Learning Navigational Maps by Observing Human Motion Patterns. O'Callaghan, S. T., Singh, S. P. N., Alempijevic, A., & Ramos, F. 2011.
doi  abstract   bibtex   
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.
@CONFERENCE{icra11.simon,
  author = {S. T. O'Callaghan and S. P. N. Singh and A. Alempijevic and F. Ramos},
  title = {Learning Navigational Maps by Observing Human Motion Patterns},
  booktitle = {International Conference on Robotics and Automation},
  year = {2011},
  pages = {4333-4340},
  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.},
  doi = {10.1109/ICRA.2011.5980478},
  fauthor = {O'Callaghan, Simon Timothy and Surya P. N. Singh and Alempijevic,
	Alen and Ramos, Fabio},
  pdf = {ICRA2011.1885.pdf}
}

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