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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)
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.
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