CCDoN: Local features for high-speed and reliable 6-DoF pose estimation of randomly stacked objects

Masanobu Nagase, Shuichi Akizuki, Manabu Hashimoto

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper introduces a high-speed 3-D object recognition method using a novel feature description. Features proposed in this study consist of three values. One is the Difference of Normals (DoN) feature value that has been proposed by Ioannou. The other two represent information about curvature. These features are named Combination of Curvatures and Difference of Normals (CCDoN) Features. These features are used for recognition of position and pose of multiple objects that are stacked randomly. Because they are low-dimensional, high-speed matching can be achieved. Moreover, high-speed and reliable matching is achieved by using only effective features selected on the basis of their estimated distinctiveness. Experimental results using real datasets have demonstrated that the processing time is about 81 times faster than that of the conventional Spin Image method. Furthermore, the proposed method achieves a 98.2% recognition rate, which is 46.6% higher than that of the Spin Image method.

Original languageEnglish
Pages (from-to)1138-1143
Number of pages6
JournalSeimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering
Volume80
Issue number12
DOIs
Publication statusPublished - 2014 Dec 1
Externally publishedYes

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Object recognition
Processing

Keywords

  • 3-D feature point matching
  • Bin- picking
  • Curvature
  • Difference of normals
  • Object recognition
  • Point cloud data

ASJC Scopus subject areas

  • Mechanical Engineering

Cite this

CCDoN : Local features for high-speed and reliable 6-DoF pose estimation of randomly stacked objects. / Nagase, Masanobu; Akizuki, Shuichi; Hashimoto, Manabu.

In: Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering, Vol. 80, No. 12, 01.12.2014, p. 1138-1143.

Research output: Contribution to journalArticle

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