悪天候下におけるLiDAR知覚のサーベイ
Survey on LiDAR Perception in Adverse Weather Conditions
霧・雪・雨などの悪天候がLiDARの知覚性能に与える影響と、その対策（データ整備、点群ノイズ除去、ロバスト知覚、センサフュージョン）に関する研究を整理し、課題と今後の方向性を示したサーベイ論文。
著者: Mariella Dreissig, Dominik Scheuble, Florian Piewak, Joschka Boedecker
分類: cs.RO, cs.CV
原文アブストラクト
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.
