Combined Shape, Appearance And Silhouette For Simultaneous Manipulator And Object Tracking
2012 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)(2012)
摘要
This paper develops an estimation framework for sensor-guided manipulation of a rigid object via a robot arm. Using an unscented Kalman Filter (UKF), the method combines dense range information (from stereo cameras and 3D ranging sensors) as well as visual appearance features and silhouettes of the object and manipulator to track both an object-fixed frame location as well as a manipulator tool or palm frame location. If available, tactile data is also incorporated. By using these different imaging sensors and different imaging properties, we can leverage the advantages of each sensor and each feature type to realize more accurate and robust object and reference frame tracking. The method is demonstrated using the DARPA ARM-S system, consisting of a Barrett TM WAM manipulator.
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关键词
kalman filters,object tracking,unscented kalman filter,visualization,drilling,image sensor,reference frame,mathematical model,robot arm,image sensors,stereo cameras
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