为高分辨率车载激光雷达设计实时语义分割框架，兼顾精度与速度。
Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans
- 利用表面法向量作为关键输入特征提升分割性能。
- 在128线车载激光雷达数据上实现高精度实时分割。
- 开源数据集与ROS2代码，适合自动驾驶研发人员使用。
近期研究强调了激光雷达语义分割在辅助驾驶系统和自动驾驶中的关键作用。然而，许多先进方法仍基于过时的低分辨率激光雷达传感器测试，难以满足实时性要求。本文针对现代高分辨率激光雷达，提出一种新型语义分割框架，同时兼顾准确性和实时处理需求。我们采集了一个由前沿128线车载激光雷达在城市交通场景中获得的新数据集，并提出一种利用表面法向量作为强输入特征的分割方法。该方法有效弥合了前沿研究与实际汽车应用之间的差距。此外，我们提供了在研究车辆上运行的机器人操作系统（ROS2）实现。数据集与代码已公开：https://github.com/kav-institute/SemanticLiDAR。
原文摘要 · Abstract (English)
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 However, many state-of-the-art methods are tested on outdated, lower-resolution LiDAR sensors and struggle with real-time constraints.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.
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