Datasets:

Voxel51
/

ColoRadar-Sample

---
license: apache-2.0
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/**
annotations_creators: []
language: en
size_categories:
- n<1K
task_categories:
- robotics
pretty_name: ColoRadar sample (FiftyOne multimodal MCAP)
tags:
- fiftyone
- multimodal
- mcap
- robotics
- radar
- mmwave
- lidar
- imu
- slam
- subterranean
dataset_summary: |
  ![preview](preview.gif)
  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 7 samples.

  ## 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 Card for ColoRadar Sample

![preview](preview.gif)

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]

	---
	license: apache-2.0
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/**
	annotations_creators: []
	language: en
	size_categories:
	- n<1K
	task_categories:
	- robotics
	pretty_name: ColoRadar sample (FiftyOne multimodal MCAP)
	tags:
	- fiftyone
	- multimodal
	- mcap
	- robotics
	- radar
	- mmwave
	- lidar
	- imu
	- slam
	- subterranean
	dataset_summary: \|
	![preview](preview.gif)

	This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 7 samples.

	## 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 Card for ColoRadar Sample

	![preview](preview.gif)

	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]