Anticipation and attention for robust object recognition with RGBD-data in an industrial application scenario

An extension based on attention and anticipation of a robot vision pipeline for object recognition in RGBD images from low-cost sensors like MS Kinect or ASUS Xtion is presented. This work originated in research on an industrial application scenario, namely shipping-container unloading, but it is applicable to advanced manipulation tasks in unstructured environments in general where the perception must be very robust while being as fast as possible. For these scenarios, we build on our previous work that proved to be competitive in cluttered scenes in table-top scenarios and which forms the backbone of our RGBD object recognition. It is further enhanced by two main contributions. First, a simple but very effective form of anticipation as top-down expectations of the evolution of the scene due to the actions of the robot is used to speed up the processing. Second, attention is used as a mechanism for further speed-up by focusing processing only on certain regions of interest of the scene based also on an anticipation mechanism. The method is analyzed in experiments using real-world data from an industrial demonstration set-up.