6D Pose Estimation for Bin-Picking based on Improved Mask R-CNN and DenseFusion

Abstract

Estimating the 6D pose of parts is a core step for robot bin-picking tasks. The problem is that various parts are usually randomly stacked with heavy occlusion in real applications. In this work, we propose a novel method to regress 6D poses by feeding RGB-D images through a two-stage neural network. In the first stage, a network for the scenes with dense parts is proposed to get instance segmentation and corresponding bounding-box. In the second stage, we propose a novel loss function and add a special module and improve the 6D pose estimation performance of the baseline network dramatically. To solve the expensive annotation cost, a simulation method is employed to generate a synthetic dataset. In experiments, our method outperformed the baseline networks (Mask R-CNN and DenseFusion) with a remarkable gap. Besides, the grasping experiments demonstrated that the method also works well in real industrial robot bin-picking applications.