Lidar degradation quantification for autonomous driving in rain
Published in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), 2021
Autonomous driving in rainy conditions remains a big challenge. One of the issues is sensor degradation. LiDAR is commonly used in autonomous driving systems to perceive and understand surrounding environments. However, LiDAR performance can be degraded by rain, thereby influencing other system performance (e.g., perception or localization). Therefore, knowing how much degradation exists in current LiDAR measurements is necessary. Most existing methods can only measure LiDAR degradation in controlled environments (e.g., a chamber with simulated rain); how to quantify LiDAR degradation in dynamic environments while the autonomous vehicle is moving is still a difficult problem. In this work, we propose a novel approach to address this problem using an anomaly detection method. Our method has been evaluated on simulated and real-world data. Experimental results demonstrate the effectiveness of our method to capture LiDAR degradation and yield reasonable degradation estimations.
Recommended citation: C. Zhang, Z. Huang, M. H. Ang and D. Rus, “LiDAR Degradation Quantification for Autonomous Driving in Rain,” 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 2021, pp. 3458-3464, doi: 10.1109/IROS51168.2021.9636694.