Crowdsourced 3D Mapping: A Combined Multi-View Geometry and Self-Supervised Learning Approach

• URL: http://arxiv.org/abs/2007.12918v1
• Date: Sat, 25 Jul 2020 12:10:16 GMT
• Title: Crowdsourced 3D Mapping: A Combined Multi-View Geometry and Self-Supervised Learning Approach
• Authors: Hemang Chawla, Matti Jukola, Terence Brouns, Elahe Arani, and Bahram Zonooz
• Abstract summary: We propose a framework that estimates the 3D positions of semantically meaningful landmarks without assuming known camera intrinsics. We utilize multi-view geometry as well as deep learning based self-calibration, depth, and ego-motion estimation for traffic sign positioning. We achieve an average single-journey relative and absolute positioning accuracy of 39cm and 1.26m respectively.
• Score: 10.610403488989428
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability to efficiently utilize crowdsourced visual data carries immense potential for the domains of large scale dynamic mapping and autonomous driving. However, state-of-the-art methods for crowdsourced 3D mapping assume prior knowledge of camera intrinsics. In this work, we propose a framework that estimates the 3D positions of semantically meaningful landmarks such as traffic signs without assuming known camera intrinsics, using only monocular color camera and GPS. We utilize multi-view geometry as well as deep learning based self-calibration, depth, and ego-motion estimation for traffic sign positioning, and show that combining their strengths is important for increasing the map coverage. To facilitate research on this task, we construct and make available a KITTI based 3D traffic sign ground truth positioning dataset. Using our proposed framework, we achieve an average single-journey relative and absolute positioning accuracy of 39cm and 1.26m respectively, on this dataset.

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