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Fast Object Pose Estimation Using Adaptive Threshold for Bin-Picking

Wu Yan, Zhihao Xu, Xuefeng Zhou, Qianxing Su, Shuai Li Orcid Logo, Hongmin Wu

IEEE Access, Volume: 8, Pages: 63055 - 63064

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1109/access.2020.2983173

Abstract

Robotic bin-picking is a common process in modern manufacturing, logistics, and warehousing that aims to pick-up known or unknown objects with random poses out of a bin by using a robot-camera system. Rapid and accurate object pose estimation pipelines have become an escalating issue for robot picki...

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Published in: IEEE Access
ISSN: 2169-3536
Published: Institute of Electrical and Electronics Engineers (IEEE) 2020
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa54162
Abstract: Robotic bin-picking is a common process in modern manufacturing, logistics, and warehousing that aims to pick-up known or unknown objects with random poses out of a bin by using a robot-camera system. Rapid and accurate object pose estimation pipelines have become an escalating issue for robot picking in recent years. In this paper, a fast 6-DoF (degrees of freedom) pose estimation pipeline for random bin-picking is proposed in which the pipeline is capable of recognizing different types of objects in various cluttered scenarios and uses an adaptive threshold segment strategy to accelerate estimation and matching for the robot picking task. Particularly, our proposed method can be effectively trained with fewer samples by introducing the geometric properties of objects such as contour, normal distribution, and curvature. An experimental setup is designed with a Kinova 6-Dof robot and an Ensenso industrial 3D camera for evaluating our proposed methods with respect to four different objects. The results indicate that our proposed method achieves a 91.25% average success rate and a 0.265s average estimation time, which sufficiently demonstrates that our approach provides competitive results for fast objects pose estimation and can be applied to robotic random bin-picking tasks.
College: Faculty of Science and Engineering
Start Page: 63055
End Page: 63064

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