论文标题
基于视觉的大规模3D语义映射，用于自动驾驶应用
Vision-based Large-scale 3D Semantic Mapping for Autonomous Driving Applications
论文作者
论文摘要
在本文中，我们为仅基于立体摄像机系统的3D语义映射提供了完整的管道。该管道包括直接的稀疏视觉探光前端以及用于全局优化的后端，包括GNSS集成和语义3D点云标签。我们提出了一个简单但有效的时间投票计划，以提高3D点标签的质量和一致性。我们的管道的定性和定量评估是在Kitti-360数据集上进行的。结果表明，我们提出的投票计划的有效性以及管道有效的大规模3D语义映射的能力。此外，我们的管道的大规模映射功能通过介绍了一张非常大的语义地图，涵盖了由车队机队收集的数据产生的8000公里的道路。
In this paper, we present a complete pipeline for 3D semantic mapping solely based on a stereo camera system. The pipeline comprises a direct sparse visual odometry front-end as well as a back-end for global optimization including GNSS integration, and semantic 3D point cloud labeling. We propose a simple but effective temporal voting scheme which improves the quality and consistency of the 3D point labels. Qualitative and quantitative evaluations of our pipeline are performed on the KITTI-360 dataset. The results show the effectiveness of our proposed voting scheme and the capability of our pipeline for efficient large-scale 3D semantic mapping. The large-scale mapping capabilities of our pipeline is furthermore demonstrated by presenting a very large-scale semantic map covering 8000 km of roads generated from data collected by a fleet of vehicles.
