@inproceedings{cb09b6bddddf4846b703fd4f45974ef6,
title = "Vision-Based Large-scale 3D Semantic Mapping for Autonomous Driving Applications",
abstract = "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 frontend 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.",
keywords = "3D mapping, autonomous driving, semantic segmentation, visual SLAM",
author = "Qing Cheng and Niclas Zeller and Daniel Cremers",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 39th IEEE International Conference on Robotics and Automation, ICRA 2022 ; Conference date: 23-05-2022 Through 27-05-2022",
year = "2022",
doi = "10.1109/ICRA46639.2022.9811368",
language = "English",
series = "Proceedings - IEEE International Conference on Robotics and Automation",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "9235--9242",
booktitle = "2022 IEEE International Conference on Robotics and Automation, ICRA 2022",
}