DENOSAU
Point Cloud Denoising in Adverse Weather Conditions for Autonomous Driving
News
[16 Jul 2021]: Our new paper has been accepted by IEEE Transactions on Intelligent Transportation Systems!
[12 Nov 2020]: Check out our ROS World 2020 Lightning Talk presented by Dr. Tao Yang!
[1 Jul 2020]: Our very proud Dr. Tao Yang is now an associate professor at NPU in China!
[1 Jul 2020]: Our paper has been accepted at IROS 2020!
[20 Dec 2019]: The project is successfully concluded and all milestones are reached!
[06 May 2019]: Dr. Tao Yang joined us as postdoc!
[17 Apr 2019]: The project homepage is online!
[01 Apr 2019]: The project is kicked off!
About
DENOSAU is a joint research project between the Groupe Renault and the University of Technology of Belfort-Montbéliard (UTBM) in France, which assesses the impact of severe weather conditions on 3D lidar data and develop new solutions that can integrate with current hardware resources, extending the state-of-the-art to achieve efficient 3D lidar data denoising. Scientifically, DENOSAU aims to address one of the most pressing problems in autonomous driving, i.e. object detection and tracking, via the study of denoising method for point cloud generated by the 3D lidar under adverse weather conditions.
Partners
Groupe Renault
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| You Li (PI) Innovation Pilot |
UTBM
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| Zhi Yan (PI) Assistant Professor |
Yassine Ruichek (Co-I) Professor |
Tao Yang Postdoc |
Publications
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Tao Yang, You Li, Yassine Ruichek, and Zhi Yan. Performance modeling a near-infrared ToF LiDAR under fog: A data-driven approach. IEEE Transactions on Intelligent Transportation Systems, August 2021.
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Tao Yang, You Li, Yassine Ruichek, and Zhi Yan. LaNoising: A data-driven approach for 903nm ToF LiDAR performance modeling under fog. In Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Online, October 2020.
Results
UTBM-Renault joint research project, 2019 (9 months).
