# TII-RATM Drone Racing Indoor drone racing flights pairing onboard visual-inertial odometry with motion capture ground truth, as native `.mcap` episodes. A quadrotor flies laps of a four-gate track, three on an ellipse and three on a lemniscate, carrying a fisheye camera and a 500 Hz IMU while a motion capture system watches the room. Each episode carries both numbers: the estimate the drone computed from its own camera and IMU, and the reference the capture system measured at the same instant. The distance between them is published as a per-pose series, so the error is measurable rather than assumed. Six flights, 10.9 minutes and 2,563 metres flown, over 17,305 camera frames, 327,086 IMU samples, 293,219 odometry poses and 179,283 capture poses. Tracking error ranges from 0.61 m to 1.66 m RMSE across the six. **Details** - Dataset name: `tii-ratm-drone-racing` - Dataset source: [https://huggingface.co/datasets/Voxel51/TII-RATM-Drone-Racing](https://huggingface.co/datasets/Voxel51/TII-RATM-Drone-Racing) - Dataset size: 0.58 GB - Dataset license: CC BY 4.0 - Tags: `multimodal, mcap, drone, uav, slam, ground-truth` - Supported splits: `N/A` - ZooDataset class: [`TIIRATMDroneRacingDataset`](../../api/fiftyone.zoo.datasets.base.md#fiftyone.zoo.datasets.base.TIIRATMDroneRacingDataset) **Example usage** Python CLI ```python import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("tii-ratm-drone-racing") # The flights the odometry found hardest view = dataset.sort_by("tracking_error_rmse_m", reverse=True) # The lemniscate laps view = dataset.match({"track": "lemniscate"}) session = fo.launch_app(dataset) ``` ```shell fiftyone zoo datasets load tii-ratm-drone-racing fiftyone app launch tii-ratm-drone-racing ```