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Bin Picking of Reflective Steel Parts Using a Dual-Resolution Convolutional Neural Network Trained in a Simulated Environment

Bin Picking of Reflective Steel Parts Using a Dual-Resolution Convolutional Neural Network Trained in a Simulated Environment

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Sammendrag
We consider the case of robotic bin picking of reflective steel parts, using a structured light 3D camera as a depth imaging device. In this paper, we present a new method for bin picking, based on a dual-resolution convolutional neural network trained entirely in a simulated environment. The dual-resolution network consists of a high resolution focus network to compute the grasp and a low resolution context network to avoid local collisions. The reflectivity of the steel parts result in depth images that have a lot of missing data. To take this into account, training of the neural net is done by domain randomization on a large set of synthetic depth images that simulate the missing data problems of the real depth images. We demonstrate both in simulation and in a real-world test that our method can perform bin picking of reflective steel parts.
Oppdragsgiver
  • Norges forskningsråd / 262900
Språk
Engelsk
Institusjon(er)
  • Norges teknisk-naturvitenskapelige universitet
  • SINTEF Ocean / Sjømatteknologi
  • SINTEF Digital / Smart Sensor Systems
  • SINTEF Digital / Mathematics and Cybernetics
År
2018
Forlag
IEEE
Bok
Proceedings of the 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO). Kuala Lumpur, Malaysia, 12-15 Dec. 2018
ISBN
978-1-7281-0377-8
Side(r)
530 - 537