Blank A, Baier L, Zwingel M, Franke J (2022)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2022
Publisher: publish-Ing.
City/Town: Offenburg
Book Volume: 2022
Pages Range: 829-838
Conference Proceedings Title: Proceedings of the Conference on Production Systems and Logistics
Event location: Vancouver, Canada
DOI: 10.15488/12184
Beyond conventional automated tasks, autonomous robot capabilities aside to human cognitive skills are
gaining importance. This comprises goods commissioning and material supply in intralogistics as well as
material feeding and assembly operations in production. Deep learning-based computer vision is considered
as enabler for autonomy. Currently, the effort to generate specific datasets is challenging. Adaptation of new
components often also results in downtimes. The objective of this paper is to propose an augmented virtuality
(AV) based RGBD data annotation and refinement method. The approach reduces required effort in initial
dataset generation to enable prior system commissioning and enables dataset quality improvement up to
operational readiness during ramp-up. In addition, remote fault intervention through a teleoperation interface
is provided to increase operational system availability. Several components within a real-world experimental
bin-picking setup serve for evaluation. The results are quantified by comparison to established annotation
methods and through known evaluation metrics for pose estimation in bin-picking scenarios. The results
enable to derive accurate and more time-efficient data annotation for different algorithms. The AV approach
shows a noticeable reduction in required effort and timespan for annotation as well as dataset refinement.
APA:
Blank, A., Baier, L., Zwingel, M., & Franke, J. (2022). Augmented Virtuality Data Annotation And Human-In-The-Loop Refinement For RGBD Data In Industrial Bin-Picking Scenarios. In Proceedings of the Conference on Production Systems and Logistics (pp. 829-838). Vancouver, Canada, CA: Offenburg: publish-Ing..
MLA:
Blank, Andreas, et al. "Augmented Virtuality Data Annotation And Human-In-The-Loop Refinement For RGBD Data In Industrial Bin-Picking Scenarios." Proceedings of the Conference on Production Systems and Logistics (CPSL 2022), Vancouver, Canada Offenburg: publish-Ing., 2022. 829-838.
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