SyDPose: Object Detection and Pose Estimation in Cluttered Real-World Depth Images Trained using Only Synthetic Data

Abstract

Object pose estimation is an important problem in robotics because it supports scene understanding and enables subsequent grasping and manipulation. Many methods, including modern deep learning approaches, exploit known object models, however, in industry these are difficult and expensive to obtain. 3D CAD models, on the other hand, are often readily available. Consequently, training a deep architecture for pose estimation exclusively from CAD models leads to a considerable decrease of the data creation effort. While this has been shown to work well for feature-and template-based approaches, real-world data is still required for pose estimation in clutter using deep learning. We use synthetically created depth data with domain-relevant background randomized noise heuristics to train an end-to-end, multi-task network, for pose estimation. We simultaneously detect, classify and estimate the poses of texture-less objects in cluttered real-world depth images of an arbitrary amount of objects. We present the results of our experiments with the LineMOD and the Occlusion dataset.