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Existing simultaneous localization and mapping (SLAM) algorithms are notrobust in challenging low-texture environments because there are only fewsalient features. The resulting sparse or semi-dense map also conveys littleinformation for motion planning. Though some work utilize plane or scene layoutfor dense map regularization, they require decent state estimation from othersources. In this paper, we propose real-time monocular plane SLAM todemonstrate that scene understanding could improve both state estimation anddense mapping especially in low-texture environments. The plane measurementscome from a pop-up 3D plane model applied to each single image. We also combineplanes with point based SLAM to improve robustness. On a public TUM dataset,our algorithm generates a dense semantic 3D model with pixel depth error of 6.2cm while existing SLAM algorithms fail. On a 60 m long dataset with loops, ourmethod creates a much better 3D model with state estimation error of 0.67%.

Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments