Authors
Victor Pujolle*, Eiji Hayashi
Computer Science and System Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-0053, Japan
*Corresponding author. Email: [email protected]
Corresponding Author
Victor Pujolle
Received 7 November 2019, Accepted 25 June 2020, Available Online 28 December 2020.
DOI
https://doi.org/10.2991/jrnal.k.201215.008
Keywords
Pose estimation; deep-learning; keypoints localization; instance segmentation; virtual training; factory automation
Abstract
Pose estimation algorithms’ goal is to find the position and the orientation of an object in space, given only an image. This task may be complex, especially in an uncontrolled environment with several parameters that can vary, like the object texture, background or the lightning conditions. Most algorithms performing pose estimation use deep learning methods. However, it may be difficult to create dataset to train such kind of models. In this paper we developed a new algorithm robust to a high variability of conditions using instance segmentation of the image and trainable on a virtual dataset. This system performs semantic keypoints based pose estimation without considering background, lighting or texture changes on the object.
Copyright
© 2020 The Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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