G. Cielniak, M. Bennewitz and W. Burgard
Where is... ? Learning and Utilizing Motion Patterns of Persons with Mobile Robots
In Proc. of the International Joint Conference on Artificial Intelligence(IJCAI), 2003.
Abstract
Whenever people move through their environments they do not move
randomly. Instead, they usually follow specific trajectories or
motion patterns corresponding to their intentions. Knowledge about
such patterns may enable a mobile robot to robustly keep track of
persons in its environment or to improve its obstacle avoidance
behavior. This paper proposes a technique for learning collections
of trajectories that characterize typical motion patterns of
persons. Data recorded with laser-range finders is clustered using
the expectation maximization algorithm. Based on the result of the
clustering process we derive a Hidden Markov Model (HMM). This HMM
is able to estimate the current and future positions of multiple
persons given knowledge about their intentions. Experimental
results obtained with a mobile robot using laser and vision data
collected in a typical office building with several persons
illustrate the reliability and robustness of the approach. We also
demonstrate that our model provides better estimates than an HMM
directly learned from the data.
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Bibtex
@InProceedings{czielniak03ijcai,
author = {Czielniak, G. and Bennewitz, M. and Burgard, W.},
title = {Where is ...? Learning and Utilizing Motion Patterns of Persons with Mobile Robots},
booktitle = {Proc. of the International Conference on Artificial Intelligence (IJCAI)},
year = {2003},
}