@inproceedings{d769c583e0b14dc59159e1f1eda929e9,
title = "Robot-Based Auto-labeling System for 6D Pose Estimation",
abstract = "6D object pose estimation is an ongoing research area in the field of computer vision. Many existing methods rely on supervised deep learning models which require multiple accurate 6D pose annotations to predict object poses. However, labeling the 6D pose is complex and time-consuming in traditional methods. In this study, we propose a robotic-arm-based 6D object pose auto-labeling approach which has limited human interaction involved. Translations and rotations of the object in the camera coordinate system can be calculated using a sequence of known robot poses and the transformation between the camera and the robot. We also implemented our custom dataset generated by the auto-labeling system in the existing 6D object pose estimation approach. Evaluation results show that the model can recognize our own test dataset and attempted 90% accuracy using ADD metric with 0.05 threshold.",
keywords = "6D object pose estimation, Annotations, Auto-labeling, Pose annotation, Robotic-arm-based",
author = "Lin, {Hsien I.} and Chang, {Jun Shiang}",
note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 8th International Congress on Information and Communication Technology, ICICT 2023 ; Conference date: 20-02-2023 Through 23-02-2023",
year = "2024",
doi = "10.1007/978-981-99-3043-2_10",
language = "English",
isbn = "9789819930425",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "123--134",
editor = "Xin-She Yang and Sherratt, {R. Simon} and Nilanjan Dey and Amit Joshi",
booktitle = "Proceedings of 8th International Congress on Information and Communication Technology - ICICT 2023",
address = "Germany",
}