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Natural Landmarks Extraction Method from Range Image for Mobile Robot
This article describes a natural landmarks detection method to use with conventional 2D laser rangefinders. The method consists of three main parts: data clustering, smoothing and segmentation. A smoothing algorithm within a scale space framework is introduced to smooth the range image. This is achi...
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| creator | Xiaowei Feng Yongyi He Wuxin Huang Jian Yuan |
| description | This article describes a natural landmarks detection method to use with conventional 2D laser rangefinders. The method consists of three main parts: data clustering, smoothing and segmentation. A smoothing algorithm within a scale space framework is introduced to smooth the range image. This is achieved by repeatedly convolving the scan data with an adaptive smoothing mask calculated according to the Mahalanobis distances from a curve-based estimator, which tracks the features using UKF (unscented Kalman filter). Clustered data is segmented and characterized by the curvature of the range data. This method is robust to noise, and can reliably detect landmarks in the unstructured environment. Experimental results show that the proposed method is efficient in natural-landmark extraction. |
| doi_str_mv | 10.1109/CISP.2009.5303473 |
| format | conference_proceeding |
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The method consists of three main parts: data clustering, smoothing and segmentation. A smoothing algorithm within a scale space framework is introduced to smooth the range image. This is achieved by repeatedly convolving the scan data with an adaptive smoothing mask calculated according to the Mahalanobis distances from a curve-based estimator, which tracks the features using UKF (unscented Kalman filter). Clustered data is segmented and characterized by the curvature of the range data. This method is robust to noise, and can reliably detect landmarks in the unstructured environment. 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The method consists of three main parts: data clustering, smoothing and segmentation. A smoothing algorithm within a scale space framework is introduced to smooth the range image. This is achieved by repeatedly convolving the scan data with an adaptive smoothing mask calculated according to the Mahalanobis distances from a curve-based estimator, which tracks the features using UKF (unscented Kalman filter). Clustered data is segmented and characterized by the curvature of the range data. This method is robust to noise, and can reliably detect landmarks in the unstructured environment. Experimental results show that the proposed method is efficient in natural-landmark extraction.</description><subject>Data mining</subject><subject>Feature extraction</subject><subject>Frequency</subject><subject>Image segmentation</subject><subject>Laser beam cutting</subject><subject>Laser modes</subject><subject>Mobile robots</subject><subject>Noise robustness</subject><subject>Smoothing methods</subject><subject>Sonar navigation</subject><isbn>1424441293</isbn><isbn>9781424441297</isbn><isbn>1424441315</isbn><isbn>9781424441310</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo9kFFLwzAUhSMy0M39APElf6AzNzdpmkcp1Q06lbn3cdMmWmwbaSvov3fi8OUcDnwcDoexaxArAGFv883L80oKYVcaBSqDZ2wOSiqlAEGf_wdpccbmv6AVgNJcsOU4Nk7IVGurjb5kxSNNnwO1vKS-7mh4H3nxNQ1UTU3s-dZPb7HmYYgd31H_6vmmo6OGOPBtdE3r-S66OF2xWaB29MuTL9j-vtjn66R8etjkd2XSWDElCoKpUGY6KHRWZb6W4BWFFEVWO8pSJcmkRgQCBwEEVGgCVSZ1FTrhLC7YzV9t470_fAzNcfD34XQB_gCaU00M</recordid><startdate>200910</startdate><enddate>200910</enddate><creator>Xiaowei Feng</creator><creator>Yongyi He</creator><creator>Wuxin Huang</creator><creator>Jian Yuan</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200910</creationdate><title>Natural Landmarks Extraction Method from Range Image for Mobile Robot</title><author>Xiaowei Feng ; Yongyi He ; Wuxin Huang ; Jian Yuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-41f7c3285f43b948ed21e4af6308dba8642a7670fa1b1f101c37fac76bc3b0b93</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Data mining</topic><topic>Feature extraction</topic><topic>Frequency</topic><topic>Image segmentation</topic><topic>Laser beam cutting</topic><topic>Laser modes</topic><topic>Mobile robots</topic><topic>Noise robustness</topic><topic>Smoothing methods</topic><topic>Sonar navigation</topic><toplevel>online_resources</toplevel><creatorcontrib>Xiaowei Feng</creatorcontrib><creatorcontrib>Yongyi He</creatorcontrib><creatorcontrib>Wuxin Huang</creatorcontrib><creatorcontrib>Jian Yuan</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xiaowei Feng</au><au>Yongyi He</au><au>Wuxin Huang</au><au>Jian Yuan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Natural Landmarks Extraction Method from Range Image for Mobile Robot</atitle><btitle>2009 2nd International Congress on Image and Signal Processing</btitle><stitle>CISP</stitle><date>2009-10</date><risdate>2009</risdate><spage>1</spage><epage>5</epage><pages>1-5</pages><isbn>1424441293</isbn><isbn>9781424441297</isbn><eisbn>1424441315</eisbn><eisbn>9781424441310</eisbn><abstract>This article describes a natural landmarks detection method to use with conventional 2D laser rangefinders. The method consists of three main parts: data clustering, smoothing and segmentation. A smoothing algorithm within a scale space framework is introduced to smooth the range image. This is achieved by repeatedly convolving the scan data with an adaptive smoothing mask calculated according to the Mahalanobis distances from a curve-based estimator, which tracks the features using UKF (unscented Kalman filter). Clustered data is segmented and characterized by the curvature of the range data. This method is robust to noise, and can reliably detect landmarks in the unstructured environment. Experimental results show that the proposed method is efficient in natural-landmark extraction.</abstract><pub>IEEE</pub><doi>10.1109/CISP.2009.5303473</doi><tpages>5</tpages></addata></record> |
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| identifier | ISBN: 1424441293 |
| ispartof | 2009 2nd International Congress on Image and Signal Processing, 2009, p.1-5 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Data mining Feature extraction Frequency Image segmentation Laser beam cutting Laser modes Mobile robots Noise robustness Smoothing methods Sonar navigation |
| title | Natural Landmarks Extraction Method from Range Image for Mobile Robot |
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