Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans
This addresses the need for real-time, accurate semantic segmentation in autonomous vehicles, though it is incremental by focusing on modern sensors and practical deployment.
The study tackles semantic segmentation of high-resolution automotive LiDAR data for real-time applications, achieving competitive accuracy with a method that uses surface normals as input features and includes a public dataset and ROS2 implementation.
In recent studies, numerous previous works emphasize the importance of semantic segmentation of LiDAR data as a critical component to the development of driver-assistance systems and autonomous vehicles. However, many state-of-the-art methods are tested on outdated, lower-resolution LiDAR sensors and struggle with real-time constraints. This study introduces a novel semantic segmentation framework tailored for modern high-resolution LiDAR sensors that addresses both accuracy and real-time processing demands. We propose a novel LiDAR dataset collected by a cutting-edge automotive 128 layer LiDAR in urban traffic scenes. Furthermore, we propose a semantic segmentation method utilizing surface normals as strong input features. Our approach is bridging the gap between cutting-edge research and practical automotive applications. Additionaly, we provide a Robot Operating System (ROS2) implementation that we operate on our research vehicle. Our dataset and code are publicly available: https://github.com/kav-institute/SemanticLiDAR.