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dc.contributor.authorGómez-Ojeda, Rubén
dc.contributor.authorGonzález-Jiménez, Javier
dc.date.accessioned2016-05-27T06:42:08Z
dc.date.available2016-05-27T06:42:08Z
dc.date.issued2016-05
dc.identifier.urihttp://hdl.handle.net/10630/11515
dc.description.abstractMost approaches to stereo visual odometry reconstruct the motion based on the tracking of point features along a sequence of images. However, in low-textured scenes it is often difficult to encounter a large set of point features, or it may happen that they are not well distributed over the image, so that the behavior of these algorithms deteriorates. This paper proposes a probabilistic approach to stereo visual odometry based on the combination of both point and line segment that works robustly in a wide variety of scenarios. The camera motion is recovered through non-linear minimization of the projection errors of both point and line segment features. In order to effectively combine both types of features, their associated errors are weighted according to their covariance matrices, computed from the propagation of Gaussian distribution errors in the sensor measurements. The method, of course, is computationally more expensive that using only one type of feature, but still can run in real-time on a standard computer and provides interesting advantages, including a straightforward integration into any probabilistic framework commonly employed in mobile robotics.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Project "PROMOVE: Advances in mobile robotics for promoting independent life of elders", funded by the Spanish Government and the "European Regional Development Fund ERDF" under contract DPI2014-55826-R.es_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectRobóticaes_ES
dc.subject.otherStereo visiones_ES
dc.subject.otherVisual odormetryes_ES
dc.subject.otherMobile robotes_ES
dc.subject.otherLine segmentes_ES
dc.titleRobust Stereo Visual Odometry through a Probabilistic Combination of Points and Line Segmentses_ES
dc.typeinfo:eu-repo/semantics/workingPaperes_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.relation.eventtitle2016 IEEE International Conference on Robotics and Automation (ICRA)es_ES
dc.relation.eventplaceStockholm, Swedenes_ES
dc.relation.eventdateMayo 2016es_ES
dc.cclicenseby-nces_ES


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