Author:
Liang Guoyuan,Chen Fan,Liang Yu,Feng Yachun,Wang Can,Wu Xinyu
Abstract
Nowadays, intelligent robots are widely applied in the manufacturing industry, in various working places or assembly lines. In most manufacturing tasks, determining the category and pose of parts is important, yet challenging, due to complex environments. This paper presents a new two-stage intelligent vision system based on a deep neural network with RGB-D image inputs for object recognition and 6D pose estimation. A dense-connected network fusing multi-scale features is first built to segment the objects from the background. The 2D pixels and 3D points in cropped object regions are then 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 feature extraction method, 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 built 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 up and moving objects, thereby demonstrating its potential to be applied in all kinds of real-time manufacturing applications.
Subject
Artificial Intelligence,Biomedical Engineering
Reference40 articles.
1. Segnet: a deep convolutional encoder-decoder architecture for scene segmentation;Badrinarayanan;IEEE Trans. Pattern Anal. Mach. Intell,2017
2. Learning 6D object pose estimation using 3D object coordinates;Brachmann,2014
3. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs;Chen;IEEE Trans. Pattern Anal. Mach. Intell
4. Rethinking atrous convolution for semantic image segmentation;Chen
5. The importance of skip connections in biomedical image segmentation;Drozdzal,2016
Cited by
14 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献