Slawomir Grzonka, Andreas Karwath, Frederic Dijoux, and Wolfram Burgard
Activity-based Estimation of Human Trajectories
We present a novel approach to incrementally determine
the trajectory of a person in 3D based on its motions
and activities in real-time. In our algorithm, we estimate the
motions and activities of the user given the data obtained from a
motion capture suit equipped with several inertial measurement
units (IMUs). These activities include walking up and down
staircases as well as opening and closing doors. We interpret
the first two types of activities as motion constraints and door
handling events as landmark detections in a graph-based simultaneous
localization and mapping (SLAM) framework. Since we
cannot distinguish between individual doors, we employ a multihypothesis
tracking approach on top of the SLAM procedure
to deal with the high data-association uncertainty. As a result,
we are able to accurately and robustly recover the trajectory
of the person. Additionally we present an algorithm to build
approximate geometrical and the topological maps based on the
estimated trajectory and detected activities. We evaluate our
approach in practical experiments carried out with different
subjects and in various environments.
Bibtex:
@article{grzonka12tro_mvn,
author = {Grzonka, S. and Karwath, A. and Dijoux, F. and Burgard, W.},
title = {{Activity-based Estimation of Human Trajectories}},
journal = IEEE Transactions on Robotics (T-RO),
number = {1},
month = {2},
volume = {8},
year = {2012},
pages = {234--245}
}
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