Date of Award
6-2024
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2024
Degree Name
Master of Science (MS)
Department
Electrical and Computer Engineering
First Advisor
Maria Kyrarini
Abstract
Humanoid robots are anticipated to play integral roles in domestic settings, aiding individuals with everyday tasks. Given the prevalence of translucent storage containers in households, characterized by their practicality and transparency, it becomes imperative to equip humanoid robots with the capability to localize and manipulate these objects accurately. Consequently, 6D pose estimation is a crucial area of research for advancing robotic manipulation. However, most existing 6D pose estimation methods and datasets predominantly focus on opaque objects, leaving a gap in research concerning translucent objects. We introduce a novel translucent object dataset with 6D pose and bounding box annotations tailored for robotic manipulation. The dataset includes six classes of translucent container objects in diverse environments and lighting conditions. Additionally, we introduce an automated ground truth annotation method that can be easily replicated and requires only RGB input. Finally, we fine-tune PoET, a transformer-based RGB-only 6D pose estimation framework, on the novel translucent object dataset. Finetuned PoET achieves 94% accuracy on translucent objects from the new dataset.
Recommended Citation
Nedorosleva, Sofia, "Translucent Object and Automated 6D Pose Annotation Method for Robotic Manipulation" (2024). Electrical and Computer Engineering Master's Theses. 7.
https://scholarcommons.scu.edu/elec_mstr/7