  Autonomous vehicles rely on a variety of sensors to gather information about
their surrounding. The vehicle's behavior is planned based on the environment
perception, making its reliability crucial for safety reasons. The active LiDAR
sensor is able to create an accurate 3D representation of a scene, making it a
valuable addition for environment perception for autonomous vehicles. Due to
light scattering and occlusion, the LiDAR's performance change under adverse
weather conditions like fog, snow or rain. This limitation recently fostered a
large body of research on approaches to alleviate the decrease in perception
performance. In this survey, we gathered, analyzed, and discussed different
aspects on dealing with adverse weather conditions in LiDAR-based environment
perception. We address topics such as the availability of appropriate data, raw
point cloud processing and denoising, robust perception algorithms and sensor
fusion to mitigate adverse weather induced shortcomings. We furthermore
identify the most pressing gaps in the current literature and pinpoint
promising research directions.
