Visual odometry: Part II: Matching, robustness, optimization, and applications

Friedrich Fraundorfer*, Davide Scaramuzza

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Visual odometry (VO) is the process of estimating the gomotion of an agent using the input of a single or multiple cameras attached to it. The advantage of VO with respect to wheel odometry is that VO is not affected by wheel slip in uneven terrain or other adverse conditions. During the feature-detection step, the image is searched for salient keypoints that are likely to match well in other images. A local feature is an image pattern that differs from its immediate neighborhood in terms of intensity, color, and texture. In the feature description step, the region around each detected feature is converted into a compact descriptor that can be matched against other descriptors. After comparing all feature descriptors between two images, the best correspondence of a feature in the second image is chosen as that with the closest descriptor. Alternatively, if only the motion model is known but not the 3-D feature position, the corresponding match can be searched along the epipolar line in the second image.

Original languageEnglish
Article number6153423
Pages (from-to)78-90
Number of pages13
JournalIEEE Robotics & Automation Magazine
Volume19
Issue number2
DOIs
Publication statusPublished - 2012

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Visual odometry: Part II: Matching, robustness, optimization, and applications'. Together they form a unique fingerprint.

Cite this