dc.creatorBarrios, Pablo
dc.creatorAdams, Martin
dc.creatorLeung, Keith
dc.creatorInostroza, Felipe
dc.creatorNaqvi, Ghayur
dc.creatorOrchard Concha, Marcos
dc.date.accessioned2019-05-29T13:10:24Z
dc.date.available2019-05-29T13:10:24Z
dc.date.created2019-05-29T13:10:24Z
dc.date.issued2017
dc.identifierIEEE Transactions on Robotics, Volumen 33, Issue 1, 2017, Pages 198-213
dc.identifier15523098
dc.identifier10.1109/TRO.2016.2627027
dc.identifierhttps://repositorio.uchile.cl/handle/2250/168807
dc.description.abstractInrobotic mapping and simultaneous localization and mapping, the ability to assess the quality of estimated maps is crucial. While concepts exist for quantifying the error in the estimated trajectory of a robot, or a subset of the estimated feature locations, the difference between all current estimated and ground-truth features is rarely considered jointly. In contrast to many current methods, this paper analyzes metrics, which automatically evaluate maps based on their joint detection and description uncertainty. In the tracking literature, the optimal subpattern assignment (OSPA) metric provided a solution to the problem of assessing target tracking algorithms and has recently been applied to the assessment of robotic maps. Despite its advantages over other metrics, the OSPA metric can saturate to a limiting value irrespective of the cardinality errors and it penalizes missed detections and false alarms in an unequal manner. This paper therefore introduces the cardinalized optimal linear assignment (COLA) metric, as a complement to the OSPA metric, for feature map evaluation. Their combination is shown to provide a robust solution for the evaluation of map estimation errors in an intuitive manner.
dc.languageen
dc.publisherIEEE
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile
dc.sourceIEEE Transactions on Robotics
dc.subjectMap metric
dc.subjectMobile robots
dc.subjectSimultaneous localization and mapping
dc.titleMetrics for Evaluating Feature-Based Mapping Performance
dc.typeArtículo de revista


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