Author: Qingwen Zhang (Kin)
This is our wiki page README, please visit our main branch for more information about the benchmark.
If you want to try the MkDocs locally, the only thing you need is Python and some python package. If you are worrying it will destory your env, you can try virual env or anaconda.
main package [user is only need for sometime, check the issue section]
pip install mkdocs-materialplugin package
pip install mkdocs-minify-plugin mkdocs-git-revision-date-localized-plugin mkdocs-git-authors-plugin mkdocs-videomkdocs serveThis benchmark implementation is based on codes from several repositories as we mentioned in the beginning. Thanks for these authors who kindly open-sourcing their work to the community. Please see our paper reference section to get more information.
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
Please cite our works if you find these useful for your research:
@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}
}
@article{jia2024beautymap,
author={Jia, Mingkai and Zhang, Qingwen and Yang, Bowen and Wu, Jin and Liu, Ming and Jensfelt, Patric},
journal={IEEE Robotics and Automation Letters},
title={BeautyMap: Binary-Encoded Adaptable Ground Matrix for Dynamic Points Removal in Global Maps},
year={2024},
volume={},
number={},
pages={1-8},
doi={10.1109/LRA.2024.3402625}
}
@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={5038-5045},
doi={10.1109/LRA.2024.3387658}
}