DocumentCode :
2702793
Title :
Learning navigational maps by observing human motion patterns
Author :
Callaghan, Simon T O ; Singh, Surya P N ; Alempijevic, Alen ; Ramos, Fabio T.
Author_Institution :
Australian Centre for Field Robot., Univ. of Sydney, Sydney, NSW, Australia
fYear :
2011
fDate :
9-13 May 2011
Firstpage :
4333
Lastpage :
4340
Abstract :
Observing 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.
Keywords :
Gaussian processes; human-robot interaction; motion estimation; path planning; probability; regression analysis; robot vision; splines (mathematics); Gaussian process learning; cluttered office; continuous probabilistic function; human motion patterns; laser sensing modalities; navigational maps; noise filtering; open forum environments; pedestrian positional traces; planning strategies; social robots; spline function regression; statistical method; vision sensing modalities; Gaussian processes; Humans; Navigation; Robot sensing systems; Trajectory; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation (ICRA), 2011 IEEE International Conference on
Conference_Location :
Shanghai
ISSN :
1050-4729
Print_ISBN :
978-1-61284-386-5
Type :
conf
DOI :
10.1109/ICRA.2011.5980478
Filename :
5980478
Link To Document :
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