Skip to main navigation Skip to search Skip to main content

Research on 3D reconstruction for robot based on SIFT feature

  • Harbin Institute of Technology
  • Ningbo University of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

On the basis of only visual and odometer, a robust perception model is established to extract environmental features through effective fixed scale feature-transformation method, and updated feature by unscented Kalman filtering. The scale invariant feature transform (SIFT) is studied for 3D reconstruction, and a fast feature matching algorithm based on SIFT is proposed. A map representation method using SIFT features is also propounded, which is more convenient for environment recognition, robot localization and makes the data association map building much easier as well than the maps using simple features such as Harris corners and edges. The results of experiment show that this method can improve the success rate and precision of robot localization.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications, WARTIA 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages976-979
Number of pages4
ISBN (Electronic)9781479969890
DOIs
StatePublished - 4 Dec 2014
Event2014 IEEE Workshop on Advanced Research and Technology in Industry Applications, WARTIA 2014 - Ottawa, Canada
Duration: 29 Sep 201430 Sep 2014

Publication series

NameProceedings - 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications, WARTIA 2014

Conference

Conference2014 IEEE Workshop on Advanced Research and Technology in Industry Applications, WARTIA 2014
Country/TerritoryCanada
CityOttawa
Period29/09/1430/09/14

Keywords

  • 3D reconstruction
  • Map representation
  • Scale invariant feature transform

Fingerprint

Dive into the research topics of 'Research on 3D reconstruction for robot based on SIFT feature'. Together they form a unique fingerprint.

Cite this