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dc.contributor.authorLeonardi, Marco
dc.contributor.authorStahl, Annette
dc.date.accessioned2019-04-12T10:24:08Z
dc.date.available2019-04-12T10:24:08Z
dc.date.created2019-01-18T14:25:39Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/11250/2594467
dc.description.abstractA correct loop closure detection is an important component of a robust SLAM (simultaneous localization and mapping) system. Loop closing refers to the process of correctly asserting that a mobile robot has returned to a previous visited location. Failing to detect a loop closure does in general not pose a threat to the positioning and mapping system of a robot, but a wrong loop closure can lead to drift of the robot and can therefore jeopardize the robot’s mission. In this paper a robust, highly parallelizable standalone algorithm for globally detecting loop closures is proposed. The algorithm is purposely built with the goal of avoiding false positives, while maintaining reasonable true positives performances. Tests conducted on the KITTI and the Scott Reef 25 dataset show that when bag-of-words approaches perform poorly, our presented approach is able to avoid wrong loop closures.nb_NO
dc.language.isoengnb_NO
dc.publisherInternational Federation of Automatic Control (IFAC)nb_NO
dc.relation.ispartof11th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles CAMS 2018 Opatija, Croatia, 10–12 September 2018
dc.titleConvolutional Autoencoder aided loop closure detection for monocular SLAMnb_NO
dc.typeChapternb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber159-164nb_NO
dc.identifier.doi10.1016/j.ifacol.2018.09.486
dc.identifier.cristin1660429
dc.description.localcode© 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd.nb_NO
cristin.unitcode194,63,25,0
cristin.unitnameInstitutt for teknisk kybernetikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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