Accurate keyframe selection and keypoint tracking for robust visual odometry

Fabio Bellavia, Fabio Bellavia, Marco Fanfani, Carlo Colombo

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

This paper presents a novel stereo visual odometry (VO) framework based on structure from motion, where a robust keypoint tracking and matching is combined with an effective keyframe selection strategy. In order to track and find correct feature correspondences a robust loop chain matching scheme on two consecutive stereo pairs is introduced. Keyframe selection is based on the proportion of features with high temporal disparity. This criterion relies on the observation that the error in the pose estimation propagates from the uncertainty of 3D points—higher for distant points, that have low 2D motion. Comparative results based on three VO datasets show that the proposed solution is remarkably effective and robust even for very long path lengths.
Original languageEnglish
Pages (from-to)833-844
Number of pages12
JournalMachine Vision and Applications
Volume27
Publication statusPublished - 2016

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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