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The World Robot Summit 2018 Assembly Challenge included four different tasks.The kitting task, which required bin-picking, was the task in which the fewestpoints were obtained. However, bin-picking is a vital skill that cansignificantly increase the flexibility of robotic set-ups, and is, therefore,an important research field. In recent years advancements have been made insensor technology and pose estimation algorithms. These advancements allow forbetter performance when performing visual pose estimation.  This paper shows that by utilizing new vision sensors and pose estimationalgorithms pose estimation in bins can be performed successfully. We alsoimplement a workcell for bin picking along with a force based grasping approachto perform the complete bin picking. Our set-up is tested on the World RobotSummit 2018 Assembly Challenge and successfully obtains a higher score comparedwith all teams at the competition. This demonstrate that current technology canperform bin-picking at a much higher level compared with previous results.

Off-the-shelf bin picking workcell with visual pose estimation: A case study on the world robot summit 2018 kitting task