Name
Abstract | ||
|---|---|---|
This paper presents a vision based human-robotic interaction (HRI) framework for the modeling and localization of industrial objects typically found in an assembly task. Automating robotic vision for complicated industrial objects is an important, yet still difficult task, especially in the stage of extracting object features. To tackle this specific problem, we have developed a new HRI system consisting of an off-line vision model acquisition, in which the object's salient features are acquired through a human-in-the-loop approach. Subsequently, two feature extraction algorithms; region-growing and edge-grouping, are applied to the model development through collaboration between the human and robot. Finally, using a Kalman filter estimation with a proper ellipse representation, our object localization system generates ellipse hypotheses by grouping edge fragments in the scene, driven by the acquired vision model of objects. The proposed system is validated by experiments using actual industrial objects for both HRI-based object modeling and automated object localization. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1016/j.compind.2005.05.020 | Computers in Industry |
Keywords | DocType | Volume |
industrial object,3D robot vision system,Salient feature extraction,complicated industrial object,object feature,robotic vision,acquired vision model,actual industrial object,off-line vision model acquisition,automated assembly system,kalman filter estimation,HRI-based object modeling,automated object localization,object localization system,Kalman filter estimation,Human-in-the-loop segmentation,salient feature extraction,3d robot vision system,elliptic edge grouping,human-in-the-loop segmentation,Elliptic edge grouping | Journal | 56 |
Issue | ISSN | Citations |
8 | Computers in Industry | 3 |
PageRank | References | Authors |
0.41 | 20 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuichi Motai | 1 | 230 | 24.68 |