{
  "id": "2108.04281",
  "version": "v1",
  "published": "2021-08-09T18:16:08.000Z",
  "updated": "2021-08-09T18:16:08.000Z",
  "title": "Visual SLAM with Graph-Cut Optimized Multi-Plane Reconstruction",
  "authors": [
    "Fangwen Shu",
    "Yaxu Xie",
    "Jason Rambach",
    "Alain Pagani",
    "Didier Stricker"
  ],
  "comment": "accepted to ISMAR-Adjunct 2021",
  "categories": [
    "cs.CV",
    "cs.RO"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-08-09T18:16:08.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "graph-cut optimized multi-plane reconstruction",
      "visual slam",
      "semantic planar slam system",
      "instance planar segmentation network",
      "geometric model estimation problems"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}