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Two different tools for three-dimensional mapping: DE-based scan matching and feature-based loop detection

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dc.affiliation.dptoUC3M. Departamento de Ingeniería de Sistemas y Automáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Robótica (Robotics Lab)es
dc.contributor.authorMartín Monar, Fernandoes
dc.contributor.authorTriebel, Rudolph
dc.contributor.authorMoreno Lorente, Luis Enriquees
dc.contributor.authorSiegwart, Roland
dc.date.accessioned2015-03-20T12:10:10Z
dc.date.available2015-03-20T12:10:10Z
dc.date.issued2014-01
dc.description.abstractAn autonomous robot must obtain information about its surroundings to accomplish multiple tasks that are greatly improved when this information is efficiently incorporated into amap. Some examples are navigation, manipulation, localization, etc. This mapping problem has been an important research area in mobile robotics during last decades. It does not have a unique solution and can be divided into multiple sub-problems. Two different aspects of the mobile robot mapping problem are addressed in this work. First, we have developed a Differential Evolution-based scan matching algorithm that operates with high accuracy in three-dimensional environments. The map obtained by an autonomous robot must be consistent after registration. It is basic to detect when the robot is navigating around a previously visited place in order to minimize the accumulated error. This phase, which is called loop detection, is the second aspect studied here. We have developed an algorithm that extracts the most important features from two different three-dimensional laser scans in order to obtain a loop indicator that is used to detect when the robot is visiting a known place. This approach allows the introduction of very different characteristics in the descriptor. First, the surface features include the geometric forms of the scan (lines, planes, and spheres). Second, the numerical features are values that describe several numerical properties of the measurements: volume, average range, curvature, etc. Both algorithms have been tested with real data to demonstrate that these are efficient tools to be used in mapping tasks.en
dc.description.statusPublicadoes
dc.format.extent23
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationRobotica (2014). 32(01), 19-41.es
dc.identifier.doi10.1017/S026357471300060X
dc.identifier.issn0263-5747 (print)
dc.identifier.issn1469-8668 (online)
dc.identifier.publicationfirstpage19
dc.identifier.publicationissue1
dc.identifier.publicationlastpage41
dc.identifier.publicationtitleRoboticaes
dc.identifier.publicationvolume32es
dc.identifier.urihttps://hdl.handle.net/10016/20301
dc.identifier.uxxiAR/0000013198
dc.language.isoengen
dc.publisherCambridge University Pressen
dc.relation.projectIDComunidad de Madrid. S2009/DPI-1559/ROBOCITY2030 IIes
dc.relation.publisherversionhttp://dx.doi.org/10.1017/S026357471300060X
dc.rights© Cambridge University Press 2013en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaRobótica e Informática Industriales
dc.subject.otherLoop detectionen
dc.subject.otherScan matchingen
dc.subject.other6D SLAMen
dc.subject.otherDifferential evolutionen
dc.subject.otherFeature descriptoren
dc.titleTwo different tools for three-dimensional mapping: DE-based scan matching and feature-based loop detectionen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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