Concatenate feature extraction for robust 3D elliptic object localization
Yuichi Motai, Akio Kosaka · 2004
Developing an efficient object localization system for complicated industrial objects is an important, yet difficult robotic task. To tackle this problem, we have developed a system consisting first of a vision model acquisition editor, where the object 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 object scene. Finally, by Kalman filter estimation of a proper ellipse representation, our object localization system successfully generates ellipse hypotheses by grouping edge fragments in the scene. The proposed system is validated by experiments using actual industrial objects.