RGB-D object pose estimation in unstructured environments

Changhyun Choi, Henrik I. Christensen

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

We present an object pose estimation approach exploiting both geometric depth and photometric color information available from an RGB-D sensor. In contrast to various efforts relying on object segmentation with a known background structure, our approach does not depend on the segmentation and thus exhibits superior performance in unstructured environments. Inspired by a voting-based approach employing an oriented point pair feature, we present a voting-based approach which further incorporates color information from the RGB-D sensor and which exploits parallel power of the modern parallel computing architecture. The proposed approach is extensively evaluated with three state-of-the-art approaches on both synthetic and real datasets, and our approach outperforms the other approaches in terms of both computation time and accuracy.

Original languageEnglish (US)
Pages (from-to)595-613
Number of pages19
JournalRobotics and Autonomous Systems
Volume75
DOIs
StatePublished - Jan 1 2016
Externally publishedYes

Bibliographical note

Funding Information:
This work has in part been sponsored by the Boeing Corporation (12966BC–Wing Assembly). The support is gratefully acknowledged.

Publisher Copyright:
© 2015 Elsevier B.V.

Keywords

  • Bin-picking
  • GPU
  • Hough transform
  • Parallelization
  • Pose estimation
  • RGB-D
  • Range sensing
  • Voting scheme

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