TartanGround

# TartanGround → FiftyOne (Native Multimodal MCAP)  Six trajectories from [theairlabcmu/TartanGround](https://huggingface.co/datasets/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 ```bash pip install fiftyone ``` ## Usage ```python 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](https://creativecommons.org/licenses/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 ```bibtex @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} } ```