Bin Picking System using Object Recognition based on Automated Synthetic Dataset Generation

Hyun Jun Jo, Cheol Hui Min, Jae-Bok Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, deep learning has been increasingly used for robot-based object grasping. Since it is important to accurately recognize the position of objects to be grasped, it is often attempted to grasp the objects through the deep learning scheme which is known to show high object recognition performance. However, large datasets are required for object recognition using deep learning and in order to create a dataset, all data must be collected and needs to be manually annotated. It is a simple task, but takes a lot of time and labor. Therefore, this study reduced the amount of required data and minimized the resources and human effort required for dataset generation through image synthesis method and automatic annotation. Faster R-CNN, the object recognition algorithm, was trained on the generated dataset and recognized the position of the object on the image. The position of an object in an image can be converted to its position in the robot coordinate system using the camera intrinsic parameters which can be obtained by camera calibration. Therefore., the robot can move to the converted position of object on the robot coordinate system to grasp the object. Experiments show that bin picking can be conducted successfully in this way.

Original languageEnglish
Title of host publication2018 15th International Conference on Ubiquitous Robots, UR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages886-890
Number of pages5
ISBN (Print)9781538663349
DOIs
Publication statusPublished - 2018 Aug 20
Event15th International Conference on Ubiquitous Robots, UR 2018 - Honolulu, United States
Duration: 2018 Jun 272018 Jun 30

Other

Other15th International Conference on Ubiquitous Robots, UR 2018
CountryUnited States
CityHonolulu
Period18/6/2718/6/30

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Optimization
  • Mechanical Engineering

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