  Recently developed deep neural networks achieved state-of-the-art results in
the subject of 6D object pose estimation for robot manipulation. However, those
supervised deep learning methods require expensive annotated training data.
Current methods for reducing those costs frequently use synthetic data from
simulations, but rely on expert knowledge and suffer from the "domain gap" when
shifting to the real world. Here, we present a proof of concept for a novel
approach of autonomously generating annotated training data for 6D object pose
estimation. This approach is designed for learning new objects in operational
environments while requiring little interaction and no expertise on the part of
the user. We evaluate our autonomous data generation approach in two grasping
experiments, where we archive a similar grasping success rate as related work
on a non autonomously generated data set.
