TY - GEN
T1 - Semantic Segmentation Based Rain and Fog Filtering Only by LiDAR Point Clouds
AU - Luo, Zhen
AU - Ma, Junyi
AU - Xiong, Guangming
AU - Hu, Xiuzhong
AU - Zhou, Zijie
AU - Xu, Jiahui
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The basis of the autonomy of an intelligent vehicle is that hardware can provide reliable perceptual information. To apply the intelligent vehicles to the field of transportation, a problem that has to be solved is the autonomous driving in adverse weather scenes. Single categories of sensors, such as LiDAR, are often affected by adverse weather, which has led to the development of multi-sensor fusion technology, but has also resulted in increased costs. In this paper, we propose a point clouds denoising method based on semantic segmentation, and advance a post-processing method to improve the performance of the network. We implement a set of software packages under a ROS framework that only needs LiDAR to denoise in adverse weather. The experimental results show that our proposed method outperforms the existing mainstream methods in terms of filtering out rain and fog point clouds and the performance has been improved by 4.1% on MIoU.
AB - The basis of the autonomy of an intelligent vehicle is that hardware can provide reliable perceptual information. To apply the intelligent vehicles to the field of transportation, a problem that has to be solved is the autonomous driving in adverse weather scenes. Single categories of sensors, such as LiDAR, are often affected by adverse weather, which has led to the development of multi-sensor fusion technology, but has also resulted in increased costs. In this paper, we propose a point clouds denoising method based on semantic segmentation, and advance a post-processing method to improve the performance of the network. We implement a set of software packages under a ROS framework that only needs LiDAR to denoise in adverse weather. The experimental results show that our proposed method outperforms the existing mainstream methods in terms of filtering out rain and fog point clouds and the performance has been improved by 4.1% on MIoU.
KW - adverse weather
KW - point clouds
KW - rain and fog denoising
KW - semantic segmentation
UR - http://www.scopus.com/inward/record.url?scp=85146489491&partnerID=8YFLogxK
U2 - 10.1109/ICUS55513.2022.9986567
DO - 10.1109/ICUS55513.2022.9986567
M3 - Conference contribution
AN - SCOPUS:85146489491
T3 - Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022
SP - 90
EP - 95
BT - Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022
A2 - Song, Rong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Unmanned Systems, ICUS 2022
Y2 - 28 October 2022 through 30 October 2022
ER -