Vision-based localization algorithm based on landmark matching, triangulation, reconstruction, and comparison
Many generic position-estimation algorithms are vulnerable to ambiguity introduced by nonunique landmarks. Also, the available high-dimensional image data is not fully used when these techniques are extended to vision-based localization. This paper presents the landmark matching, triangulation, reco...
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| Veröffentlicht in: | IEEE transactions on robotics 2005-04, Vol.21 (2), p.217-226 |
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| container_title | IEEE transactions on robotics |
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| creator | Yuen, D.C.K. MacDonald, B.A. |
| description | Many generic position-estimation algorithms are vulnerable to ambiguity introduced by nonunique landmarks. Also, the available high-dimensional image data is not fully used when these techniques are extended to vision-based localization. This paper presents the landmark matching, triangulation, reconstruction, and comparison (LTRQ global localization algorithm, which is reasonably immune to ambiguous landmark matches. It extracts natural landmarks for the (rough) matching stage before generating the list of possible position estimates through triangulation. Reconstruction and comparison then rank the possible estimates. The LTRC algorithm has been implemented using an interpreted language, onto a robot equipped with a panoramic vision system. Empirical data shows remarkable improvement in accuracy when compared with the established random sample consensus method. LTRC is also robust against inaccurate map data. |
| doi_str_mv | 10.1109/TRO.2004.835452 |
| format | Article |
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Also, the available high-dimensional image data is not fully used when these techniques are extended to vision-based localization. This paper presents the landmark matching, triangulation, reconstruction, and comparison (LTRQ global localization algorithm, which is reasonably immune to ambiguous landmark matches. It extracts natural landmarks for the (rough) matching stage before generating the list of possible position estimates through triangulation. Reconstruction and comparison then rank the possible estimates. The LTRC algorithm has been implemented using an interpreted language, onto a robot equipped with a panoramic vision system. Empirical data shows remarkable improvement in accuracy when compared with the established random sample consensus method. LTRC is also robust against inaccurate map data.</description><identifier>ISSN: 1552-3098</identifier><identifier>EISSN: 1941-0468</identifier><identifier>DOI: 10.1109/TRO.2004.835452</identifier><identifier>CODEN: ITREAE</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Algorithms ; and comparison (LTRC) ; Applied sciences ; Comparative analysis ; Computer science; control theory; systems ; Control theory. 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Also, the available high-dimensional image data is not fully used when these techniques are extended to vision-based localization. This paper presents the landmark matching, triangulation, reconstruction, and comparison (LTRQ global localization algorithm, which is reasonably immune to ambiguous landmark matches. It extracts natural landmarks for the (rough) matching stage before generating the list of possible position estimates through triangulation. Reconstruction and comparison then rank the possible estimates. The LTRC algorithm has been implemented using an interpreted language, onto a robot equipped with a panoramic vision system. Empirical data shows remarkable improvement in accuracy when compared with the established random sample consensus method. LTRC is also robust against inaccurate map data.</description><subject>Algorithms</subject><subject>and comparison (LTRC)</subject><subject>Applied sciences</subject><subject>Comparative analysis</subject><subject>Computer science; control theory; systems</subject><subject>Control theory. Systems</subject><subject>Data mining</subject><subject>Exact sciences and technology</subject><subject>Image reconstruction</subject><subject>Image sensors</subject><subject>Insects</subject><subject>Landmark matching</subject><subject>Machine vision</subject><subject>Miscellaneous</subject><subject>Mobile robots</subject><subject>natural landmark</subject><subject>Navigation</subject><subject>panoramic image</subject><subject>random sample consensus (RANSAC)</subject><subject>reconstruction</subject><subject>Robot localization</subject><subject>Robot sensing systems</subject><subject>Robot vision systems</subject><subject>Robotics</subject><subject>Robots</subject><subject>triangulation</subject><subject>Vision systems</subject><subject>vision-based localization</subject><issn>1552-3098</issn><issn>1941-0468</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqFkctLxDAQxoso-Dx78FIEPdndyaNpchTxBYIgq9eQTdM12jZr0h70r3e0guDFUyYzv2-YmS_LDgnMCAE1XzzczygAn0lW8pJuZDtEcVIAF3IT47KkBQMlt7PdlF4AKFfAdrLuyScf-mJpkqvzNljT-g8zYCo37SpEPzx3-VTEVGv6ujPxNe_MYJ99vzrLh-hNvxrbb81ZHp0NfRriaKc_CnIburWJPoV-P9tqTJvcwc-7lz1eXS4uboq7--vbi_O7wpZEDIWhRhLVKCkdF1WFY3OuhDUNJXXDHbFyyWsjuCKYc4wugSsHdVkxUTFLG7aXnU591zG8jS4NuvPJuhbnd2FMmiqQwLDzv6CESohSIXj8B3wJY-xxCU2BCCglEITmE2RjSCm6Rq-jx3u9awL6yySNJukvk_RkEipOftqahKdvoumtT78yISSuBMgdTZx3zv2WORGqYuwT6T6bZw</recordid><startdate>20050401</startdate><enddate>20050401</enddate><creator>Yuen, D.C.K.</creator><creator>MacDonald, B.A.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Systems</topic><topic>Data mining</topic><topic>Exact sciences and technology</topic><topic>Image reconstruction</topic><topic>Image sensors</topic><topic>Insects</topic><topic>Landmark matching</topic><topic>Machine vision</topic><topic>Miscellaneous</topic><topic>Mobile robots</topic><topic>natural landmark</topic><topic>Navigation</topic><topic>panoramic image</topic><topic>random sample consensus (RANSAC)</topic><topic>reconstruction</topic><topic>Robot localization</topic><topic>Robot sensing systems</topic><topic>Robot vision systems</topic><topic>Robotics</topic><topic>Robots</topic><topic>triangulation</topic><topic>Vision systems</topic><topic>vision-based localization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yuen, D.C.K.</creatorcontrib><creatorcontrib>MacDonald, B.A.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Aerospace Database</collection><jtitle>IEEE transactions on robotics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yuen, D.C.K.</au><au>MacDonald, B.A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Vision-based localization algorithm based on landmark matching, triangulation, reconstruction, and comparison</atitle><jtitle>IEEE transactions on robotics</jtitle><stitle>TRO</stitle><date>2005-04-01</date><risdate>2005</risdate><volume>21</volume><issue>2</issue><spage>217</spage><epage>226</epage><pages>217-226</pages><issn>1552-3098</issn><eissn>1941-0468</eissn><coden>ITREAE</coden><abstract>Many generic position-estimation algorithms are vulnerable to ambiguity introduced by nonunique landmarks. Also, the available high-dimensional image data is not fully used when these techniques are extended to vision-based localization. This paper presents the landmark matching, triangulation, reconstruction, and comparison (LTRQ global localization algorithm, which is reasonably immune to ambiguous landmark matches. It extracts natural landmarks for the (rough) matching stage before generating the list of possible position estimates through triangulation. Reconstruction and comparison then rank the possible estimates. The LTRC algorithm has been implemented using an interpreted language, onto a robot equipped with a panoramic vision system. Empirical data shows remarkable improvement in accuracy when compared with the established random sample consensus method. LTRC is also robust against inaccurate map data.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TRO.2004.835452</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | IEEE transactions on robotics, 2005-04, Vol.21 (2), p.217-226 |
| issn | 1552-3098 1941-0468 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms and comparison (LTRC) Applied sciences Comparative analysis Computer science control theory systems Control theory. Systems Data mining Exact sciences and technology Image reconstruction Image sensors Insects Landmark matching Machine vision Miscellaneous Mobile robots natural landmark Navigation panoramic image random sample consensus (RANSAC) reconstruction Robot localization Robot sensing systems Robot vision systems Robotics Robots triangulation Vision systems vision-based localization |
| title | Vision-based localization algorithm based on landmark matching, triangulation, reconstruction, and comparison |
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