教師資料查詢 | 類別: 期刊論文 | 教師: 蔡奇謚 Chi-yi Tsai (瀏覽個人網頁)

標題:Visual Object Recognition and Pose Estimation Based on a Deep Semantic Segmentation Network
學年107
學期1
出版(發表)日期2018/09/18
作品名稱Visual Object Recognition and Pose Estimation Based on a Deep Semantic Segmentation Network
作品名稱(其他語言)
著者Chien-Ming Lin; Chi-Yi Tsai; Yu-Cheng Lai; Shin-An Li; Ching-Chang Wong
單位
出版者
著錄名稱、卷期、頁數IEEE Sensors Journal 18(22), p.9370-9381
摘要In recent years, deep learning-based object recognition algorithms become emerging in robotic vision applications. This paper addresses the design of a novel deep learning-based visual object recognition and pose estimation system for a robot manipulator to handle random object picking tasks. The proposed visual control system consists of a visual perception module, an object pose estimation module, a data argumentation module, and a robot manipulator controller. The visual perception module combines deep convolution neural networks (CNNs) and a fully connected conditional random field layer to realize an image semantic segmentation function, which can provide stable and accurate object classification results in cluttered environments. The object pose estimation module implements a model-based pose estimation method to estimate the 3D pose of the target for picking control. In addition, the proposed data argumentation module automatically generates training data for training the deep CNN. Experimental results show that the proposed scene segmentation method used in the data argumentation module reaches a high accuracy rate of 97.10% on average, which is higher than other state-of-the-art segment methods. Moreover, with the proposed data argumentation module, the visual perception module reaches an accuracy rate over than 80% and 72% in the case of detecting and recognizing one object and three objects, respectively. In addition, the proposed model-based pose estimation method provides accurate 3D pose estimation results. The average translation and rotation errors in the three axes are all smaller than 0.52 cm and 3.95 degrees, respectively. These advantages make the proposed visual control system suitable for applications of random object picking and manipulation.
關鍵字Pose estimation;Three-dimensional displays;Robots;Visual perception;Image segmentation;Object recognition;Semantics
語言英文(美國)
ISSN1530-437X
期刊性質國外
收錄於SCI;EI;
產學合作
通訊作者
審稿制度
國別美國
公開徵稿
出版型式,電子版,紙本
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