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Cue integration for visual servoing

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2 Author(s)
D. Kragic ; Dept. of Comput. Sci., R. Inst. of Technol., Stockholm, Sweden ; H. I. Christensen

The robustness and reliability of vision algorithms is, nowadays, the key issue in robotic research and industrial applications. To control a robot in a closed-loop fashion, different tracking systems have been reported in the literature. A common approach to increased robustness of a tracking system is the use of different models (CAD model of the object, motion model) known a priori. Our hypothesis is that fusion of multiple features facilitates robust detection and tracking of objects in scenes of realistic complexity. A particular application is the estimation of a robot's end-effector position in a sequence of images. The research investigates the following two different approaches to cue integration: 1) voting and 2) fuzzy logic-based fusion. The two approaches have been tested in association with scenes of varying complexity. Experimental results clearly demonstrate that fusion of cues results in a tracking system with a robust performance. The robustness is in particular evident for scenes with multiple moving objects and partial occlusion of the tracked object

Published in:

IEEE Transactions on Robotics and Automation  (Volume:17 ,  Issue: 1 )