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- W3192632787 abstract "This paper presents a semantic planar SLAM system that improves pose estimation and mapping using cues from an instance planar segmentation network. While the mainstream approaches are using RGB-D sensors, employing a monocular camera with such a system still faces challenges such as robust data association and precise geometric model fitting. In the majority of existing work, geometric model estimation problems such as homography estimation and piece-wise planar reconstruction (PPR) are usually solved by standard (greedy) RANSAC separately and sequentially. However, setting the inlier-outlier threshold is difficult in absence of information about the scene (i.e. the scale). In this work, we revisit these problems and argue that two mentioned geometric models (homographies/3D planes) can be solved by minimizing an energy function that exploits the spatial coherence, i.e. with graph-cut optimization, which also tackles the practical issue when the output of a trained CNN is inaccurate. Moreover, we propose an adaptive parameter setting strategy based on our experiments, and report a comprehensive evaluation on various open-source datasets." @default.
- W3192632787 created "2021-08-16" @default.
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- W3192632787 date "2021-08-09" @default.
- W3192632787 modified "2023-10-16" @default.
- W3192632787 title "Visual SLAM with Graph-Cut Optimized Multi-Plane Reconstruction" @default.
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- W3192632787 doi "https://doi.org/10.48550/arxiv.2108.04281" @default.
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