Using Online Modelled Spatial Constraints for Pose Estimation in an Industrial Setting

We introduce a vision system that is able to on-line learn spatial constraints to improve pose estimation in terms of correct recognition as well as computational speed. By making use of a simulated industrial robot system performing various pick and place tasks, we show the effect of model building when making use of visual knowledge in terms of visually extracted pose hypotheses as well as action knowledge in terms of pose hypotheses verified by action execution. We show that the use of action knowledge significantly improves the pose estimation process.