Flexible 3D localization of planar objects for industrial bin-picking with monocamera vision system

Abstract

In this paper, we present a robust and flexible vision system for 3D localization of planar parts for industrial robots. Our system is able to work with nearly any object with planar shape, randomly placed inside a standard industrial bin or on a conveyor belt. Differently from most systems based on 2D image analysis, which usually can manage parts disposed in single layers, our approach can estimate the 6 degrees of freedom (DoF) pose of planar objects from a single 2D image. The choice of a single camera solution makes our system cheaper and faster with respect to systems using expensive industrial 3D cameras, or laser triangulation systems, or laser range finders. Our system can work virtually with any planar piece, without changing the software parameters, because the input for the recognition and localization algorithm is the CAD data of the planar part. The localization software is based on a two step strategy: i) a candidates selection step based on a well-engineered voting scheme ii) a refinement and best match selection step based on a robust iterative optimize-and-score procedure. During this second step, we employ a novel strategy we called search-in-the-stack that avoids the optimization from being stuck on local minima (representing false positives) created when objects are almost regularly stacked. Our system is currently installed in seven real world industrial plants, with different setups, working with hundreds of different models and successfully guiding the manipulators to pick several hundreds of thousands of pieces per year. In the experiment section, we report statistics about our system at work in real production plants on more than 60000 cycles.