Absolute pose estimation using multiple forms of correspondences from RGB-D frames

Shuda Li, Andrew D Calway

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

6 Citations (Scopus)
538 Downloads (Pure)

Abstract

We describe a new approach to absolute pose estimation from noisy and outlier contaminated matching point sets for RGB-D sensors. We show that by integrating multiple forms of correspondence based on 2-D and 3-D points and surface normals gives more precise, accurate and robust pose estimates. This is because it gives more constraints than using one form alone and increases the available measurements, especially when dealing with sparse matching sets. We demonstrate the approach by incorporating it within a RANSAC algorithm and introduce a novel direct least-square approach to calculate pose estimates. Results from experiments on synthetic and real data demonstrate improved performance over existing methods.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Robotics and Automation (ICRA 2016)
Subtitle of host publicationProceedings of a meeting held 16-21 May 2016, Stockholm, Sweden
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages4756-4761
Number of pages6
ISBN (Electronic)9781467380263
ISBN (Print)9781467380270
DOIs
Publication statusPublished - Aug 2016
Event2016 IEEE International Conference on Robotics and Automation (ICRA) - Stockholm, Sweden
Duration: 16 May 201621 May 2016

Conference

Conference2016 IEEE International Conference on Robotics and Automation (ICRA)
Country/TerritorySweden
CityStockholm
Period16/05/1621/05/16

Keywords

  • computer vision
  • robotics
  • pose estimation
  • SLAM

Fingerprint

Dive into the research topics of 'Absolute pose estimation using multiple forms of correspondences from RGB-D frames'. Together they form a unique fingerprint.

Cite this