dataset upload

README.md

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-license: cc-by-nc-sa-4.0
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+<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}
+}
+```

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