Robust selective stereo SLAM without loop closure and bundle adjustment

Fabio Bellavia, Fabio Bellavia, Marco Fanfani, Fabio Pazzaglia, Carlo Colombo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

29 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationImage Analysis and Processing – ICIAP 2013
Pages462-471
Number of pages10
Publication statusPublished - 2013

Publication series

NameLECTURE NOTES IN COMPUTER SCIENCE

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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