English  |  正體中文  |  简体中文  |  Items with full text/Total items : 53757/88386 (61%)
Visitors : 10525402      Online Users : 25
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: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/65320


    Title: Image Feature Initialization for SLAM and Moving Object Detection
    Authors: Wang, Yin-Tien;Ju, Rukg-Chi;Huang, Yu-Wen;Lin, Ming-Chun
    Contributors: 淡江大學機械與機電工程學系
    Keywords: Image feature initialization;Speeded up robust features (SURF);Simnltaneons localization and mapping(SLAM);Moving object detection
    Date: 2009-09
    Issue Date: 2011-10-20 21:40:02 (UTC+8)
    Publisher: Japan: ICIC International
    Abstract: For robot visual Simultaneous Localization and Mapping (vSLAM), the first task is to initialize the image features. We present an improved feature initialization algorithm for robot SLAM, including detection of image features, selection of good features, calculation of image depth, and update of feature locations. Meanwhile, we extend the usage of the feature initialization algorithm to mapping and detection of features on a moving object. Experimentation is performed with real platform and the results show that the performance of the proposed feature initialization algorithm is efficient for visual SLAM and moving object detection.
    Relation: ICIC Express Letters 3(3)pt.A, pp.477-482
    Appears in Collections:[Graduate Institute & Department of Mechanical and Electro-Mechanical Engineering] Journal Article

    Files in This Item:

    There are no files associated with this item.

    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