Robust Selective Stereo SLAM without Loop Closure and Bundle Adjustment

  • Fabio Bellavia
  • Marco Fanfani
  • Fabio Pazzaglia
  • Carlo Colombo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)


This paper presents a novel stereo SLAM framework, where a robust loop chain matching scheme for tracking keypoints is combined with an effective frame selection strategy. The proposed approach, referred to as selective SLAM (SSLAM), relies on the observation that the error in the pose estimation propagates from the uncertainty of the three-dimensional points. This is higher for distant points, corresponding to matches with low temporal flow disparity in the images. Comparative results based on the reference KITTI evaluation framework show that SSLAM is effective and can be implemented efficiently, as it does not require any loop closure or bundle adjustment.


SLAM Structure from Motion RANSAC feature matching frame selection 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fabio Bellavia
    • 1
  • Marco Fanfani
    • 1
  • Fabio Pazzaglia
    • 1
  • Carlo Colombo
    • 1
  1. 1.Computational Vision GroupUniversity of FlorenceFlorenceItaly

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