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Semi-semantic Line-Cluster Assisted Monocular SLAM for Indoor Environments

  • Ting SunEmail author
  • Dezhen Song
  • Dit-Yan Yeung
  • Ming Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11754)

Abstract

This paper presents a novel method to reduce the scale drift for indoor monocular simultaneous localization and mapping (SLAM). We leverage the prior knowledge that in the indoor environment, the line segments form tight clusters, e.g. many door frames in a straight corridor are of the same shape, size and orientation, so the same edges of these door frames form a tight line segment cluster. We implement our method in the popular ORB-SLAM2, which also serves as our baseline. In the front end we detect the line segments in each frame and incrementally cluster them in the 3D space. In the back end, we optimize the map imposing the constraint that the line segments of the same cluster should be the same. Experimental results show that our proposed method successfully reduces the scale drift for indoor monocular SLAM.

Keywords

SLAM Monocular Indoor Line-segment Clustering 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ting Sun
    • 1
    Email author
  • Dezhen Song
    • 2
  • Dit-Yan Yeung
    • 3
  • Ming Liu
    • 1
  1. 1.Department of Electronic and Computer EngineeringHong Kong University of Science and TechnologyHong KongChina
  2. 2.Department of Computer Science and EngineeringTexas A&M UniversityCollege StationUSA
  3. 3.Department of Computer ScienceHong Kong University of Science and TechnologyHong KongChina

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