A Manufacturing-Oriented Intelligent Vision System Based on Deep Neural Network for Object Recognition and 6D Pose Estimation - Liang, Guoyuan; Chen, Fan; Liang, Yu; Feng, Yachun; Wang, Can; Wu, Xinyu (Frontiers in Neurorobotics, 2021)
Nowadays, intelligent robots are widely applied in manufacturing industry at various working places or assembly lines. In most manufacturing tasks, determining the category and pose of parts are important yet challenging due to the complex environments. \textcolor{red}{This paper presents a new two-stage intelligent vision system based on deep neural network with RGB-D image inputs for object recognition and 6D pose estimation.} A dense-connected network fusing multi-scale features is firstly built to segment the objects from the background. Then the 2D pixels and 3D points in cropped object regions are fed into a pose estimation network to make object pose predictions based on fusion of color and geometry features. By introducing the channel and position attention modules, the pose estimation network presents an effective way for feature extraction by stressing important features whilst suppressing unnecessary ones. Comparative experiments with several state-of-the-art networks conducted on two well-known benchmark datasets, YCB-Video and LineMOD, verified the effectiveness and superior performance of the proposed method. Moreover, we build a vision-guided robotic grasping system based on the proposed method using a Kinova Jaco2 manipulator with an RGB-D camera installed. Grasping experiments proved that the robot system can effectively implement common operations such as picking and moving objects, thereby demonstrating its potential being applied in all kinds of real-time manufacturing applications.