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Computer Science > Computer Vision and Pattern Recognition

arXiv:2203.01087 (cs)
[Submitted on 2 Mar 2022]

Title:Vision-based Large-scale 3D Semantic Mapping for Autonomous Driving Applications

Authors:Qing Cheng, Niclas Zeller, Daniel Cremers
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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 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.
Comments: ICRA 2022 Contributed paper
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
ACM classes: I.2.10; I.4.5; I.4.6; I.4.8; I.5.4
Cite as: arXiv:2203.01087 [cs.CV]
  (or arXiv:2203.01087v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2203.01087

Submission history

From: Qing Cheng [view email]
[v1] Wed, 2 Mar 2022 13:18:38 UTC (5,608 KB)
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