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dc.contributor.convenorHenk Nijmeijer and Bart van Aremen_AU
dc.contributor.authorParra, Ignacioen_US
dc.contributor.authorSotelo, Miguel Angelen_US
dc.contributor.authorVlacic, Ljuboen_US
dc.contributor.editorHenk Nijmeijer and Bart van Aremen_US
dc.date.accessioned2017-04-24T09:05:25Z
dc.date.available2017-04-24T09:05:25Z
dc.date.issued2008en_US
dc.date.modified2010-10-27T08:27:09Z
dc.identifier.doi10.1109/IVS.2008.4621277en_AU
dc.identifier.urihttp://hdl.handle.net/10072/24153
dc.description.abstractThis paper describes a new approach for estimating the vehicle motion trajectory in complex urban environments by means of visual odometry. A new strategy for robust feature extraction and data post-processing is developed and tested on-road. Scale-invariant Image Features (SIFT) are used in order to cope with the complexity of urban environments. The obtained results are discussed and compared to previous works. In the prototype system, the ego-motion of the vehicle is computed using a stereo-vision system mounted next to the rear view mirror of the car. Feature points are matched between pairs of frames and linked into 3D trajectories. The distance between estimations is dynamically adapted based on reprojection and estimation errors. Vehicle motion is estimated using the non-linear, photogrametric approach based on RANSAC (RAndom SAmple Consensus). The obvious application of the method is to provide on-board driver assistance in navigation tasks, or to provide a means of autonomously navigating a vehicle. The method has been tested in real traffic conditions without using prior knowledge about the scene or the vehicle motion. An example of how to estimate a vehiclepsilas trajectory is provided along with suggestions for possible further improvement of the proposed odometry algorithm.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent592957 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherIEEE, Eindhoven University of Technologyen_US
dc.publisher.placeEindhoven, The Netherlandsen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencenameIV08 Intelligent Vehicles Symposiumen_US
dc.relation.ispartofconferencetitleProceedings of the IEEE Intelligent Vehicles Symposium - IV08en_US
dc.relation.ispartofdatefrom2008-06-04en_US
dc.relation.ispartofdateto2008-06-06en_US
dc.relation.ispartoflocationEindhoven, The netherlandsen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchControl Systems, Robotics and Automationen_US
dc.subject.fieldofresearchcode090602en_US
dc.titleRobust Visual Odometry for Complex Urban Environmentsen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.rights.copyrightCopyright 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_AU
gro.date.issued2008
gro.hasfulltextFull Text


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