2D Visual Odometry method for Global Positioning Measurement
The goal of this paper is to develop a method for estimating the 2D trajectory of a road vehicle using visual odometry. To do so, the ego-motion of the vehicle relative to the road is computed using a stereo-vision system mounted next to the rear view mirror. Feature points are computed using Harris...
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creator | Garcia, R.G. Sotelo, M.A. Parra, I. Fernandez, D. Gavilan, M. |
description | The goal of this paper is to develop a method for estimating the 2D trajectory of a road vehicle using visual odometry. To do so, the ego-motion of the vehicle relative to the road is computed using a stereo-vision system mounted next to the rear view mirror. Feature points are computed using Harris detector. After that, features are matched between pairs of frames and linked into 2D trajectories. A photogrametric approach is proposed to solve the non-linear equations using a least-squared approximation. The purpose is to merge trajectory information provided by the visual odometry system with information provided by other sensors, such as GPS, in order to produce really accurate measurements of vehicle position. Providing assistance to drivers is among the prime applications of the proposed method. Nonetheless, other applications such as autonomous robot or vehicle navigation are also considered. The proposed method has been tested in real traffic conditions without using prior knowledge about the scene nor the vehicle motion. We provide examples of estimated vehicle trajectories using the proposed method and discuss the key issues for further improvement. |
doi_str_mv | 10.1109/WISP.2007.4447545 |
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We provide examples of estimated vehicle trajectories using the proposed method and discuss the key issues for further improvement.</description><subject>2D visual odometry</subject><subject>Computer vision</subject><subject>Detectors</subject><subject>egomotion estimation</subject><subject>global position measurement</subject><subject>Global Positioning System</subject><subject>Mirrors</subject><subject>non-linear least squares</subject><subject>Nonlinear equations</subject><subject>Position measurement</subject><subject>RANSAC</subject><subject>Remotely operated vehicles</subject><subject>Road vehicles</subject><subject>Robot sensing systems</subject><subject>Sensor systems</subject><isbn>9781424408290</isbn><isbn>1424408296</isbn><isbn>9781424408306</isbn><isbn>142440830X</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpNj81KAzEUhSMiKHUeQNzkBWa8N8nkB9xI1VqotGDRZUkmGY3MTGQyXfTtLdiFm3P4OPDBIeQGoUIEc_exfNtUDEBVQghVi_qMFEZpFEwI0Bzk-X9mBi5JkfM3AKCS3BhzRe7ZI32PeW87uvapD9N4oMf8Sp62aaSLLrnjtEk5TjENcfikr8Hm_Rj6MEzX5KK1XQ7FqWdk-_y0nb-Uq_ViOX9YldHAVHLBHUPlamkdcO_BIjZKC6bAOxQqOGaF175pJaJF6aEBaTkzodYN18Bn5PZPG0MIu58x9nY87E6X-S-U2kjR</recordid><startdate>200710</startdate><enddate>200710</enddate><creator>Garcia, R.G.</creator><creator>Sotelo, M.A.</creator><creator>Parra, I.</creator><creator>Fernandez, D.</creator><creator>Gavilan, M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200710</creationdate><title>2D Visual Odometry method for Global Positioning Measurement</title><author>Garcia, R.G. ; Sotelo, M.A. ; Parra, I. ; Fernandez, D. ; Gavilan, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-343b217b56ab03dd0a11c784270db147eb2a4d8dcf611a16d0c06a329e58c3803</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>2D visual odometry</topic><topic>Computer vision</topic><topic>Detectors</topic><topic>egomotion estimation</topic><topic>global position measurement</topic><topic>Global Positioning System</topic><topic>Mirrors</topic><topic>non-linear least squares</topic><topic>Nonlinear equations</topic><topic>Position measurement</topic><topic>RANSAC</topic><topic>Remotely operated vehicles</topic><topic>Road vehicles</topic><topic>Robot sensing systems</topic><topic>Sensor systems</topic><toplevel>online_resources</toplevel><creatorcontrib>Garcia, R.G.</creatorcontrib><creatorcontrib>Sotelo, M.A.</creatorcontrib><creatorcontrib>Parra, I.</creatorcontrib><creatorcontrib>Fernandez, D.</creatorcontrib><creatorcontrib>Gavilan, M.</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>Garcia, R.G.</au><au>Sotelo, M.A.</au><au>Parra, I.</au><au>Fernandez, D.</au><au>Gavilan, M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>2D Visual Odometry method for Global Positioning Measurement</atitle><btitle>2007 IEEE International Symposium on Intelligent Signal Processing</btitle><stitle>WISP</stitle><date>2007-10</date><risdate>2007</risdate><spage>1</spage><epage>6</epage><pages>1-6</pages><isbn>9781424408290</isbn><isbn>1424408296</isbn><eisbn>9781424408306</eisbn><eisbn>142440830X</eisbn><abstract>The goal of this paper is to develop a method for estimating the 2D trajectory of a road vehicle using visual odometry. To do so, the ego-motion of the vehicle relative to the road is computed using a stereo-vision system mounted next to the rear view mirror. Feature points are computed using Harris detector. After that, features are matched between pairs of frames and linked into 2D trajectories. A photogrametric approach is proposed to solve the non-linear equations using a least-squared approximation. The purpose is to merge trajectory information provided by the visual odometry system with information provided by other sensors, such as GPS, in order to produce really accurate measurements of vehicle position. Providing assistance to drivers is among the prime applications of the proposed method. Nonetheless, other applications such as autonomous robot or vehicle navigation are also considered. The proposed method has been tested in real traffic conditions without using prior knowledge about the scene nor the vehicle motion. We provide examples of estimated vehicle trajectories using the proposed method and discuss the key issues for further improvement.</abstract><pub>IEEE</pub><doi>10.1109/WISP.2007.4447545</doi><tpages>6</tpages></addata></record> |
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language | eng |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | 2D visual odometry Computer vision Detectors egomotion estimation global position measurement Global Positioning System Mirrors non-linear least squares Nonlinear equations Position measurement RANSAC Remotely operated vehicles Road vehicles Robot sensing systems Sensor systems |
title | 2D Visual Odometry method for Global Positioning Measurement |
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