Object 6 Degrees of Freedom Pose Estimation with Mask-R-CNN and Virtual Training
- https://doi.org/10.2991/jrnal.k.201215.008How to use a DOI?
- Pose estimation, deep-learning, keypoints localization, instance segmentation, virtual training, factory automation
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.
- © 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 -