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DUFOMap: Efficient Dynamic Awareness Mapping

arXiv page poster [video coming soon]

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

🚀 2024-11-20: Update dufomap Python API from 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 unified format first follow this wiki page.

0. Setup

sudo apt update && sudo apt install gcc-10 g++-10
sudo apt install libtbb-dev liblz4-dev

Clone quickly and init submodules:

git clone --recursive -b main --single-branch https://github.com/KTH-RPL/dufomap.git

Or you can directly build docker image through our Dockerfile:

docker build -t dufomap .

1. Build & Run

Build:

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 or format your own dataset follow custom dataset section.

wget https://zenodo.org/records/8160051/files/00.zip -p data
unzip data/00.zip -d data

Run:

./build/dufomap_run data/00 assets/config.toml

dufomap

2. Evaluation

Please reference to DynamicMap_Benchmark for the evaluation of DUFOMap and comparison with other dynamic removal methods.

Evaluation Section link

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) funded by the Knut and Alice Wallenberg Foundation including the WASP NEST PerCorSo.

Feel free to explore below projects that use ufomap (attach code links as follows):

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}
}