TartanGround → FiftyOne (Native Multimodal MCAP)

Six trajectories from theairlabcmu/TartanGround, one per environment (AbandonedFactory, CyberPunkDowntown, GreatMarsh, Hospital, JapaneseCity, NordicHarbor), converted to native multimodal MCAP episodes. Each episode carries the front camera, its segmentation stream, per-frame lidar point clouds, ego pose, and IMU plot channels on a 10 Hz frame clock.

Installation

pip install fiftyone

Usage

import fiftyone as fo
import fiftyone.utils.huggingface as fouh

dataset = fouh.load_from_hub(
    "Voxel51/TartanGround",
    name="TartanGround",
    persistent=True,
)
fo.launch_app(dataset)

What you get

• 6 .mcap episodes of 757 to 3,727 frames
• Streams per episode: /front-camera (JPEG), /front-segmentation (PNG), /lidar (point clouds), /ego-pose, /imu.plot
• Per-episode fields: environment, trajectory, num_frames, duration

License & attribution

The source dataset is released by the CMU AirLab under CC-BY-4.0; this subset is distributed under the same license. Changes from the source: trajectory subsetting, conversion to MCAP, and JPEG transcoding of the RGB frames.

Citation

@article{patel2025tartanground,
  title={TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation},
  author={Patel, Manthan and Yang, Fan and Qiu, Yuheng and Cadena, Cesar and Scherer, Sebastian and Hutter, Marco and Wang, Wenshan},
  journal={arXiv preprint arXiv:2505.10696},
  year={2025}
}