Learning Navigational Maps by Observing Human Motion Patterns

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
IEEE International Conference on Robotics and Automation (ICRA'11), 2011, pp. 4333 - 4340
Issue Date:
2011-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2010005261.pdf2.03 MB
Adobe PDF
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.
Please use this identifier to cite or link to this item: