Cue integration for visual servoing. - Kragic, D.
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
Keywords: CAD model; Closed-loop control; Cue integration; Fuzzy logic-based fusion; Image sequence; Industrial applications; Multiple feature fusion; Multiple moving objects; Object tracking; Partial occlusion; Reliability; Robot end-effector position estimation; Robust object detection; Robustness; Tracking systems; Visual servoing; Voting