Sim-to-real Object Pose Estimation for Random Bin Picking

Abstract

In industry, random bin picking is a complex and difficult task where instance segmentation and object pose estimation based on point clouds are key processes. Recently, learning-based segmentation and pose estimation methods for 3D point clouds have been proposed. However, many of them require supervised learning with datasets with annotations of objects. Since it is difficult to annotate all stacked instances in bin picking dataset, learning without real-world datasets has become a major interest. In this paper, we introduce an instance-level object pose estimation method for bin picking, which is trained using only simulated data and seamlessly applied to real-world scenarios without additional adaptation. To enable this, we introduce a method for generating a comprehensive synthetic dataset using a physics simulator, which incorporates 3D CAD models of objects and automatically generates annotations for both segmentation and pose estimation. Our experiments, conducted on synthetic datasets, highlight the competitive performance of our method in terms of recall and accuracy. Furthermore, we demonstrate the successful integration of our approach with real robot random bin picking, resulting in significantly improved picking success rates.