GPO: Global Plane Optimization for Fast and Accurate Monocular SLAM Initialization

• URL: http://arxiv.org/abs/2004.12051v2
• Date: Sun, 24 May 2020 01:59:57 GMT
• Title: GPO: Global Plane Optimization for Fast and Accurate Monocular SLAM Initialization
• Authors: Sicong Du, Hengkai Guo, Yao Chen, Yilun Lin, Xiangbing Meng, Linfu Wen, Fei-Yue Wang
• Abstract summary: The algorithm starts by homography estimation in a sliding window. The proposed method fully exploits the plane information from multiple frames and avoids the ambiguities in homography decomposition. Experimental results show that our method outperforms the fine-tuned baselines in both accuracy and real-time.
• Score: 22.847353792031488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Initialization is essential to monocular Simultaneous Localization and Mapping (SLAM) problems. This paper focuses on a novel initialization method for monocular SLAM based on planar features. The algorithm starts by homography estimation in a sliding window. It then proceeds to a global plane optimization (GPO) to obtain camera poses and the plane normal. 3D points can be recovered using planar constraints without triangulation. The proposed method fully exploits the plane information from multiple frames and avoids the ambiguities in homography decomposition. We validate our algorithm on the collected chessboard dataset against baseline implementations and present extensive analysis. Experimental results show that our method outperforms the fine-tuned baselines in both accuracy and real-time.

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