M. Bennewitz, W. Burgard, and S. Thrun
Learning Motion Patterns of Persons for Mobile Service Robots
In Proc. of the VDI-Conference Robotik 2002 (Robotik),
2002.
Abstract
We propose a method for learning models of people's motion behaviors
in an indoor environment. As people move through their
environments, they do not move randomly. Instead, they often engage
in typical motion patterns, related to specific locations that they
might be interested in approaching and specific trajectories that
they might follow in doing so. Knowledge about such patterns may
enable a mobile robot to develop improved people following and
obstacle avoidance skills. This paper proposes an algorithm that
learns collections typical trajectories that characterize a person's
motion patterns. Data, recorded by mobile robots equipped with laser
range finders, is clustered into different types of motion using the
popular expectation maximization algorithm, while simultaneously
learning multiple motion patterns. Experimental results, obtained
using data collected in a domestic residence, illustrate that highly
predictive models of human motion patterns can be learned.
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Bibtex
@InProceedings{Bennewitz02Learning1,
author = {Bennewitz, M. and Burgard,
W. and Thrun, S.},
title = {Learning Motion Patterns of Persons for Mobile Service Robots},
booktitle = {Proc.~of the VDI-Conference Robotik 2002 (Robotik)},
year = {2002}
}