6D Pose Estimation of Occlusion-Free Objects for Robotic Bin-Picking Using PPF-MEAM With 2D Images (Occlusion-Free PPF-MEAM)

Abstract

Pose estimation that locates objects in a bin is necessary for a robotic bin picking system. Although many algorithms have shown high performance in pose estimation, most algorithms estimate the poses of objects regardless of their occlusion. This can reduce the success rate in picking up the object. To resolve this issue, we propose a novel pipeline that estimates a pose only for occlusion-free objects based on point pair feature-based pose estimation with multiple edge appearance model (PPF-MEAM). The proposed method detects occlusion-free objects in the 2D image captured by a camera with a convolutional neural network framework. Next, corresponding point clouds of occlusion-free objects need to be extracted by using their locations in the 2D image. we propose a robust extraction method that finds the 3D points corresponding to image pixels in the 2D image to reduce the effect of the calibration errors between the camera and 3D sensor. The point cloud of the occlusion-free objects is finally input into a pipeline of PPF-MEAM to estimate the pose of the object. The experiment results prove that the proposed method is about 50% faster 30% higher in terms of pose estimation success rate compared with the original PPF. Moreover, it increases the success rate of picking tasks compared with the original PPF-MEAM.