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.

翻译：自动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此类传感器产生负面影响，通过在测量数据中引入非预期噪声降低其可靠性。本文针对该问题，提出一种检测激光雷达数据中恶劣天气效应的新方法。我们将该问题重新表述为异常值检测任务，并采用基于能量的框架来检测点云中的异常点。具体而言，我们的方法通过学习为内点分配低能量值、为异常点分配高能量值，从而实现对恶劣天气效应的鲁棒检测。通过大量实验证明，该方法在恶劣天气检测性能上优于现有最先进方法，且对未见过的天气效应具有更强的鲁棒性。此外，我们还展示了该方法如何实现异常值检测与语义分割的同步处理。最后，为拓展恶劣天气下激光雷达感知的研究领域，我们发布了SemanticSpray数据集，该数据集包含高速公路场景中标注的车辆水雾数据。