Stable position and pose estimation of industrial parts using evaluation of observability of 3D vector pairs

Shuichi Akizuki, Manabu Hashimoto

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

This paper introduces a stable 3D object detection method that can be applied to complicated scenes consisting of randomly stacked industrial parts. The proposed method uses a 3D vector pair that consists of paired 3D vectors with a shared starting point. By considering the observability of vector pairs, the proposed method has achieved high recognition performance. The observability factor of the vector pair is calculated by simulating the visible state of the vector pair from various viewpoints. By integrating the observability factor and the distinctiveness factor proposed in our previous work, a few vector pairs that are effective for recognition are automatically extracted froman object model, and then used for the matching process. Experiments have confirmed that the proposed method improves the 88.5% recognition success rate of previous state-of-the-art methods to 93.1%. The processing time of the proposed method is fast enough for robotic bin-picking.

Original languageEnglish
Pages (from-to)174-181
Number of pages8
JournalJournal of Robotics and Mechatronics
Volume27
Issue number2
DOIs
Publication statusPublished - 2015
Externally publishedYes

Keywords

  • Bin-picking
  • Point cloud data
  • Position and pose estimation
  • Vector pair

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

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