Automatic 3D Object Recognition and Localization for Robotic Grasping

Bruno Santo, Liliana Antão, Gil Gonçalves

2021

Abstract

With the emergence of Industry 4.0 and its highly re-configurable manufacturing context, the typical fixed-position grasping systems are no longer usable. This reality underlined the necessity for fully automatic and adaptable robotic grasping systems. With that in mind, the primary purpose of this paper is to join Machine Learning models for detection and pose estimation into an automatic system to be used in a grasping environment. The developed system uses Mask-RCNN and Densefusion models for the recognition and pose estimation of objects, respectively. The grasping is executed, taking into consideration both the pose and the object’s ID, as well as allowing for user and application adaptability through an initial configuration. The system was tested both on a validation dataset and in a real-world environment. The main results show that the system has more difficulty with complex objects; however, it shows promising results for simpler objects, even with training on a reduced dataset. It is also able to generalize to objects slightly different than the ones seen in training. There is an 80% success rate in the best cases for simple grasping attempts.

Paper Citation

in Harvard Style

Santo B., Antão L. and Gonçalves G. (2021). Automatic 3D Object Recognition and Localization for Robotic Grasping. In Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-522-7, pages 416-425. DOI: 10.5220/0010552704160425

in Bibtex Style

@conference{icinco21,
author={Bruno Santo and Liliana Antão and Gil Gonçalves},
title={Automatic 3D Object Recognition and Localization for Robotic Grasping},
booktitle={Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2021},
pages={416-425},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010552704160425},
isbn={978-989-758-522-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Automatic 3D Object Recognition and Localization for Robotic Grasping
SN - 978-989-758-522-7
AU - Santo B.
AU - Antão L.
AU - Gonçalves G.
PY - 2021
SP - 416
EP - 425
DO - 10.5220/0010552704160425