English  |  正體中文  |  简体中文  |  Items with full text/Total items : 49521/84657 (58%)
Visitors : 7597493      Online Users : 65
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/987654321/92586


    Title: Data Association and Map Management for Robot SLAM using Local Invariant Features
    Authors: Wang, Yin-Tien;Feng, Ying-Chieh
    Contributors: 淡江大學機械與機電工程學系
    Keywords: Robot Mapping;Local Invariant Feature Detectors;Speeded-Up Robust Features (SURF);Simultaneous Localization and Mapping (SLAM)
    Date: 2013-08-04
    Issue Date: 2013-10-19 12:16:46 (UTC+8)
    Publisher: New York : IEEE Computer Society, Institute of Electrical and Electronics Engineers
    Abstract: To build a persistent map with visual landmarks is one of the most important steps for implementing the visual simultaneous localization and mapping (SLAM). The corner detector is a common method utilized to detect visual landmarks for constructing a map of the environment. However, due to the scalevariant characteristic of corner detection, extensive computational cost is needed to recover the scale and orientation of corner features in SLAM tasks. The purpose of this paper is to build the map using a local invariant feature detector, namely speeded-up robust features (SURF), to detect scale- and orientation-invariant features as well as provide a robust representation of visual landmarks for SLAM. The procedures of detection, description and matching of regular SURF algorithms are modified in this paper in order to provide a robust data-association of visual landmarks in SLAM. Furthermore, the effective method of map management for SURF features in SLAM is also designed to improve the accuracy of robot state estimation.
    Relation: Proceedings of 2013 IEEE International Conference on Mechatronics and Automation (ICMA), pp.1102-1107
    Appears in Collections:[機械與機電工程學系暨研究所] 會議論文

    Files in This Item:

    File Description SizeFormat
    Wang2013Proc.IEEEicma.pdf575KbAdobe PDF288View/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