UGV

FoMo

Multi-season robot localization and SLAM

A boreal-forest UGV dataset with over 64 km of repeated seasonal routes, paired lidars, stereo and rear camera imagery, radar, inertial measurements, audio, power and weather metadata, and GNSS-derived position references.

Metadata: Complete Metadata needs review

Quick facts

Vehicle type
UGV
Environment
Outdoor
Data origin
Real-world
Sensor count
12
Ground truth
Available
Calibration
Full Raw Calibration Data
Annotations
Not reported

Sensor overview

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

Ground truth

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

Ground-truth references for FoMo
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 Other Three-rover PPK GNSS with static base and CORS correction · Emlid M2 3 onboard GNSS reference receivers Not reported 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.
Hybrid
Accuracy notes
Centimeter-scale position errors in open areas, tens of centimeters under canopy, especially Green. Reported covariance underestimates true errors. Orientation is unreliable and represented by identity quaternions.
Reference notes
Three 10 Hz rover GNSS recordings are processed with Emlid Studio/RTKLib against an Emlid Reach RS3 base, itself corrected using a CORS station. The fused position and separate covariance files are supplied for each recording. The final reference rate is not separately asserted. Raw RINEX is available for verification. GNSS timestamps need not coincide with sensor timestamps. Reference apparatus is excluded from input sensors.

Calibration and synchronization

Reported calibration level, reproducibility signals, and supporting notes.

Level
Full Raw Calibration Data
Processed parameters
Available
Raw calibration data
Available
Calibration targets
Checkerboard, retro-reflective circular target and AprilTag validation screen

Reproducible calibration data

Raw calibration data is reported, so users can reproduce or inspect the calibration process.

Notes

Per-recording calibration parameters and downloadable calibration sequences. Allan variance IMU characterization uses 24-hour data. Basler intrinsics use a checkerboard; ZED X intrinsics are manufacturer-provided. A Hesai PandarXT-32 is used to bridge Basler extrinsics and is calibration equipment, not a normal route lidar. Radar alignment optimizes x, y and yaw.

Known or intentional limitations

Reported constraints and characteristics to check before using the dataset.

Ground truth contains position only; identity quaternions do not encode measured attitude.

Reference covariances underestimate true position errors, particularly under forest canopy.

Some route-deployment pairs are absent and the winter Green route changes.

Metadata CSVs are excluded from generated MCAP bags.

Basler auto-exposure failed in one cold recording.

Sensor scans are not compensated for ego motion.

Cameras

zed_x

Model
ZED X
Setup
Stereo
Count
1
Effective cameras
2
Modality
Not Reported
Resolution
1920 x 1200
Rate
10 Hz
Shutter
Not Reported
Lens type
Not Reported
HFOV
Not reported
VFOV
Not reported
Notes
Rectified PNG images. Driver timestamps have an estimated 51.48 ms mean delay with 8.78 ms standard deviation. The 56 Hz setting belongs to calibration, rather than normal route recordings.

basler

Model
Basler ace2
Setup
Mono
Count
1
Effective cameras
1
Modality
Not Reported
Resolution
1920 x 1200
Rate
10 Hz
Shutter
Not Reported
Lens type
Not Reported
HFOV
Not reported
VFOV
Not reported
Notes
Rear-facing wide-lens images are undistorted PNGs. Intrinsic calibration emphasizes the ground behind the robot. One cold-weather recording has an auto-exposure failure producing a white image.

IMUs

Xsens MTi-30

Model
Xsens MTi-30
Count
1
Accel / gyro
Not reported

VectorNav VN100

Model
VectorNav VN100
Count
1
Accel / gyro
Not reported

GNSS

Not reported

LiDAR

RoboSense Ruby Plus

Model or name
RoboSense Ruby Plus
Count
1
Dimensions
3
Rate
10 Hz
Channels
Not reported
Range
Not reported

Leishen LS128S1

Model or name
Leishen LS128S1
Count
1
Dimensions
3
Rate
10 Hz
Channels
Not reported
Range
Not reported

Radar

Navtech CIR-304H

Count
1
Released data
Scanning power images
Frame rate
4 Hz
Doppler velocity
Not reported
Elevation angle
Not reported
Configured range
Not reported
Field of view
Not reported
File format
Oxford-format polar PNG
Notes
Two-dimensional polar scans with per-azimuth timestamps and rotational encoder values. No ego-motion compensation. SDK conversion to Cartesian images does not establish released point clouds or Doppler measurements.

Pitot tube

Not reported

Additional sensors

Barometer

Model
Adafruit DPS310
Count
1
Rate
100 Hz
Notes
Pressure data are in metadata CSVs, which are excluded from generated MCAP files.

Other sensor

Name
Left and right microphones
Count
2
Rate
16000 Hz
Notes
Independent left and right WAV channels, stored as one-second snippets; Table 2 and Figure 6 disagree on model.

Odometry

Name
Differential-drive wheel odometry
Count
1
Notes
Odometry computed from the ideal differential-drive model.

Wheel encoder

Model
Hall effect sensors
Count
2
Rate
4 Hz

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
Not reported

BibTeX

Not reported

Dataset corrections

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