Abstract
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.
Abstract (translated)
无人驾驶车辆依赖激光雷达传感器感知环境。如雨、雪和雾等不良天气条件会消极影响这些传感器,通过在测量中引入不必要的噪声,降低其可靠性。在本研究中,我们解决这个问题并提出了一种新的方法来检测激光雷达数据中的不良天气效应。我们将这个问题重新定义为异常检测任务,并使用基于能量的框架来检测点云中的异常。更具体地说,我们的算法学习将低能量评分与正常 points 关联,并将高能量评分与异常点关联,以 robust 地检测不良天气效应。在广泛的实验中,我们表明,我们的算法在不良天气检测方面表现更好,对未观测到的天气效应的鲁棒性比先前的先进方法更高。此外,我们展示如何应用我们的算法进行同时的异常检测和语义分割。最后,为了扩大激光雷达在不良天气条件下的感知研究领域,我们发布了语义喷雾数据集,其中包含道路类似场景中标注的车辆行驶喷雾数据。
URL
https://arxiv.org/abs/2305.16129