Pose Estimation of Similar Shape Objects using Convolutional Neural Network trained by Synthetic data

The objective of this paper is accurate 6D pose estimation from 2.5D point clouds for object classes with a high shape variation, such as vegetables and fruit. General pose estimation methods usually focus on calculating rigid transformations between known models and the target scene, and do not explicitly consider shape variations. We employ deep convolutional neural networks (CNN), which show robust and state of the art performance for the 2D image domain. In contrast, normally the performance of pose estimation from point clouds is weak, because it is hard to prepare large enough annotated training data. To overcome this issue, we propose an autonomous generation process of synthetic 2.5D point clouds covering different shape variations of the objects. The synthetic data is used to train the deep CNN model in order to estimate the object poses. We propose a novel loss function to guide the estimator to have larger feature distances for different poses, and to directly estimate the correct object pose. We performed an evaluation using real objects, where the training was conducted with artificial CAD models downloaded from a public web resource. The results indicate that our approach is suitable for real world robotic applications.