Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/87308
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dc.contributor.authorBibby, C.en
dc.contributor.authorReid, I.en
dc.date.issued2010en
dc.identifier.citation2010 IEEE International Conference on Robotics and Automation (ICRA), 2010 / pp.257-264en
dc.identifier.isbn9781424450404en
dc.identifier.issn1050-4729en
dc.identifier.urihttp://hdl.handle.net/2440/87308-
dc.description.abstractWe present a hybrid SLAM system for marine environments that combines cubic splines to represent the trajectories of dynamic objects, point features to represent stationary objects and an occupancy grid to represent land masses. This hybrid representation enables SLAM to be applied in environments with moving objects, where solutions using point features alone are computationally prohibitive or where dense objects e.g. landmasses can not be represented correctly using point features. Estimation is achieved using a sliding window framework with reversible data-association and reversible model-selection. Our main contributions are: (i) a hybrid representation of the environment; (ii) occupancy grid fusion is continually refined for the duration of the sliding window; (iii) the trajectories of dynamic objects are represented using cubic splines and (iv) radar scans are re-rendered at a sub-scan resolution to compensate for the egomotion during the scan acquisition period. We show that the continual refinement of the occupancy grid greatly improves the quality of the resultant map, leading to a better estimate of the egomotion and therefore better estimates of the trajectories of dynamic objects. We also demonstrate that the use of cubic splines to represent trajectories has two major advantages: (i) the state space is compressed i.e. many vehicle poses can be represented using a single spline section and (ii) the trajectory becomes continuous and so fusing information from asynchronous sensors running at multiple frequencies becomes trivial. The efficacy of our system is demonstrated using real marine radar data, showing that it can successfully estimate the positions/velocities of objects and landmasses observed during a typical voyage on a small boat.en
dc.description.statementofresponsibilityCharles Bibby, Ian Reiden
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseriesIEEE International Conference on Robotics and Automation ICRAen
dc.rights©2010 IEEEen
dc.titleA hybrid SLAM representation for dynamic marine environmentsen
dc.typeConference paperen
dc.identifier.rmid0020131213en
dc.contributor.conferenceIEEE International Conference on Robotics and Automation (ICRA) (03 May 2010 - 07 May 2010 : Anchorage, AK)en
dc.identifier.doi10.1109/ROBOT.2010.5509262en
dc.publisher.placeUSAen
dc.identifier.pubid18417-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS02en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidReid, I. [0000-0001-7790-6423]en
Appears in Collections:Computer Science publications

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