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    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/116041

    题名: Visual Object Recognition and Pose Estimation Based on a Deep Semantic Segmentation Network
    作者: Chien-Ming Lin;Chi-Yi Tsai;Yu-Cheng Lai;Shin-An Li;Ching-Chang Wong
    关键词: Pose estimation;Three-dimensional displays;Robots;Visual perception;Image segmentation;Object recognition;Semantics
    日期: 2018-09-18
    上传时间: 2019-03-23 12:10:24 (UTC+8)
    出版者: IEEE
    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.
    關聯: IEEE Sensors Journal 18(22), p.9370-9381
    DOI: 10.1109/JSEN.2018.2870957
    显示于类别:[電機工程學系暨研究所] 期刊論文


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