  6D pose recognition has been a crucial factor in the success of robotic
grasping, and recent deep learning based approaches have achieved remarkable
results on benchmarks. However, their generalization capabilities in real-world
applications remain unclear. To overcome this gap, we introduce 6IMPOSE, a
novel framework for sim-to-real data generation and 6D pose estimation. 6IMPOSE
consists of four modules: First, a data generation pipeline that employs the 3D
software suite Blender to create synthetic RGBD image datasets with 6D pose
annotations. Second, an annotated RGBD dataset of five household objects
generated using the proposed pipeline. Third, a real-time two-stage 6D pose
estimation approach that integrates the object detector YOLO-V4 and a
streamlined, real-time version of the 6D pose estimation algorithm PVN3D
optimized for time-sensitive robotics applications. Fourth, a codebase designed
to facilitate the integration of the vision system into a robotic grasping
experiment. Our approach demonstrates the efficient generation of large amounts
of photo-realistic RGBD images and the successful transfer of the trained
inference model to robotic grasping experiments, achieving an overall success
rate of 87% in grasping five different household objects from cluttered
backgrounds under varying lighting conditions. This is made possible by the
fine-tuning of data generation and domain randomization techniques, and the
optimization of the inference pipeline, overcoming the generalization and
performance shortcomings of the original PVN3D algorithm. Finally, we make the
code, synthetic dataset, and all the pretrained models available on Github.
