Visual SLAM with Graph-Cut Optimized Multi-Plane Reconstruction

• URL: http://arxiv.org/abs/2108.04281v1
• Date: Mon, 9 Aug 2021 18:16:08 GMT
• Title: Visual SLAM with Graph-Cut Optimized Multi-Plane Reconstruction
• Authors: Fangwen Shu, Yaxu Xie, Jason Rambach, Alain Pagani, Didier Stricker
• Abstract summary: 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.
• Score: 11.215334675788952
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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