  Localizing an object accurately with respect to a robot is a key step for
autonomous robotic manipulation. In this work, we propose to tackle this task
knowing only 3D models of the robot and object in the particular case where the
scene is viewed from uncalibrated cameras -- a situation which would be typical
in an uncontrolled environment, e.g., on a construction site. We demonstrate
that this localization can be performed very accurately, with millimetric
errors, without using a single real image for training, a strong advantage
since acquiring representative training data is a long and expensive process.
Our approach relies on a classification Convolutional Neural Network (CNN)
trained using hundreds of thousands of synthetically rendered scenes with
randomized parameters. To evaluate our approach quantitatively and make it
comparable to alternative approaches, we build a new rich dataset of real robot
images with accurately localized blocks.
