Deep-learning based robust edge detection for point pair feature-based pose estimation with multiple edge appearance models

Diyi Liu, Shogo Arai, Fuyuki Tokuda, Yajun Xu, Jun Kinugawa, Kazuhiro Kosuge

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

Abstract

To realize a robotic bin picking system, pose estimation for the objects randomly piled up in a bin is necessary. For various types of objects, many pose estimation algorithms have been proposed so far. Point Pair Featurebased Pose Estimation with Multiple Edge Appearance Models (PPF-MEAM) has been proposed for estimating the pose of industrial parts including some parts whose point clouds are defective in our previous work. Although this method shows high performance in pose estimation under a constant environment, its performance drops under the changing light conditions without tuning parameters. To overcome this problem, we propose Deep-Learning based Robust Edge Detection (DLED) for PPF-MEAM to make it robust to changes of the light. The effectiveness of DLED is proved by the edge detection experiment under different light conditions. Moreover, the pose estimation experiment proves that DLED could improve the pose estimation performance of PPF-MEAM under different light conditions.

Original languageEnglish
Title of host publicationIEEE International Conference on Robotics and Biomimetics, ROBIO 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2920-2925
Number of pages6
ISBN (Electronic)9781728163215
DOIs
Publication statusPublished - 2019 Dec
Event2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019 - Dali, China
Duration: 2019 Dec 62019 Dec 8

Publication series

NameIEEE International Conference on Robotics and Biomimetics, ROBIO 2019

Conference

Conference2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019
CountryChina
CityDali
Period19/12/619/12/8

Keywords

  • Deep Learning
  • Edge Detection
  • PPF-MEAM
  • Pose Estimation
  • Robotic Bin Picking

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Hardware and Architecture
  • Mechanical Engineering
  • Control and Optimization

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  • Cite this

    Liu, D., Arai, S., Tokuda, F., Xu, Y., Kinugawa, J., & Kosuge, K. (2019). Deep-learning based robust edge detection for point pair feature-based pose estimation with multiple edge appearance models. In IEEE International Conference on Robotics and Biomimetics, ROBIO 2019 (pp. 2920-2925). [8961752] (IEEE International Conference on Robotics and Biomimetics, ROBIO 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ROBIO49542.2019.8961752