Off-the-shelf bin picking workcell with visual pose estimation: A case
  study on the world robot summit 2018 kitting task
Frederik Hagelskjær Kasper Høj Lorenzen Dirk Kraft
The World Robot Summit 2018 Assembly Challenge included four different tasks.
The kitting task, which required bin-picking, was the task in which the fewest
points were obtained. However, bin-picking is a vital skill that can
significantly increase the flexibility of robotic set-ups, and is, therefore,
an important research field. In recent years advancements have been made in
sensor technology and pose estimation algorithms. These advancements allow for
better performance when performing visual pose estimation.
  This paper shows that by utilizing new vision sensors and pose estimation
algorithms pose estimation in bins can be performed successfully. We also
implement a workcell for bin picking along with a force based grasping approach
to perform the complete bin picking. Our set-up is tested on the World Robot
Summit 2018 Assembly Challenge and successfully obtains a higher score compared
with all teams at the competition. This demonstrate that current technology can
perform bin-picking at a much higher level compared with previous results.