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.

翻译：LiDAR传感器因其能够提供精确的距离测量以及对光照条件的鲁棒性，在自动驾驶和机器人应用中至关重要。然而，雾、雨、雪、尘等空中颗粒会降低其性能，而在室外环境中难免会遇到这些恶劣天气条件。通过有监督语义分割去除这些颗粒是一种直接的方法，但逐点标注这些颗粒过于繁琐。为解决该问题并增强恶劣条件下的感知能力，我们基于对公开WADS和DENSE数据集上噪声点与清洁点的位置分布及强度特征的精确分析，开发了两种动态滤波方法，分别称为动态多阈值噪声去除（DMNR）和DMNR-H。在两个数据集上，DMNR与DMNR-H均显著优于最先进的无监督方法，并略优于基于有监督深度学习的方法。此外，我们的方法对不同LiDAR传感器以及雪、雾等空中颗粒具有更强的鲁棒性。