Modality
Rgb
Resolution
672 x 376
Rate
15 Hz
Shutter
Rolling
Lens type
Not Reported
HFOV
Not reported
VFOV
Not reported

IMUs

TARA stereo-inertial sensor IMU

Model
LSM6DS0
Count
1
Accel / gyro
140 / 140 Hz

GNSS

Reach

Model or name
Reach
Count
1
Rate
5 Hz
GNSS type
RTK
Position output
Available
Velocity output
Available
Heading output
Not reported

LiDAR

Not reported

Additional sensors

Wheel encoder

Name
Hall-effect wheel encoders
Count
3
Rate
10 Hz
Notes
Raw motor measurements and post-processed Ackermann-model wheel odometry are released.

Annotations

Reported annotation availability and task support.

Availability
Not reported
Format
Not reported
Class count
Not reported
Semantic segmentation Not reported
Instance segmentation Not reported
Object detection Not reported
Optical flow Not reported
Depth ground truth Not reported

Citation

Citation key and BibTeX kept at the end of the page for reference.

Citation key
Pire2019_therosariodataset

BibTeX

@article{Pire2019_therosariodataset,
  title = {The {{Rosario}} Dataset: {{Multisensor}} Data for Localization and Mapping in Agricultural Environments},
  shorttitle = {The {{Rosario}} Dataset},
  author = {Pire, Taih{\'u} and Mujica, Mart{\'i}n and Civera, Javier and Kofman, Ernesto},
  year = {2019},
  month = may,
  journal = {The International Journal of Robotics Research},
  volume = {38},
  number = {6},
  pages = {633--641},
  issn = {0278-3649, 1741-3176},
  doi = {10.1177/0278364919841437},
  urldate = {2023-10-04},
  langid = {english}
}

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