English  |  正體中文  |  简体中文  |  Items with full text/Total items : 62567/95223 (66%)
Visitors : 2520272      Online Users : 279
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/115395

    Title: Visual object recognition and pose estimation based on a deep semantic segmentation network
    Authors: Chien-Ming Lin;Chi-Yi Tsai;Yu-Cheng Lai;Shin-An Li;Ching-Chang Wong
    Keywords: Pose estimation;Three-dimensional displays;Robots;Visual perception;Image segmentation;Object recognition;Semantics
    Date: 2018-11-15
    Issue Date: 2018-10-25 12:10:22 (UTC+8)
    Publisher: IEEE Sensors Journal
    Abstract: 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.
    Relation: IEEE Sensors Journal 18(22), p.1-11
    DOI: 10.1109/JSEN.2018.2870957
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

    Files in This Item:

    File Description SizeFormat
    Visual object recognition and pose estimation based on a deep semantic segmentation network.pdf2520KbAdobe PDF1View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback