Understand
This paper describes Team Delft's robot, which won the Amazon Picking Challenge 2016, including both the Picking and the Stowing competitions.
- The goal of the challenge is to automate pick and place operations in unstructured environments, specifically the shelves in an Amazon warehouse.
- Team Delft's robot is based on an industrial robot arm, 3D cameras and a customized gripper.
- The robot's software uses ROS to integrate off-the-shelf components and modules developed specifically for the competition, implementing Deep Learning and other AI techniques for object recognition and pose estimation, grasp planning and motion planning.
Built on
Ros: an open-source robot operating system
Quigley, M., Conley, K., Gerkey, B.P., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: · 2009
Earlier work this paper cites.
Super 4pcs fast global pointcloud registration via smart indexing
Mellado, N., Aiger, D., Mitra, N.J.: · 2014
Earlier work this paper cites.
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Faster R-CNN: Towards real-time object detection with region proposal networks
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”SMACH”, [Online] Available: http://wiki.ros.org/smach (2016)
Bohren, J.: · 2016
Cited alongside, same era.
http://www.delftrobotics.com/
Delft Robotics, B.V.:
Cited in the paper.
http://www.factory-in-a-day.eu
Factory-in-a-day:
Cited in the paper.
http://rosindustrial.org/
ROS-Industrial:
Cited in the paper.
”MoveIt!”, [Online] Available: http://moveit.ros.org
Sucan, I.A., Chitta, S.:
Cited in the paper.
http://robotics.tudelft.nl
TUD Robotics Institute:
Cited in the paper.
Then
Lessons from the amazon picking challenge
Correll, N., Bekris, K.E., Berenson, D., Brock, O., Causo, A., Hauser, K., Okada, K., Rodriguez, A., Romano, J.M., Wurman, P.R.: · 2016
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