hoangcuongbk80 / cfear_radarodometry

Implementation of CFEAR Radarodometry is described in

Lidar-level localization with radar? The CFEAR approach to accurate, fast and robust large-scale radar odometry in diverse environments

Accepted for Transactions On Robotics!!

• A video demo of our latest results is shown here.

CFEAR-3 journal: "Lidar-level localization with radar? The CFEAR approach to accurate, fast and robust large-scale radar odometry in diverse environments"

• Accepted for journal "Transactions On Robotics"
• Preprint of our paper
• A demo is found here. The video intends to visually demonstrate the experiments carried out in the paper
• We release most of our content, including
  • Our diverse radar datasets
  • Our full evaluation which includes source code for paper figures
  • Source code

citation

@misc{https://doi.org/10.48550/arxiv.2211.02445,
  doi = {10.48550/ARXIV.2211.02445},
  url = {https://arxiv.org/abs/2211.02445},
  author = {Adolfsson, Daniel and Magnusson, Martin and Alhashimi, Anas and Lilienthal, Achim J. and Andreasson, Henrik},
  keywords = {Robotics (cs.RO), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Lidar-level localization with radar? The CFEAR approach to accurate, fast and robust large-scale radar odometry in diverse environments},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International}
  booktitle = {To Appear in Transactions on Robotics}
} 

CFEAR-2 Conference article: CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry

Presented at IROS 2021

citation

@INPROCEEDINGS{9636253,  author={Adolfsson, Daniel and Magnusson, Martin and Alhashimi, Anas and Lilienthal, Achim J. and Andreasson, Henrik},
booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
title={CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry},
year={2021},  volume={},  number={},  pages={5462-5469},
doi={10.1109/IROS51168.2021.9636253}}

• Published conference article and preprint
• IROS 2021 presentation

CFEAR-1 workshop presentation: Oriented surface points for efficient and accurate radar odometry

• The initial work on CFEAR was presented at Radar Perception for All-Weather Autonomy, a Half-Day Workshop at 2021 IEEE International Conference on Robotics and Automation (ICRA)
• Workshop preprint
• Workshop presentation

citation

@article{DBLP:journals/corr/abs-2109-09994,
  author    = {Daniel Adolfsson and Martin Magnusson and Anas W. Alhashimi and Achim J. Lilienthal and Henrik Andreasson},
  title     = {Oriented surface points for efficient and accurate radar odometry},
  journal   = {CoRR}, volume    = {abs/2109.09994}, year      = {2021}, url       = {https://arxiv.org/abs/2109.09994}, eprinttype = {arXiv}, eprint    = {2109.09994},
  timestamp = {Mon, 27 Sep 2021 15:21:05 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2109-09994.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
  

Relevant Publications

CorAl: Introspection for robust radar and lidar perception in diverse environments using differential entropy

Learns detection of small localization errors using Navtech radar data

Bibtex

@article{DBLP:journals/corr/abs-2109-09994,
  author    = {Daniel Adolfsson and Martin Magnusson and Anas W. Alhashimi and Achim J. Lilienthal and Henrik Andreasson},
  title     = {Oriented surface points for efficient and accurate radar odometry},
  journal   = {CoRR}, volume    = {abs/2109.09994}, year      = {2021}, url       = {https://arxiv.org/abs/2109.09994}, eprinttype = {arXiv}, eprint    = {2109.09994},
  timestamp = {Mon, 27 Sep 2021 15:21:05 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2109-09994.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
  

Contact

• If you have any questions, feel free to contact me: Daniel Adolfsson (dan11003) [email protected]