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.

本文提出一种仅基于立体相机系统完成 3D 语义映射的完整流程，其中包括全局优化的直接稀疏视觉里程表前端和 GNSS 集成，以及语义 3D 点云标记的后端。作者提出了一种简单但有效的时间投票方案，提高了 3D 点标签的质量和一致性，并在 KITTI-360 数据集上进行了定性和定量评估，结果表明了提出的投票方案的有效性和流水线对于大规模高效 3D 语义映射的能力。另外，作者还展示了流水线的大规模映射能力，展示了由车队收集的数据生成的覆盖 8000 公里道路的大规模语义地图。