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+<p>
+  <h1 align="center">DUFOMap: Efficient Dynamic Awareness Mapping</h1>
+</p>
+
+[![arXiv](https://img.shields.io/badge/arXiv-2403.01449-b31b1b?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2403.01449)
+[![page](https://img.shields.io/badge/Project-Page-green)](https://KTH-RPL.github.io/dufomap)
+[![poster](https://img.shields.io/badge/RAL2024|Poster-6495ed?style=flat&logo=Shotcut&logoColor=wihte)](https://mit-spark.github.io/Longterm-Perception-WS/assets/proceedings/DUFOMap/poster.pdf)
+[![video](https://img.shields.io/badge/video-YouTube-FF0000?logo=youtube&logoColor=white)](https://youtu.be/isDnAVoVD5M)
+
+Quick Demo: Run with the **same parameter setting** without tuning for different sensor (e.g 16, 32, 64, and 128 channel LiDAR and Livox-series mid360), the following shows the data collected from:
+
+| Leica-RTC360 | 128-channel LiDAR | Livox-mid360 |
+| ------- | ------- | ------- |
+| ![](assets/imgs/dufomap_leica.gif) | ![](assets/imgs/doals_train_128.gif) | ![](assets/imgs/two_floor_mid360.gif) |
+<!-- | ------- | ------- | ------- | -->
+
+🚀 2024-11-20: Update dufomap Python API from [SeFlow](https://github.com/KTH-RPL/SeFlow) try it now! `pip install dufomap` and run `python main.py --data_dir data/00` to get the cleaned map directly. Support all >=Python 3.8 in Windows and Linux. Please extract your own data to **the unified format** first follow [this wiki page](https://kth-rpl.github.io/DynamicMap_Benchmark/data/creation/#custom-data).
+
+
+Clone quickly and init submodules:
+```bash
+git clone --recursive -b main --single-branch https://github.com/KTH-RPL/dufomap.git
+
+
+# The easiest way to run DUFOMap:
+pip install dufomap
+python main.py --data_dir data/00
+```
+
+
+### Dependencies
+
+If you want to compile the C++ source version, please install the following dependencies:
+
+```bash
+sudo apt update && sudo apt install gcc-10 g++-10
+sudo apt install libtbb-dev liblz4-dev
+```
+
+Or you can directly build docker image through our [Dockerfile](Dockerfile):
+```bash
+docker build -t dufomap .
+```
+
+### 1. Build & Run
+
+Build:
+
+```bash
+cmake -B build -D CMAKE_CXX_COMPILER=g++-10 && cmake --build build
+```
+
+Prepare Data: Teaser data (KITTI 00: 384.4Mb) can be downloaded via follow commands, more data detail can be found in the [dataset section](https://kth-rpl.github.io/DynamicMap_Benchmark/data) or format your own dataset follow [custom dataset section](https://kth-rpl.github.io/DynamicMap_Benchmark/data/creation/#custom-data).
+
+```bash
+wget https://zenodo.org/records/8160051/files/00.zip -p data
+unzip data/00.zip -d data
+```
+
+Run:
+
+```bash
+./build/dufomap_run data/00 assets/config.toml
+```
+
+![dufomap](assets/demo.png)
+
+## 2. Evaluation
+
+Please reference to [DynamicMap_Benchmark](https://github.com/KTH-RPL/DynamicMap_Benchmark) for the evaluation of DUFOMap and comparison with other dynamic removal  methods.
+
+[Evaluation Section link](https://github.com/KTH-RPL/DynamicMap_Benchmark/blob/master/scripts/README.md#evaluation)
+
+
+## Acknowledgements
+
+Thanks to HKUST Ramlab's members: Bowen Yang, Lu Gan, Mingkai Tang, and Yingbing Chen, who help collect additional datasets. 
+
+This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program ([WASP](https://wasp-sweden.org/)) funded by the Knut and Alice Wallenberg Foundation including the WASP NEST PerCorSo.
+
+Feel free to explore below projects that use [ufomap](https://github.com/UnknownFreeOccupied/ufomap) (attach code links as follows):
+- [RA-L'24 DUFOMap, Dynamic Awareness]()
+- [RA-L'23 SLICT, SLAM](https://github.com/brytsknguyen/slict)
+- [RA-L'20 UFOMap, Mapping Framework](https://github.com/UnknownFreeOccupied/ufomap)
+
+### Citation
+
+Please cite our works if you find these useful for your research.
+
+```
+@article{daniel2024dufomap,
+  author={Duberg, Daniel and Zhang, Qingwen and Jia, MingKai and Jensfelt, Patric},
+  journal={IEEE Robotics and Automation Letters}, 
+  title={{DUFOMap}: Efficient Dynamic Awareness Mapping}, 
+  year={2024},
+  volume={9},
+  number={6},
+  pages={1-8},
+  doi={10.1109/LRA.2024.3387658}
+}
+@article{duberg2020ufomap,
+  author={Duberg, Daniel and Jensfelt, Patric},
+  journal={IEEE Robotics and Automation Letters}, 
+  title={{UFOMap}: An Efficient Probabilistic 3D Mapping Framework That Embraces the Unknown}, 
+  year={2020},
+  volume={5},
+  number={4},
+  pages={6411-6418},
+  doi={10.1109/LRA.2020.3013861}
+}
+@inproceedings{zhang2023benchmark,
+  author={Zhang, Qingwen and Duberg, Daniel and Geng, Ruoyu and Jia, Mingkai and Wang, Lujia and Jensfelt, Patric},
+  booktitle={IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, 
+  title={A Dynamic Points Removal Benchmark in Point Cloud Maps}, 
+  year={2023},
+  pages={608-614},
+  doi={10.1109/ITSC57777.2023.10422094}
+}
+```