Efficient global scan matching using saliency-based scan point resampling

This paper presents a method of improving the performance of global scan matching with a laser range finder using saliency-based scan point resampling. The proposed method calculates the saliency of each scan point according to the amount of information the scan point has, or the length of the line...

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Main Author: Tomono, M.
Format: Conference Proceeding
Language:English
Subjects:
Curve fitting
Degradation
Feature extraction
Global localization
Laser theory
Map building
Matched filters
Mobile robots
Paper technology
Robot sensing systems
Scan matching
Shape
Simultaneous localization and mapping
SLAM
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description This paper presents a method of improving the performance of global scan matching with a laser range finder using saliency-based scan point resampling. The proposed method calculates the saliency of each scan point according to the amount of information the scan point has, or the length of the line segment on which the scan point lies. Based on the saliency, the method preserves important scan points and discards redundant scan points. Experimental results show that the proposed method improves the efficiency and accuracy of global scan matching without degrading matching rates.
doi_str_mv 10.1109/IROS.2005.1545180
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The proposed method calculates the saliency of each scan point according to the amount of information the scan point has, or the length of the line segment on which the scan point lies. Based on the saliency, the method preserves important scan points and discards redundant scan points. 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The proposed method calculates the saliency of each scan point according to the amount of information the scan point has, or the length of the line segment on which the scan point lies. Based on the saliency, the method preserves important scan points and discards redundant scan points. Experimental results show that the proposed method improves the efficiency and accuracy of global scan matching without degrading matching rates.</description><subject>Curve fitting</subject><subject>Degradation</subject><subject>Feature extraction</subject><subject>Global localization</subject><subject>Laser theory</subject><subject>Map building</subject><subject>Matched filters</subject><subject>Mobile robots</subject><subject>Paper technology</subject><subject>Robot sensing systems</subject><subject>Scan matching</subject><subject>Shape</subject><subject>Simultaneous localization and mapping</subject><subject>SLAM</subject><issn>2153-0858</issn><issn>2153-0866</issn><isbn>0780389123</isbn><isbn>9780780389120</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo9kEtPwzAQhC0eEqX0ByAu-QMpu3Y2to-oKhCpUiUe58rxoxglaRSHQ_89Qa2Yw-xhvtnDMHaPsEQE_Vi9bd-XHICWSAWhggs240giB1WWl-wWpAKhNHJx9R-QumGLlL5hktBUoJyxah1CtNF3Y7ZvDrVpsmRNl7VmtF-x22c_6c-TaSbEHvPaJO9OSH-IU2nwybR9M0F37DqYJvnF-c7Z5_P6Y_Wab7Yv1eppk0eUNOYBpDOewAWODq0KgUsuSq0LRdxLEQwFVBQCaK-Ut6W1tSYOyjsSRenEnD2c_kbv_a4fYmuG4-68gvgFU0pPNw</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Tomono, M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2005</creationdate><title>Efficient global scan matching using saliency-based scan point resampling</title><author>Tomono, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-f07dae50df21d1c8ff27236994852e73fa5f185ff09e88ec6ccb95208ed5346d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Curve fitting</topic><topic>Degradation</topic><topic>Feature extraction</topic><topic>Global localization</topic><topic>Laser theory</topic><topic>Map building</topic><topic>Matched filters</topic><topic>Mobile robots</topic><topic>Paper technology</topic><topic>Robot sensing systems</topic><topic>Scan matching</topic><topic>Shape</topic><topic>Simultaneous localization and mapping</topic><topic>SLAM</topic><toplevel>online_resources</toplevel><creatorcontrib>Tomono, M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tomono, M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Efficient global scan matching using saliency-based scan point resampling</atitle><btitle>2005 IEEE/RSJ International Conference on Intelligent Robots and Systems</btitle><stitle>IROS</stitle><date>2005</date><risdate>2005</risdate><spage>1856</spage><epage>1861</epage><pages>1856-1861</pages><issn>2153-0858</issn><eissn>2153-0866</eissn><isbn>0780389123</isbn><isbn>9780780389120</isbn><abstract>This paper presents a method of improving the performance of global scan matching with a laser range finder using saliency-based scan point resampling. The proposed method calculates the saliency of each scan point according to the amount of information the scan point has, or the length of the line segment on which the scan point lies. Based on the saliency, the method preserves important scan points and discards redundant scan points. Experimental results show that the proposed method improves the efficiency and accuracy of global scan matching without degrading matching rates.</abstract><pub>IEEE</pub><doi>10.1109/IROS.2005.1545180</doi><tpages>6</tpages></addata></record>
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Curve fitting
Degradation
Feature extraction
Global localization
Laser theory
Map building
Matched filters
Mobile robots
Paper technology
Robot sensing systems
Scan matching
Shape
Simultaneous localization and mapping
SLAM
title Efficient global scan matching using saliency-based scan point resampling
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