Journal of Robotics, Networking and Artificial Life

Volume 7, Issue 4, March 2021, Pages 249 - 252

Object 6 Degrees of Freedom Pose Estimation with Mask-R-CNN and Virtual Training

Authors
Victor Pujolle*, Eiji Hayashi
Computer Science and System Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-0053, Japan
*Corresponding author. Email: victor.pujolle5@gmail.com
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.201215.008How to use a DOI?
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 Atlantis Press B.V.
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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Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
7 - 4
Pages
249 - 252
Publication Date
2020/12
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
https://doi.org/10.2991/jrnal.k.201215.008How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press B.V.
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/).

Cite this article

TY  - JOUR
AU  - Victor Pujolle
AU  - Eiji Hayashi
PY  - 2020
DA  - 2020/12
TI  - Object 6 Degrees of Freedom Pose Estimation with Mask-R-CNN and Virtual Training
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 249
EP  - 252
VL  - 7
IS  - 4
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.k.201215.008
DO  - https://doi.org/10.2991/jrnal.k.201215.008
ID  - Pujolle2020
ER  -