Learning Navigational Maps by Observing Human Motion Patterns

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dc.contributor.author O'Callaghan, ST
dc.contributor.author Singh, SP
dc.contributor.author Alempijevic, A
dc.contributor.author Ramos, FT
dc.contributor.editor Bicchi, A
dc.date.accessioned 2012-10-12T03:36:22Z
dc.date.issued 2011-01
dc.identifier.citation IEEE International Conference on Robotics and Automation (ICRA'11), 2011, pp. 4333 - 4340
dc.identifier.isbn 978-1-61284-386-5
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/19212
dc.description.abstract AbstractObserving human motion patterns is informative for social robots that share the environment with people. This paper presents a methodology to allow a robot to navigate in a complex environment by observing pedestrian positional traces. A continuous probabilistic function is determined using Gaussian process learning and used to infer the direction a robot should take in different parts of the environment. The approach learns and filters noise in the data producing a smooth underlying function that yields more natural movements. Our method combines prior conventional planning strategies with most probable trajectories followed by people in a principled statistical manner, and adapts itself online as more observations become available. The use of learning methods are automatic and require minimal tuning as compared to potential fields or spline function regression. This approach is demonstrated testing in cluttered office and open forum environments using laser and vision sensing modalities. It yields paths that are similar to the expected human behaviour without any a priori knowledge of the environment or explicit programming.
dc.format Ryan Stoker
dc.publisher IEEE
dc.relation.hasversion Accepted manuscript version en_US
dc.relation.isbasedon 10.1109/ICRA.2011.5980478
dc.title Learning Navigational Maps by Observing Human Motion Patterns
dc.type Conference Proceeding
dc.parent IEEE International Conference on Robotics and Automation (ICRA'11)
dc.journal.number en_US
dc.publocation China en_US
dc.publocation China
dc.publocation China
dc.publocation China
dc.publocation China
dc.identifier.startpage 4333 en_US
dc.identifier.endpage 4340 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.conference Robotics and Automation (ICRA), 2011 IEEE International Conference on
dc.conference Robotics and Automation (ICRA), 2011 IEEE International Conference on
dc.conference Robotics and Automation (ICRA), 2011 IEEE International Conference on
dc.conference Robotics and Automation (ICRA), 2011 IEEE International Conference on
dc.for 0801 Artificial Intelligence and Image Processing
dc.personcode 996745
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom Robotics and Automation (ICRA), 2011 IEEE International Conference on en_US
dc.date.activity 20110509 en_US
dc.date.activity 2011-05-09
dc.date.activity 2011-05-09
dc.date.activity 2011-05-09
dc.date.activity 2011-05-09
dc.location.activity Shanghai, China en_US
dc.location.activity Shanghai, China
dc.location.activity Shanghai, China
dc.location.activity Shanghai, China
dc.location.activity Shanghai, China
dc.description.keywords Gaussian processes , Humans , Navigation , Robot sensing systems , Trajectory , Uncertainty en_US
dc.description.keywords Gaussian processes , Humans , Navigation , Robot sensing systems , Trajectory , Uncertainty
dc.description.keywords Gaussian processes , Humans , Navigation , Robot sensing systems , Trajectory , Uncertainty
dc.description.keywords Gaussian processes , Humans , Navigation , Robot sensing systems , Trajectory , Uncertainty
dc.description.keywords Gaussian processes , Humans , Navigation , Robot sensing systems , Trajectory , Uncertainty
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Elec, Mech and Mechatronic Systems


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