SLAM with SC-PHD filters: an underwater vehicle application
dc.contributor.author
dc.date.accessioned
2015-03-25T12:22:59Z
dc.date.available
2015-03-25T12:22:59Z
dc.date.issued
2014
dc.identifier.issn
1070-9932
dc.identifier.uri
dc.description.abstract
The random finite-set formulation for multiobject estimation provides a means of estimating the number of objects in cluttered environments with missed detections within a unified probabilistic framework. This methodology is now becoming the dominant mathematical framework within the sensor fusion community for developing multiple-target tracking algorithms. These techniques are also gaining traction in the field of feature-based simultaneous localization and mapping (SLAM) for mobile robotics. Here, we present one such instance of this approach with an underwater vehicle using a hierarchical multiobject estimation method for estimating both landmarks and vehicle position
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.isformatof
Reproducció digital del document publicat a: http://dx.doi.org/10.1109/MRA.2014.2310132
dc.relation.ispartof
© IEEE Robotics and Automation Magazine, 2014, vol. 21, p. 38-45
dc.relation.ispartofseries
Articles publicats (D-ATC)
dc.rights
Tots els drets reservats
dc.subject
dc.title
SLAM with SC-PHD filters: an underwater vehicle application
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/embargoedAccess
dc.embargo.terms
Cap
dc.date.embargoEndDate
info:eu-repo/date/embargoEnd/2026-01-01
dc.relation.projectID
info:eu-repo/grantAgreement/EC/FP7/288273/EU/Persistent Autonomy through Learning, Adaptation, Observation and Re-planning/PANDORA
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.relation.FundingProgramme
dc.relation.ProjectAcronym