Improved Scan Matching Performance in Snowy Environments Using Semantic Segmentation
Masahiro Obuchi, Takanori Emaru, Ankit A. Ravankar · 2021
Inclement weather conditions such as snowy environments poses a lot of challenge for autonomous driving. Because of the dynamic changes in the environment, there will be difference between the prior map obtained from a LiDAR system and current sensor data. To overcome this problem, in this study, we present a semantic segmentation based method to recognize the snow cover from the camera images and project the results on the LiDAR point cloud to distinguish the differences. Our results shows improved localization accuracy in snow environment.