Vision-Based Large-scale 3D Semantic Mapping for Autonomous Driving Applications
Cheng Q, Zeller N, Cremers D (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 9235-9242
Conference Proceedings Title: Proceedings - IEEE International Conference on Robotics and Automation
Event location: Philadelphia, PA, USA
ISBN: 9781728196817
DOI: 10.1109/ICRA46639.2022.9811368
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.
Involved external institutions
Germany (DE)How to cite
APA:
Cheng, Q., Zeller, N., & Cremers, D. (2022). Vision-Based Large-scale 3D Semantic Mapping for Autonomous Driving Applications. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 9235-9242). Philadelphia, PA, USA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Cheng, Qing, Niclas Zeller, and Daniel Cremers. "Vision-Based Large-scale 3D Semantic Mapping for Autonomous Driving Applications." Proceedings of the 39th IEEE International Conference on Robotics and Automation, ICRA 2022, Philadelphia, PA, USA Institute of Electrical and Electronics Engineers Inc., 2022. 9235-9242.
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