  LiDAR sensors are critical for autonomous driving and robotics applications
due to their ability to provide accurate range measurements and their
robustness to lighting conditions. However, airborne particles, such as fog,
rain, snow, and dust, will degrade its performance and it is inevitable to
encounter these inclement environmental conditions outdoors. It would be a
straightforward approach to remove them by supervised semantic segmentation.
But annotating these particles point wisely is too laborious. To address this
problem and enhance the perception under inclement conditions, we develop two
dynamic filtering methods called Dynamic Multi-threshold Noise Removal (DMNR)
and DMNR-H by accurate analysis of the position distribution and intensity
characteristics of noisy points and clean points on publicly available WADS and
DENSE datasets. Both DMNR and DMNR-H outperform state-of-the-art unsupervised
methods by a significant margin on the two datasets and are slightly better
than supervised deep learning-based methods. Furthermore, our methods are more
robust to different LiDAR sensors and airborne particles, such as snow and fog.
