Add 13 files (1/1)

.gitattributes

@@ -58,3 +58,10 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/arpg_lab_run2.fo.mcap filter=lfs diff=lfs merge=lfs -text
+data/aspen_run9.fo.mcap filter=lfs diff=lfs merge=lfs -text
+data/ec_hallways_run4.fo.mcap filter=lfs diff=lfs merge=lfs -text
+data/edgar_army_run5.fo.mcap filter=lfs diff=lfs merge=lfs -text
+data/edgar_classroom_run4.fo.mcap filter=lfs diff=lfs merge=lfs -text
+data/longboard_run1.fo.mcap filter=lfs diff=lfs merge=lfs -text
+data/outdoors_run8.fo.mcap filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,202 @@
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README.md

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+---
+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
+---
+
+# ColoRadar sample → FiftyOne (Native Multimodal MCAP)
+
+![preview](preview.gif)
+
+7 sequences from [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 to native multimodal MCAP episodes.
+
+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.
+
+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.
+
+## Installation
+
+```bash
+pip install fiftyone
+```
+
+## Usage
+
+```python
+import fiftyone as fo
+import fiftyone.utils.huggingface as fouh
+
+dataset = fouh.load_from_hub(
+    "Voxel51/ColoRadar-Sample",
+    name="ColoRadar-Sample",
+    persistent=True,
+)
+fo.launch_app(dataset)
+```
+
+The sequences that covered the most ground:
+
+```python
+view = dataset.sort_by("ground_truth_path_m", reverse=True)
+fo.launch_app(dataset, view=view)
+```
+
+## What you get
+
+Each episode contains:
+
+- `/lidar-points`, the Ouster OS1-64 scans, as `foxglove.PointCloud` with `x`, `y`, `z`,
+  `intensity`, `reflectivity` and `ring`
+- `/single-chip-radar-points`, the AWR1843's own point clouds, as `foxglove.PointCloud` with
+  `x`, `y`, `z`, `intensity`, `range` and `doppler`
+- `/cascade-radar-points`, points derived from each cascaded radar heatmap, as
+  `foxglove.PointCloud` with `x`, `y`, `z`, `intensity` and `doppler`
+- `/cascade-radar-bev`, a top-down render of each cascaded radar heatmap, as
+  `foxglove.CompressedVideo`
+- `/imu.plot`, the Microstrain inertial unit at about 100 Hz
+- `/ground-truth`, the release's LiDAR-inertial trajectory, as `foxglove.PoseInFrame`, with its
+  position on `/ground-truth.plot`
+- `/motion-capture`, the Vicon pose of the rig in the motion-capture space, with its position on
+  `/motion-capture.plot`
+- `/tf`, each sensor's pose in the rig's base frame, as `foxglove.FrameTransform`
+- `/sequence`, naming the sequence and its environment
+
+| 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 |
+
+Episodes carry the fields `sequence`, `environment`, `recorded`, `duration`, `num_lidar_scans`, `num_lidar_points`, `num_cascade_frames`, `num_cascade_points`, `num_single_chip_scans`, `num_single_chip_points`, `num_imu_samples`, `ground_truth_path_m` and `has_motion_capture`. `environment` is the release's own description of each sequence's group, and `ground_truth_path_m` the length of the LiDAR-inertial trajectory.
+
+## Notes on the conversion
+
+The bags are ROS 1 and were read without a ROS install. The LiDAR scans drop the returns the sensor marks empty, and the single-chip radar's point clouds are carried as recorded.
+
+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. `/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.
+
+`/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. 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.
+
+## License & attribution
+
+The source release is distributed under the Apache License 2.0, and this conversion is distributed under the same license, whose text is in `LICENSE` beside this card. Cite:
+
+```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}
+}
+```
+
+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.

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fiftyone.yml

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+description: ColoRadar sample, 7 handheld sequences pairing a cascaded imaging
+  radar and a single-chip radar with a 64-beam LiDAR and an inertial unit,
+  from hallways, a lab and a motion-capture space to an underground mine and a
+  fast ride along paths, converted to native multimodal MCAP episodes
+fiftyone:
+  version: '>=1.19.0'
+format: FiftyOneDataset
+license: apache-2.0
+name: ColoRadar-Sample
+tags:
+- fiftyone
+- imu
+- lidar
+- mcap
+- mmwave
+- multimodal
+- radar
+- robotics
+- slam
+- subterranean

metadata.json

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samples.json

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