Autonomous vehicles rely on LiDAR sensors to perceive the environment. Adverse weather conditions like rain, snow, and fog negatively affect these sensors, reducing their reliability by introducing unwanted noise in the measurements. In this work, we tackle this problem by proposing a novel approach for detecting adverse weather effects in LiDAR data. We reformulate this problem as an outlier detection task and use an energy-based framework to detect outliers in point clouds. More specifically, our method learns to associate low energy scores with inlier points and high energy scores with outliers allowing for robust detection of adverse weather effects. In extensive experiments, we show that our method performs better in adverse weather detection and has higher robustness to unseen weather effects than previous state-of-the-art methods. Furthermore, we show how our method can be used to perform simultaneous outlier detection and semantic segmentation. Finally, to help expand the research field of LiDAR perception in adverse weather, we release the SemanticSpray dataset, which contains labeled vehicle spray data in highway-like scenarios. The dataset is available at http://dx.doi.org/10.18725/OPARU-48815 .

翻译：自主驾驶车辆依赖激光雷达传感器感知环境。雨、雪、雾等恶劣天气条件会对这些传感器产生负面影响，通过引入测量中的非期望噪声降低其可靠性。本文针对该问题提出一种检测激光雷达数据中恶劣天气影响的新方法。我们将该问题重新定义为异常值检测任务，并采用基于能量的框架检测点云中的异常值。具体而言，我们的方法学习将低能量分数分配给正常点，将高能量分数分配给异常值，从而实现对恶劣天气影响的稳健检测。在大量实验中，我们证明该方法在恶劣天气检测方面优于现有最先进方法，且对未见过的天气影响具有更高的鲁棒性。此外，我们还展示了该方法可同时进行异常值检测与语义分割。最后，为拓展恶劣天气下激光雷达感知研究领域，我们发布了 SemanticSpray 数据集，其中包含高速公路场景中的标记车辆水雾数据。该数据集可通过 http://dx.doi.org/10.18725/OPARU-48815 获取。