(video) [3D-DLAD-v4] Vision-based Large-scale 3D Semantic Mapping for Autonomous Driving, Qing Cheng

Workshop Schedule:
https://sites.google.com/view/3d-dlad-v4-iv2022/schedule
Speaker : Qing Cheng
Abstract : 3D perception is one of the most considerable challenges. High-quality 3D maps are a complementary source of information to online perception. 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 labelling. We propose a simple but effective temporally consistent labelling scheme which improves the quality and consistency of the 3D point labels. The whole pipeline runs in real-time. Qualitative and quantitative evaluations of our pipeline are performed on the KITTI-360 dataset. The results show the effectiveness of our proposed temporally consistent labelling scheme and the capability of our pipeline for efficient large-scale 3D semantic mapping. The large-scale mapping capability 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.