dc.creator | Barrios, Pablo | |
dc.creator | Adams, Martin | |
dc.creator | Leung, Keith | |
dc.creator | Inostroza, Felipe | |
dc.creator | Naqvi, Ghayur | |
dc.creator | Orchard Concha, Marcos | |
dc.date.accessioned | 2019-05-29T13:10:24Z | |
dc.date.available | 2019-05-29T13:10:24Z | |
dc.date.created | 2019-05-29T13:10:24Z | |
dc.date.issued | 2017 | |
dc.identifier | IEEE Transactions on Robotics, Volumen 33, Issue 1, 2017, Pages 198-213 | |
dc.identifier | 15523098 | |
dc.identifier | 10.1109/TRO.2016.2627027 | |
dc.identifier | https://repositorio.uchile.cl/handle/2250/168807 | |
dc.description.abstract | Inrobotic 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.language | en | |
dc.publisher | IEEE | |
dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Chile | |
dc.source | IEEE Transactions on Robotics | |
dc.subject | Map metric | |
dc.subject | Mobile robots | |
dc.subject | Simultaneous localization and mapping | |
dc.title | Metrics for Evaluating Feature-Based Mapping Performance | |
dc.type | Artículo de revista | |