Authors:
Benjamin Deutsch
1
;
Frank Deinzer
1
;
Matthias Zobel
1
and
Joachim Denzler
2
Affiliations:
1
University of Erlangen-Nuremberg, Germany
;
2
University of Passau, Germany
Keyword(s):
Service Robotics, Object Tracking, Zoom Planning, Object Recognition, Grip Planning.
Related
Ontology
Subjects/Areas/Topics:
Image Processing
;
Informatics in Control, Automation and Robotics
;
Mobile Robots and Autonomous Systems
;
Robotics and Automation
;
Vision, Recognition and Reconstruction
Abstract:
We present a vision-based robotic system which uses a combination of several active sensing strategies to grip a free-standing small target object with an initially unknown position and orientation. The object position is determined and maintained with a probabilistic visual tracking system. The cameras on the robot contain a motorized zoom lens, allowing the focal lengths of the cameras to be adjusted during the approach. Our system uses an entropy-based approach to find the optimal zoom levels for reducing the uncertainty in the position estimation in real-time. The object can only be gripped efficiently from a few distinct directions, requiring the robot to first determine the pose of the object in a classification step, and then decide on the correct angle of approach in a grip planning step. The optimal angle is trained and selected using reinforcement learning, requiring no user-supplied knowledge about the object. The system is evaluated by comparing the experimental results t
o ground-truth information.
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