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TartanGround dataset by theairlabcmu
⭐ 34.3
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Dataset

TartanGround

by theairlabcmu hf-dataset--theairlabcmu--tartanground
Nexus Index
34.3 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 53
R: Recency 38
Q: Quality 30
Tech Context
Vital Performance
0 DL / 30D
0.0%
Source →
Data Integrity 34.3 FNI Score
- Size
- Rows
Parquet Format
- Tokens
Dataset Information Summary
Entity Passport
Registry ID hf-dataset--theairlabcmu--tartanground
License CC-BY-4.0
Provider huggingface
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Cite this dataset

Academic & Research Attribution

BibTeX 
@misc{hf_dataset__theairlabcmu__tartanground,
  author = {theairlabcmu},
  title = {TartanGround Dataset},
  year = {2026},
  howpublished = {\url{https://huggingface.co/datasets/theairlabcmu/tartanground}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
theairlabcmu. (2026). TartanGround [Dataset]. Free2AITools. https://huggingface.co/datasets/theairlabcmu/tartanground

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Full Specifications [+]

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
34.3
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 53
Recency (R) 38
Quality (Q) 30

đŸ’Ŧ Index Insight

FNI V2.0 for TartanGround: Semantic (S:50), Authority (A:0), Popularity (P:53), Recency (R:38), Quality (Q:30).

Free2AITools Nexus Index

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Dataset Specification

TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation

TartanGround Overview

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:

text 
@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}
}

Social Proof

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🛡ī¸ Dataset Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-dataset--theairlabcmu--tartanground
slug
theairlabcmu--tartanground
source
huggingface
author
theairlabcmu
license
CC-BY-4.0
tags
language:en, license:cc-by-4.0, size_categories:10m<n<100m, arxiv:2505.10696, region:us, robotics, perception, navigation, slam, semantic-segmentation, occupancy-mapping, synthetic-data, multimodal, ground-robot, dataset

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag

📊 Engagement & Metrics

downloads
42,346
stars
3
forks
0

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