  Most of industrial robotic assembly tasks today require fixed initial
conditions for successful assembly. These constraints induce high production
costs and low adaptability to new tasks. In this work we aim towards flexible
and adaptable robotic assembly by using 3D CAD models for all parts to be
assembled. We focus on a generic assembly task - the Siemens Innovation
Challenge - in which a robot needs to assemble a gear-like mechanism with high
precision into an operating system. To obtain the millimeter-accuracy required
for this task and industrial settings alike, we use a depth camera mounted near
the robot end-effector. We present a high-accuracy two-stage pose estimation
procedure based on deep convolutional neural networks, which includes
detection, pose estimation, refinement, and handling of near- and full
symmetries of parts. The networks are trained on simulated depth images with
means to ensure successful transfer to the real robot. We obtain an average
pose estimation error of 2.16 millimeters and 0.64 degree leading to 91%
success rate for robotic assembly of randomly distributed parts. To the best of
our knowledge, this is the first time that the Siemens Innovation Challenge is
fully addressed, with all the parts assembled with high success rates.
