theairlabcmu/TartanGround
TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation Dataset Description TartanGround is a large-scale, multi-modal dataset designed to advance the perception and autonomy of ground robots operating in diverse environments. Collected across 63 photorealistic simulation environments, it provides comprehensive data streams for various robotic tasks. Key Features Environments: 63 diverse simulation environments categorized… See the full description on the dataset page: https://huggingface.co/datasets/theairlabcmu/TartanGround.
TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation
Dataset Description
TartanGround is a large-scale, multi-modal dataset designed to advance the perception and autonomy of ground robots operating in diverse environments. Collected across 63 photorealistic simulation environments, it provides comprehensive data streams for various robotic tasks.
Key Features
- Environments: 63 diverse simulation environments categorized into:
- Indoor
- Nature
- Rural
- Urban
- Industrial/Infrastructure
- Historical/Thematic
- Trajectories: 878 trajectories captured across the environments.
- Samples: Over 1.44 million samples.
- Robot Platforms:
- Omnidirectional (
P0000,P0001, ...) - Differential Drive (
P1000,P1001, ...) - Quadrupedal (
P2000,P2001, ...)
- Sensor Modalities:
- RGB Stereo Camera Pairs (front, back, left, right, top, bottom)
- Depth Maps
- Semantic Segmentation
- Optical Flow
- Stereo Disparity
- LiDAR Point Clouds
- IMU Data
- Ground Truth Poses (6-DOF)
- Semantic Occupancy Maps (3D voxel grids)
- Proprioceptive Data (for quadruped trajectories)
Applications
TartanGround supports a wide range of robotic perception and navigation tasks, including:
- Semantic Occupancy Prediction
- Open-Vocabulary Occupancy Prediction
- Visual SLAM
- Neural Scene Representation
- Bird's-eye-view Prediction
- Navigation and more
License
The dataset is licensed under the Creative Commons Attribution 4.0 International License.
Citation
If you use TartanGround in your research, please cite the following paper:
@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}
}