Add TII-RATM drone racing flights with paired odometry and capture ground truth ((root))

README.md

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+---
+license: cc-by-4.0
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/**
+annotations_creators: []
+language: en
+size_categories:
+- n<1K
+task_categories:
+- robotics
+pretty_name: TII-RATM Drone Racing (FiftyOne multimodal MCAP)
+tags:
+- fiftyone
+- multimodal
+- mcap
+- drone
+- uav
+- slam
+- visual-inertial-odometry
+- ground-truth
+---
+
+# TII-RATM Drone Racing → FiftyOne (Native Multimodal MCAP)
+
+![preview](preview.gif)
+
+The
+[TII-RATM](https://github.com/tii-racing/tii-ratm-dataset)
+drone racing flights, as published with OpenVINS odometry in
+[alvgaona/tii-ratm-rosbag2](https://huggingface.co/datasets/alvgaona/tii-ratm-rosbag2),
+converted to native multimodal MCAP episodes.
+
+A quadrotor flies laps of a four-gate indoor track, three on an ellipse and
+three on a lemniscate. It carries a fisheye camera and a 500 Hz IMU, and a
+motion capture system watches the room throughout.
+
+What makes the set useful is that both numbers are present: the estimate the
+drone computed from its own camera and IMU, and the reference the capture
+system measured at the same instant. Every episode carries both, plus the
+distance between them.
+
+## Installation
+
+```bash
+pip install fiftyone
+```
+
+## Usage
+
+```python
+import fiftyone as fo
+import fiftyone.utils.huggingface as fouh
+
+dataset = fouh.load_from_hub(
+    "Voxel51/TII-RATM-Drone-Racing",
+    name="TII-RATM-Drone-Racing",
+    persistent=True,
+)
+fo.launch_app(dataset)
+```
+
+The flights the odometry found hardest:
+
+```python
+view = dataset.sort_by("tracking_error_rmse_m", reverse=True)
+```
+
+## What you get
+
+Six flights, 10.9 minutes and 2,563 metres flown. Each episode carries:
+
+- `/camera`, the onboard fisheye view at 640x480 and ~26 Hz, as
+  `foxglove.CompressedVideo`
+- `/feature-tracks`, the same view with the points the odometry is tracking
+  drawn on it
+- `/camera-calibration`, the equidistant fisheye intrinsics
+- `/imu.plot`, accelerometer and gyroscope at ~490 Hz
+- `/pose-ground-truth`, the motion capture pose
+- `/pose-vio`, the odometry pose, in the same frame
+- `/tracking-error.plot`, the distance between the two at each capture pose
+- `/trajectory`, both flown paths as line strips
+- `/points-slam` and `/points-msckf`, the feature points the estimator is
+  holding
+- `/gates`, the four track gates as boxes
+- `/gate-range.plot`, the distance to each gate
+- `/tf`, the odometry and camera frames
+- `/instruction`, the track being flown
+
+Across the whole set that comes to 17,305 camera frames, 9,658 feature-track
+frames, **327,086 IMU samples**, 293,219 odometry poses and 179,283 capture
+poses.
+
+Episodes carry the fields `flight`, `track`, `distance_m`, `mean_speed_ms`,
+`max_speed_ms`, `tracking_error_rmse_m`, `tracking_error_median_m`,
+`tracking_error_max_m`, `tracking_error_final_m`, `num_camera_frames`,
+`num_feature_track_frames`, `num_imu_samples`, `num_vio_poses`,
+`num_ground_truth_poses`, `num_gates` and `duration`.
+
+| Flight | Track | Distance | ATE RMSE | ATE max |
+|---|---|---|---|---|
+| flight-03p-ellipse | ellipse | 380 m | 0.612 m | 2.349 m |
+| flight-02p-ellipse | ellipse | 446 m | 0.634 m | 3.455 m |
+| flight-09p-lemniscate | lemniscate | 447 m | 0.642 m | 1.699 m |
+| flight-08p-lemniscate | lemniscate | 367 m | 0.674 m | 2.194 m |
+| flight-01p-ellipse | ellipse | 479 m | 1.072 m | 3.482 m |
+| flight-07p-lemniscate | lemniscate | 444 m | 1.656 m | 3.784 m |
+
+## Notes on the conversion
+
+The odometry and the capture system report in unrelated frames, since the
+estimator starts at its own origin with an arbitrary yaw. A rigid transform
+is fitted over every time-matched pose in the flight and applied to the
+odometry, which is the usual way an absolute trajectory error is measured.
+Scale is not fitted: the estimate is metric because it is inertial-aided, so
+solving for scale would hide drift rather than measure it.
+
+Because that fit spreads the residual across the whole flight,
+`/tracking-error.plot` does not start at zero and does not climb steadily. It
+is the distance from the reference at each instant, not the distance from a
+shared starting point. On `flight-08p-lemniscate` it runs 1.26 m at the start,
+dips to 0.15 m mid-flight and reaches 2.19 m by touchdown, where the estimate
+places the drone 1.4 m below the floor it has landed on.
+
+Video is re-encoded to Annex-B H.264 without B-frames. The source carries raw
+`bgr8` frames, which is most of its 44 GB.
+
+The camera uses an equidistant (Kannala-Brandt) fisheye model. The intrinsics
+are published as they were calibrated, and a viewer that assumes a pinhole or
+plumb-bob model will not undistort this correctly.
+
+Two source streams are not carried. `loop_depth` is typed as a 16-bit depth
+image but only a few dozen of its 307,200 pixels are ever set, so it is a
+sparse projection of loop-closure features rather than a depth map, and
+`points_aruco` is empty in every message of every flight.
+
+## License & attribution
+
+The source release is distributed under
+[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/), and this conversion
+is distributed under the same license.
+
+Changes from the source: conversion to the FiftyOne MCAP flavor, re-encoding
+of the video to H.264, alignment of the odometry onto the capture frame, and
+encoding of the pose, IMU and detection streams as message streams.

fiftyone.yml

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+description: Six indoor drone racing flights carrying an onboard fisheye camera,
+  a 500 Hz IMU, OpenVINS visual-inertial odometry and motion capture ground
+  truth on one clock, with the per-pose tracking error between them, converted
+  to native multimodal MCAP episodes
+fiftyone:
+  version: '>=1.19.0'
+format: FiftyOneDataset
+license: cc-by-4.0
+name: TII-RATM-Drone-Racing
+tags:
+- drone
+- fiftyone
+- ground-truth
+- mcap
+- multimodal
+- slam
+- uav
+- visual-inertial-odometry

metadata.json

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samples.json

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