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

  • Carolina RaposoEmail author
  • João P. Barreto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9912)

Abstract

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.

Keywords

Monocular visual SLAM Piecewise planar reconstruction 

Notes

Acknowledgments

Carolina Raposo acknowledges the Portuguese Science Foundation (FCT) for funding her PhD under grant SFRH/BD/88446/2012. The authors also thank FCT and COMPETE2020 program for generous funding through project VisArthro with reference PTDC/EEI-AUT/3024/2014.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Institute of Systems and RoboticsUniversity of CoimbraCoimbraPortugal

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