Other
COVERED
3D semantic segmentation
218 manually annotated point clouds of a collaborative industrial robot workspace, with six semantic classes and four practical scenarios.
Access links
Primary places to inspect, download, read about, or reproduce the dataset.
Quick facts
- Vehicle type
- Other
- Environment
- Indoor
- Data origin
- Real-world
- Sensor count
- 4
- Ground truth
- Not reported
- Calibration
- Not reported
- Annotations
- Available
Sensor overview
High-level sensor availability before the detailed sensor records below.
Ground truth
Reference scope, method, rate, coverage, and provenance.
Not reported
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 reportedKnown or intentional limitations
Reported constraints and characteristics to check before using the dataset.
Robot subclasses are not distinguished; the authors identify more classes and scenarios as future extensions.
Cameras
Not reported
IMUs
Not reported
GNSS
Not reported
LiDAR
Ouster OS0-128
- Model or name
- Ouster OS0-128
- Count
- 4
- Dimensions
- 3
- Rate
- Not reported
- Channels
- Not reported
- Range
- Not reported
Radar
Not reported
Pitot tube
Not reported
Additional sensors
Not reported
Annotations
Reported annotation availability and task support.
- Availability
- Available
- Format
- pcd; npy
- Class count
- 6
Semantic segmentation
Available
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.
@misc{munasinghe2023covered,
title={COVERED, CollabOratiVE Robot Environment Dataset for 3D Semantic segmentation},
author={Munasinghe, Charith and Mohammadi Amin, Fatemeh and Scaramuzza, Davide and van de Venn, Hans Wernher},
year={2023},
eprint={2302.12656},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2302.12656v2}
}
Dataset corrections
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