A novel information fusion method for vision perception and location of intelligent industrial robots

Shoufeng Jin, Qiangqiang Lin, Jian Yang, Yu Bie, Mingrui Tian, Zhixiong Li

Research output: Journal PublicationArticlepeer-review

1 Citation (Scopus)

Abstract

An improved SURF (Speeded-Up Robust Feature)algorithm is proposed to deal with the time-consuming and low precision of positioning of industrial robot. Hessian matrix determinant is used to extract feature points from the target image and a multi-scale spatial pyramid is constructed. The location and scale value of feature points are determined by neighbourhood non-maximum suppression method. The direction of feature points is defined as directional feature descriptors by the binary robust independent elementary feature (BRIEF). The progressive sample consensus (PROSAC) is used to carry out second precise matching and remove mismatching points based on the Hamming distance. Then, an affine transformation model is established to describe the relationship between the template and target images. Centroid coordinates of the target can be obtained based on the affine transformation. Comparative tests were carried out to demonstrate that the proposed method can effectively improve the recognition rate and positioning accuracy of the industrial robots. The average time consuming is less than 0.2 s, the matching accuracy is 96 %, and the positioning error of the robot is less than 1.5 mm. Therefore, the proposed method has practical application importance.

Original languageEnglish
Pages (from-to)4-10
Number of pages7
JournalElektronika ir Elektrotechnika
Volume25
Issue number5
DOIs
Publication statusPublished - 9 Oct 2019

Keywords

  • Affine transformation
  • BRIEF descriptor
  • Centroid coordinates
  • Industrial robot
  • SURF algorithm

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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