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2024 (English)In: The proceedings of 2024 IEEE/SICE International Symposium on System Integration (SII), Institute of Electrical and Electronics Engineers (IEEE), 2024, p. 1425-1432Conference paper, Published paper (Refereed)
Abstract [en]
Robot manipulation in retail environments is a challenging task due to the need for large amounts of annotated data for accurate 6D-pose estimation of items. Onsite data collection, additional manual annotation, and model fine-tuning are often required when deploying robots in new environments, as varying lighting conditions, clutter, and occlusions can significantly diminish performance. Therefore, we propose a system to annotate the 6D pose of items using mixed reality (MR) to enhance the robustness of robot manipulation in retail environments. Our main contribution is a system that can display 6D-pose estimation results of a trained model from multiple perspectives in MR, and enable onsite (re-)annotation of incorrectly inferred item poses using hand gestures. The proposed system is compared to a PC-based annotation system using a mouse and the robot camera’s point cloud in an extensive quantitative experiment. Our experimental results indicate that MR can increase the accuracy of pose annotation, especially by reducing position errors.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Mammals, Annotation systems, Data collection, Fine tuning, Large amounts, Lighting conditions, Manual annotation, Mixed reality, Pose-estimation, Robot manipulation, Varying lighting, Mixed reality
National Category
Robotics and automation
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kau:diva-99148 (URN)10.1109/SII58957.2024.10417443 (DOI)2-s2.0-85186266074 (Scopus ID)979-8-3503-1208-9 (ISBN)979-8-3503-1207-2 (ISBN)
Conference
IEEE/SICE International Symposium on System Integration, Ha Long, Vietnam, January 8-11, 2024.
2024-04-032024-04-032025-02-09Bibliographically approved