DENOSAU

DENOSAU logo Point Cloud Denoising in Adverse Weather Conditions for Autonomous Driving

News

[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

You Li (PI)
Innovation Pilot

UTBM

Zhi Yan (PI)
Assistant Professor
Yassine Ruichek (Co-I)
Professor
Tao Yang
Postdoc

Publications

  1. 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), Las Vegas, USA, October 2020.

Results


UTBM-Renault joint research project, 2019 (9 months).

renault logo utbm logo