Semantic Segmentation Based Rain and Fog Filtering Only by LiDAR Point Clouds

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.