---
license: cc-by-sa-4.0
task_categories:
- object-detection
- robotics
tags:
- Autonomous-Driving
- Egocentric-Perception
- Crowded-Unstructured-Environments
---
<div id="top" align="center">

![robosense](./assets/caption.gif)

<!-- <div align="center"> -->
<img src="assets/icon.png" width="80">
<!-- <h1>RoboSense</h1> -->
<h3>Large-scale Dataset and Benchmark for Egocentric Robot Perception and Navigation in Crowded and Unstructured Environments</h3>

<a href="https://arxiv.org/abs/2408.15503"><img src="https://img.shields.io/badge/arXiv-Paper-<color>"></a> [![License](https://img.shields.io/badge/License-CC_%20_BY--NC--SA_4.0-blue.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/)

</div>



## Description
* RoboSense is a large-scale multimodal dataset constructed to facilitate egocentric robot perception capabilities especially in crowded and unstructured environments.
* It contains more than 133K synchronized data of 3 main types of sensors (Camera, LiDAR and Fisheye), with 1.4M 3D bounding box and IDs annotated in the full $360^{\circ}$ view, forming 216K trajectories across 7.6K temporal sequences.
* It has $270\times$ and $18\times$ as many annotations of surrounding obstacles within near ranges as the previous datasets collected for autonomous driving scenarios such as KITTI and nuScenes. 
* Based on RoboSense, we formulate 6 benchmarks of both perception and prediction tasks to facilitate the future research development.

**For more information, please visit our GitHub repository: https://github.com/suhaisheng/RoboSense.**

## Data Format

**1. Each image file named by the following format:**

```bash
image_{trainval/test}/processed_data_{date}/images/{cam_id}/{timestamp}.jpg
```

where **#cam_id** ranges from 0-7, with 0-3 indicating **Camera** image folder and 4-7 indicating **Fisheye** image folder.

**2. Each Hesai pointcloud named by the following format:**
```bash
lidar_occ_{trainval/test}/processed_data_{date}/hs64/{cam_id}/{timestamp}.bin
```

**3. Each Livox pointcloud named by the following format:**
```bash
lidar_occ_{trainval/test}/processed_data_{date}/livox/{cam_id}/{timestamp}.pcd
```

**4. Each Occupancy annotation file named by the following format:**
```bash
lidar_occ_{trainval/test}/processed_data_{date}/occ/{cam_id}/{timestamp}.npz
```

**5. For the training/validation splits containing 3D box/trajectory annotations/calibrations, please refer to the file path:**

```bash
RoboSense/splits/robosense_local_{train/val}.pkl
```
## License <a name="license"></a>

All assets and code within this repo are under the [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) unless specified otherwise.


## Citation
If you find RoboSense is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry.

```bibtex
@inproceedings{su2025robosense,
  title={RoboSense: Large-scale Dataset and Benchmark for Egocentric Robot Perception and Navigation in Crowded and Unstructured Environments},
  author={Su, Haisheng and Song, Feixiang and Ma, Cong and Wu, Wei and Yan, Junchi},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2025}
}
```