MFPN-6D : Real-time One-stage Pose Estimation of Objects on RGB Images

Abstract

6D pose estimation of objects is an important part of robot grasping. The latest research trend on 6D pose estimation is to train a deep neural network to directly predict the 2D projection position of the 3D key points from the image, establish the corresponding relationship, and finally use Pespective-n-Point (PnP) algorithm performs pose estimation. The current challenge of pose estimation is that when the object texture-less, occluded and scene clutter, the detection accuracy will be reduced, and most of the existing algorithm models are large and cannot take the real-time requirements. In this paper, we introduce a Multi-directional Feature Pyramid Network, MFPN, which can efficiently integrate and utilize features. We combined the Cross Stage Partial Network (CSPNet) with MFPN to design a new network for 6D pose estimation, MFPN-6D. At the same time, we propose a new confidence calculation method for object pose estimation, which can fully consider spatial information and plane information. At last, we tested our method on the LINEMOD and Occluded-LINEMOD datasets. The experimental results demonstrate that our algorithm is robust to textureless materials and occlusion, while running more efficiently compared to other methods.