\(\pi \) Match: Monocular vSLAM and Piecewise Planar Reconstruction Using Fast Plane Correspondences

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• 关键词：Monocular visual SLAM ; Piecewise planar reconstruction
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9912
• 期：1
• 页码：380-395
• 全文大小：7,215 KB
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• 作者单位：Carolina Raposo (17)
  João P. Barreto (17)
  
  17. Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal
  
• 丛书名：Computer Vision ¨C ECCV 2016
• ISBN：978-3-319-46484-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9912

文摘

This paper proposes \(\pi \)Match, a monocular SLAM pipeline that, in contrast to current state-of-the-art feature-based methods, provides a dense Piecewise Planar Reconstruction (PPR) of the scene. It builds on recent advances in planar segmentation from affine correspondences (ACs) for generating motion hypotheses that are fed to a PEaRL framework which merges close motions and decides about multiple motion situations. Among the selected motions, the camera motion is identified and refined, allowing the subsequent refinement of the initial plane estimates. The high accuracy of this two-view approach allows a good scale estimation and a small drift in scale is observed, when compared to prior monocular methods. The final discrete optimization step provides an improved PPR of the scene. Experiments on the KITTI dataset show the accuracy of \(\pi \)Match and that it robustly handles situations of multiple motions and pure rotation of the camera. A Matlab implementation of the pipeline runs in about 0.7 s per frame.