Fast and Robust Pose Estimation Algorithm for Bin Picking Using Point Pair Feature

Mingyu Li, Koichi Hashimoto

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

7 Citations (Scopus)

Abstract

Bin picking refers to picking up the objects randomly piled in the container (bin) and robotic bin picking is always used to improve the industrial production efficiency. A pose estimation algorithm is necessary to tell the poses of the objects to the robot. This paper proposes a pose estimation algorithm for bin picking using 3D point cloud data. Point Pair Feature algorithm is performed in a fast way to propose possible poses and the poses are verified by a voxel-based verification method. Iterative Closest Point is used to refine the result poses. Our algorithm is proved to be more accurate and faster than Curve Set Feature algorithm and Point Pair Feature algorithm, robust to occlusion and able to detect multiple poses in one scene.

Original languageEnglish
Title of host publication2018 24th International Conference on Pattern Recognition, ICPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1604-1609
Number of pages6
ISBN (Electronic)9781538637883
DOIs
Publication statusPublished - 2018 Nov 26
Event24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
Duration: 2018 Aug 202018 Aug 24

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2018-August
ISSN (Print)1051-4651

Other

Other24th International Conference on Pattern Recognition, ICPR 2018
Country/TerritoryChina
CityBeijing
Period18/8/2018/8/24

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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