Model-based stereo-tracking of non-polyhedral objects for automatic disassembly experiments
Automatic disassembly tasks in the engine compartment of a used car constitute a challenge for control of a disassembly robot by machine vision. Experience in exploratory experiments under such conditions forced us to abandon data-driven aggregation of edge elements into straight-line data segments in favor of a direct association of individual edge elements with model segments obtained from scene domain models of tools and workpieces. In addition, we had to switch from a conventional single camera hand-eye configuration to a movable stereoconfiguration mounted on a separate 'observer' robot. A eneralisation of our model-based tracking includes the parameters, which characterize the relative pose of one camera with respect to the other one of the stereo-camera set-up, into the set of parameters to be re-estimated for each new stereo image pair. This results in a continuous re-calibration during a relative movement between stereo-camera set-up and tracked objects. Our approach had to be extended further in order to cope with non-polyhedral objects. The methodological improvements of machine vision in the course of this research are treated in detail. We discuss, moreover, the systematic trading-off of computational resources for increased robustness which is vital for visual control of automatic disassembly robots.
non-polyhedral objects disassembly machine vision model-based tracking visual servoing image sequence evaluation stereo-vision kalman-filtering