#### NOTE
This is a **Hugging Face dataset**. For large datasets, ensure `huggingface_hub>=1.1.3` to avoid rate limits. Learn more in the Hugging Face integration docs.
# Dataset Card for ColoRadar Sample
This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 7 samples. Each sample is one handheld sequence from ColoRadar, stored as a native multimodal MCAP episode.
The source is [ColoRadar](https://arpg.colorado.edu/coloradar/), the 3D millimetre-wave radar dataset from the Autonomous Robotics and Perception Group at the University of Colorado Boulder, converted from ROS 1 bags. A handheld rig carries two FMCW radars, a TI MMWCAS-RF-EVM cascaded imaging radar and a TI AWR1843BOOST single-chip radar, beside an Ouster OS1-64 LiDAR and a Lord Microstrain 3DM-GX5-25 inertial unit. The release holds 52 sequences recorded in seven places: hallways, a lab, a large motion-capture space, outdoor built environments, the narrow and the large passages of an underground mine, and a fast ride along paths and roads. This sample carries one sequence from each place, the shortest of its group.
## Installation
If you haven’t already, install FiftyOne:
```bash
pip install -U fiftyone
```
## Usage
```python
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub(
"Voxel51/ColoRadar-Sample",
name="ColoRadar-Sample",
persistent=True,
)
# Launch the App
session = fo.launch_app(dataset)
```
## Dataset Details
### Dataset Description
7 sequences totalling 13m 46s of recording, 8,263 LiDAR scans holding 382.6 million points, 4,145 cascaded radar frames and 8,340 single-chip radar scans.
- **Curated by:** Autonomous Robotics and Perception Group, University of Colorado Boulder (source release)
- **Funded by:** [More Information Needed]
- **Shared by:** Voxel51 (FiftyOne conversion)
- **Language(s):** Not applicable (sensor data)
- **License:** Apache 2.0
### Dataset Sources
- **Repository:** [ColoRadar](https://arpg.colorado.edu/coloradar/) (source release); [Voxel51/ColoRadar-Sample](https://huggingface.co/datasets/Voxel51/ColoRadar-Sample) (this conversion)
- **Paper:** [ColoRadar: The direct 3D millimeter wave radar dataset](https://doi.org/10.1177/02783649211068535) (The International Journal of Robotics Research, 2022)
- **Demo:** [More Information Needed]
## Uses
### Direct Use
Research on millimetre-wave radar, LiDAR and inertial sensing, including SLAM, in indoor, outdoor and subterranean environments, using the LiDAR-inertial ground-truth trajectory shipped with each sequence.
### Out-of-Scope Use
[More Information Needed]
## Dataset Structure
**Topology**
An ungrouped FiftyOne dataset with `media_type="multimodal"`. One sample is one sequence, and its `filepath` is a `.fo.mcap` file. There are 7 samples, no sample tags, and an empty `dataset.info`. The dataset has no FiftyOne label fields; every signal lives inside the MCAP file as a channel and is shown by the App’s multimodal viewer.
**Sample fields**
| Field | FiftyOne type | Description |
|------------------------------------------------------------------------|-----------------|----------------------------------------------------------------------------------------|
| `id`, `filepath`, `tags`, `metadata`, `created_at`, `last_modified_at` | built-in | Standard FiftyOne sample fields |
| `sequence` | `StringField` | Sequence name, e.g. `arpg_lab_run2` |
| `environment` | `StringField` | The release’s own description of the sequence’s group, e.g. `Indoor built environment` |
| `recorded` | `StringField` | Recording date |
| `duration` | `FloatField` | Sequence length in seconds |
| `num_lidar_scans` | `IntField` | LiDAR scans |
| `num_lidar_points` | `IntField` | LiDAR points across all scans |
| `num_cascade_frames` | `IntField` | Cascaded radar frames |
| `num_cascade_points` | `IntField` | Points derived from the cascaded radar heatmaps across all frames |
| `num_single_chip_scans` | `IntField` | Single-chip radar scans |
| `num_single_chip_points` | `IntField` | Single-chip radar points across all scans |
| `num_imu_samples` | `IntField` | Inertial samples |
| `ground_truth_path_m` | `FloatField` | Length of the LiDAR-inertial ground-truth trajectory in metres |
| `has_motion_capture` | `BooleanField` | Whether the sequence carries a motion-capture pose track |
**Episode contents**
Each MCAP episode contains these channels:
| Channel | Schema or content |
|-------------------------------------------|----------------------------------------------------------------------------------------------------------------------|
| `/lidar-points` | Ouster OS1-64 scans, `foxglove.PointCloud` with `x`, `y`, `z`, `intensity`, `reflectivity` and `ring` |
| `/single-chip-radar-points` | The AWR1843’s own point clouds, `foxglove.PointCloud` with `x`, `y`, `z`, `intensity`, `range` and `doppler` |
| `/cascade-radar-points` | Points derived from each cascaded radar heatmap, `foxglove.PointCloud` with `x`, `y`, `z`, `intensity` and `doppler` |
| `/cascade-radar-bev` | A top-down render of each cascaded radar heatmap, `foxglove.CompressedVideo` |
| `/imu.plot` | The Microstrain inertial unit at about 100 Hz |
| `/ground-truth`, `/ground-truth.plot` | The release’s LiDAR-inertial trajectory as `foxglove.PoseInFrame`, with its position as a plot |
| `/motion-capture`, `/motion-capture.plot` | The Vicon pose of the rig in the motion-capture space, with its position as a plot |
| `/tf` | Each sensor’s pose in the rig’s base frame, `foxglove.FrameTransform` |
| `/sequence` | Names the sequence and its environment |
**Sequences**
| Sequence | Environment | Recorded | Duration | LiDAR scans | Cascade frames | Single-chip scans | Path |
|------------------------|--------------------------------------|------------|------------|---------------|------------------|---------------------|--------|
| `arpg_lab_run2` | Indoor built environment | 2020-12-21 | 1m 41s | 1,014 | 504 | 1,015 | 67 m |
| `aspen_run9` | Large motion capture space | 2021-02-24 | 1m 16s | 765 | 414 | 836 | 42 m |
| `ec_hallways_run4` | Indoor built environment | 2020-12-21 | 1m 30s | 899 | 447 | 902 | 103 m |
| `edgar_army_run5` | Large subterranean environment | 2021-02-23 | 1m 54s | 1,141 | 567 | 1,141 | 108 m |
| `edgar_classroom_run4` | Narrow subterranean environment | 2021-02-23 | 2m 44s | 1,639 | 816 | 1,639 | 184 m |
| `longboard_run1` | High-speed multi-use paths and roads | 2021-02-22 | 2m 57s | 1,767 | 882 | 1,770 | 766 m |
| `outdoors_run8` | Outdoor built environment | 2021-03-02 | 1m 44s | 1,038 | 515 | 1,037 | 121 m |
**Parsing decisions**
- **Why MCAP:** the dataset is one multimodal sample per sequence, so the radars, LiDAR, inertial unit, plots and poses play back on one shared clock.
- **ROS 1 bags:** the bags are ROS 1 and were read without a ROS install.
- **LiDAR and single-chip radar:** the LiDAR scans drop the returns the sensor marks empty, and the single-chip radar’s point clouds are carried as recorded.
- **Cascaded radar points:** the cascaded radar publishes no point cloud, only a heatmap of 128 range by 128 azimuth by 32 elevation cells, each holding an intensity and a Doppler velocity. Its points are derived the way the release’s plotting tool derives them: every cell beyond the tenth range bin whose intensity exceeds a fifth of the way from the frame’s weakest cell to its strongest, placed at the cell’s centre. The release’s tool also thins them on a 0.3 m grid; these are not thinned.
- **Cascaded radar render:** `/cascade-radar-bev` draws each heatmap from above, the strongest cell in each column of elevations on a scale of 30 dB below the frame’s peak, with the radar facing up.
- **Calibration:** `/tf` is the release’s calibration, each sensor’s pose in the rig’s base frame, with the single-chip radar’s heatmap frame `dca_link` placed on its point-cloud frame as the bags place it.
- **Left out:** the raw ADC samples of both radars and the single-chip radar’s heatmaps are not carried, nor are the bags’ own `/tf` stream and the LiDAR’s built-in inertial unit; all are in the release.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Data Collection and Processing
Recorded with a handheld rig carrying a TI MMWCAS-RF-EVM cascaded imaging radar, a TI AWR1843BOOST single-chip radar, an Ouster OS1-64 LiDAR and a Lord Microstrain 3DM-GX5-25 inertial unit. The FiftyOne conversion reads the ROS 1 bags without a ROS install and writes one MCAP episode per bag; see Parsing decisions above for the changes made.
#### Who are the source data producers?
The Autonomous Robotics and Perception Group at the University of Colorado Boulder.
### Annotations
#### Annotation process
The dataset has no human annotations. The ground-truth trajectory in each sequence is the release’s LiDAR-inertial trajectory.
#### Who are the annotators?
Not applicable.
#### Personal and Sensitive Information
[More Information Needed]
## Citation
Work using the dataset should cite:
**BibTeX:**
```bibtex
@article{kramer2022coloradar,
title={ColoRadar: The direct 3D millimeter wave radar dataset},
author={Kramer, Andrew and Harlow, Kyle and Williams, Christopher and Heckman, Christoffer},
journal={The International Journal of Robotics Research},
volume={41},
number={4},
pages={351--360},
year={2022},
doi={10.1177/02783649211068535}
}
```
**APA:**
Kramer, A., Harlow, K., Williams, C., & Heckman, C. (2022). ColoRadar: The direct 3D millimeter wave radar dataset. *The International Journal of Robotics Research, 41*(4), 351-360. https://doi.org/10.1177/02783649211068535
## More Information
The source release is distributed under the Apache License 2.0, and this conversion is distributed under the same license, whose text is in the `LICENSE` file beside this card.
Changes from the source: 7 of the release’s 52 sequences, converted from ROS 1 bags to the FiftyOne MCAP flavor, the LiDAR’s empty returns dropped, points derived from the cascaded radar’s heatmaps with a top-down render of each, the release’s calibration carried as transforms, and the raw ADC samples, the single-chip heatmaps and the bags’ own `/tf` left out.
## Dataset Card Authors
[More Information Needed]
## Dataset Card Contact
[More Information Needed]