  We propose a fast and accurate method of 6D object pose estimation for
bin-picking of mechanical parts by a robot manipulator. We extend the
single-shot approach to stereo vision by application of attention architecture.
Our convolutional neural network model regresses to object locations and
rotations from either a left image or a right image without depth information.
Then, a stereo feature matching module, designated as Stereo Grid Attention,
generates stereo grid matching maps. The important point of our method is only
to calculate disparity of the objects found by the attention from stereo
images, instead of calculating a point cloud over the entire image. The
disparity value is then used to calculate the depth to the objects by the
principle of triangulation. Our method also achieves a rapid processing speed
of pose estimation by the single-shot architecture and it is possible to
process a 1024 x 1024 pixels image in 75 milliseconds on the Jetson AGX Xavier
implemented with half-float model. Weakly textured mechanical parts are used to
exemplify the method. First, we create original synthetic datasets for training
and evaluating of the proposed model. This dataset is created by capturing and
rendering numerous 3D models of several types of mechanical parts in virtual
space. Finally, we use a robotic manipulator with an electromagnetic gripper to
pick up the mechanical parts in a cluttered state to verify the validity of our
method in an actual scene. When a raw stereo image is used by the proposed
method from our stereo camera to detect black steel screws, stainless screws,
and DC motor parts, i.e., cases, rotor cores and commutator caps, the
bin-picking tasks are successful with 76.3%, 64.0%, 50.5%, 89.1% and 64.2%
probability, respectively.
