2010
Grzonka, Slawomir; Dijoux, Frederic; Karwath, Andreas; Burgard, Wolfram
Learning Maps of Indoor Environments Based on Human Activity Conference
Embedded Reasoning, Papers from the 2010 AAAI Spring Symposium, 2010.
Abstract | Links | BibTeX | Tags: activity recognition, localization, machine learning, mobile systems and mobility, simultaneous localization and mapping, social robotics
@conference{grzonka2010a,
title = {Learning Maps of Indoor Environments Based on Human Activity},
author = {Slawomir Grzonka and Frederic Dijoux and Andreas Karwath and Wolfram Burgard},
url = {http://www.aaai.org/ocs/index.php/SSS/SSS10/paper/view/1172},
year = {2010},
date = {2010-03-23},
booktitle = {Embedded Reasoning, Papers from the 2010 AAAI Spring Symposium},
crossref = {DBLP:conf/aaaiss/2010-4},
abstract = {We present a novel approach to build approximate maps of structured environments utilizing human motion and activity. Our approach uses data recorded with a data suit which is equipped with several IMUs to detect movements of a person and door opening and closing events. In our approach we interpret the movements as motion constraints and door handling events as landmark detections in a graph-based SLAM framework. As we cannot distinguish between individual doors, we employ a multi-hypothesis approach on top of the SLAM system to deal with the high data-association uncertainty. As a result, our approach is able to accurately and robustly recover the trajectory of the person. We additionally take advantage of the fact that people traverse free space and that doors separate rooms to recover the geometric structure of the environment after the graph optimization. We evaluate our approach in several experiments carried out with different users and in environments of different types.},
keywords = {activity recognition, localization, machine learning, mobile systems and mobility, simultaneous localization and mapping, social robotics},
pubstate = {published},
tppubtype = {conference}
}
We present a novel approach to build approximate maps of structured environments utilizing human motion and activity. Our approach uses data recorded with a data suit which is equipped with several IMUs to detect movements of a person and door opening and closing events. In our approach we interpret the movements as motion constraints and door handling events as landmark detections in a graph-based SLAM framework. As we cannot distinguish between individual doors, we employ a multi-hypothesis approach on top of the SLAM system to deal with the high data-association uncertainty. As a result, our approach is able to accurately and robustly recover the trajectory of the person. We additionally take advantage of the fact that people traverse free space and that doors separate rooms to recover the geometric structure of the environment after the graph optimization. We evaluate our approach in several experiments carried out with different users and in environments of different types.