List of Datasets

Handheld

Wild-Places

Lidar place recognition

Paper-era eight-sequence forest place-recognition collection with processed lidar submaps and SLAM-derived poses over fourteen months. Maintained releases revise trajectories and submaps.

Metadata: Complete Metadata curator reviewed Released 2022

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

Supplementary

Not reported

Quick facts

Vehicle type
Handheld
Environment
Outdoor
Data origin
Real-world
Sensor count
1
Ground truth
Available
Calibration
Not reported
Annotations
Not reported

Sensor overview

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

Cameras

Not reported

IMUs

Not reported

GNSS

Not reported

LiDAR

1 reported

Radar

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.
6DoF LiDAR SLAM Not reported Not reported 2 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.
Derived
Accuracy notes
The paper cites 0.3 m mean error from another Wildcat study and reports >95% of map point correspondences below 0.95 m in two comparisons. Neither establishes independent per-pose accuracy for this release.
Reference notes
Wildcat continuous-time lidar-inertial SLAM estimates trajectories and pose-graph loop closures; ICP and MSAC align sequence maps. poses.csv stores timestamps, XYZ and quaternion XYZW. This is one coherent derived reference pipeline.

Calibration and synchronization

Reported calibration level, reproducibility signals, and supporting notes.

Level
Not reported
Processed parameters
Not reported
Raw calibration data
Not reported
Calibration targets
Not reported

Calibration data

Calibration reproducibility details are not reported.

Notes

Not reported

Known or intentional limitations

Reported constraints and characteristics to check before using the dataset.

Reference poses are SLAM-derived; no independent per-pose accuracy guarantee is established.

Reverse revisits, dense repetitive vegetation and long-term environmental change challenge place recognition.

Water or mud can yield no lidar returns in illustrated scenes.

Figure 7 query-sequence descriptions conflict; aggregate length and duration also disagree with the sequence rows.

The eight-sequence count describes the analysed paper collection. Maintained v2 omitted K-04; v3 (November 2025) revises trajectories/submaps, adds intensity and changes file structure. Current archive counts are not established here.

The official project page reports a different aggregate distance and approximate submap count from the paper. Aggregate trajectory length and duration remain unknown.

The CSIRO download portal requires JavaScript; bulk files were not inspected.

Cameras

Not reported

IMUs

Not reported

GNSS

Not reported

LiDAR

VLP-16

Model or name
VLP-16
Count
1
Dimensions
3
Rate
Not reported
Channels
16
Range
120 m

Radar

Not reported

Additional sensors

Not reported

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
knights2023wildplacesv3

BibTeX

@misc{knights2023wildplacesv3,
  title={Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments},
  author={Knights, Joshua and Vidanapathirana, Kavisha and Ramezani, Milad and Sridharan, Sridha and Fookes, Clinton and Moghadam, Peyman},
  year={2023},
  eprint={2211.12732},
  archivePrefix={arXiv},
  primaryClass={cs.RO},
  url={https://arxiv.org/abs/2211.12732v3},
  note={Analyzed arXiv version 3, dated 2 March 2023}
}

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