Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11772
DC FieldValueLanguage
dc.contributorDepartment of Electrical Engineering-
dc.creatorZhang, X-
dc.creatorRad, AB-
dc.creatorWong, YK-
dc.date.accessioned2015-07-14T01:29:51Z-
dc.date.available2015-07-14T01:29:51Z-
dc.identifier.urihttp://hdl.handle.net/10397/11772-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2012 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).en_US
dc.rightsThe following publication Zhang, X., Rad, A. B., & Wong, Y. K. (2012). Sensor fusion of monocular cameras and laser rangefinders for line-based simultaneous localization and mapping (SLAM) tasks in autonomous mobile robots. Sensors, 12(1), (Suppl. ), 429-452 is available athttps://dx.doi.org/10.3390/s120100429en_US
dc.subjectFeature fusionen_US
dc.subjectMulti-sensor point estimation fusion (MPEF)en_US
dc.subjectHomography transform matrixen_US
dc.subjectSLAMen_US
dc.titleSensor fusion of monocular cameras and laser rangefinders for line-based simultaneous localization and mapping (SLAM) tasks in autonomous mobile robotsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage429-
dc.identifier.epage452-
dc.identifier.volume12-
dc.identifier.issue1-
dc.identifier.doi10.3390/s120100429-
dcterms.abstractThis paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM) in dynamic environments. The designed approach consists of two features: (i) the first one is a fusion module which synthesizes line segments obtained from laser rangefinder and line features extracted from monocular camera. This policy eliminates any pseudo segments that appear from any momentary pause of dynamic objects in laser data. (ii) The second characteristic is a modified multi-sensor point estimation fusion SLAM (MPEF-SLAM) that incorporates two individual Extended Kalman Filter (EKF) based SLAM algorithms: monocular and laser SLAM. The error of the localization in fused SLAM is reduced compared with those of individual SLAM. Additionally, a new data association technique based on the homography transformation matrix is developed for monocular SLAM. This data association method relaxes the pleonastic computation. The experimental results validate the performance of the proposed sensor fusion and data association method.-
dcterms.bibliographicCitationSensors, Jan. 2012, v. 12, no. 1, p. 429-452-
dcterms.isPartOfSensors-
dcterms.issued2012-
dc.identifier.isiWOS:000299537100024-
dc.identifier.pmid22368478-
dc.identifier.eissn1424-8220-
dc.identifier.rosgroupidr56998-
dc.description.ros2011-2012 > Academic research: refereed > Publication in refereed journal-
dc.description.oapublished_final-
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