6D object localization in car-assembly industrial environment

In this work a visual object detection and localization workflow is presented for the 6D pose estimation of objects with challenging characteristics in terms of weak texture, surface properties and symmetries. The objects of interest aim to support robot grasping in the context of human-robot collaboration during a car door assembly process in industrial manufacturing environments, inherently characterised by cluttered background and unfavorable illumination conditions. For the purpose of this specific application two different datasets were collected and annotated for training a learning based method that extracts the object pose from a single frame. The first dataset was acquired in controlled laboratory conditions and the other in the actual industrial environment. The trained models were used as part of a module for object pose estimation deployed to a mobile robotic platform that exploits the Robot Operating System (ROS) as middleware. The derived models were evaluated in a number of test sequences from the actual industrial environment, demonstrating the potential of the presented method for industrial applications.