3D Object Detection and 6D Pose Estimation Using RGB-D Images and Mask R-CNN

Abstract

Understanding 3D scenes have attracted significant interests in recent years. Specifically, it is used with visual sensors to provide the information for a robotic manipulator to interact with the target object. Thus, 6D pose estimation and object recognition from point clouds or RGB-D images are important tasks for visual servoing. In this paper, we propose a learning based approach to perform 6D pose estimation for robotic manipulation using Mask R-CNN and the structured light technique. The proposed technique optimizes the 6D pose between the target objects and 3D CAD models in multi-layers. Our method is evaluated on a publicly available dataset for 6D pose estimation and shows its efficiency in computation time. The experimental results demonstrate the feasibility of the random bin picking application.