Activity-Based Estimation of Human Trajectories

Slawomir Grzonka, Andreas Karwath, Frederic Dijoux, Wolfram Burgard

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

Abstract

We present a novel approach to incrementally determine the trajectory of a person in 3-D based on its motions and activities in real time. In our algorithm, we estimate the motions and activities of the user given the data that are obtained from a motion capture suit equipped with several inertial measurement units. 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 topological maps based on the estimated trajectory and detected activities. We evaluate our approach in practical experiments that are carried out with different subjects and in various environments.
Original languageEnglish
Pages (from-to)234-245
Number of pages12
JournalIEEE Transactions on Robotics
Volume28
Issue number1
DOIs
Publication statusPublished - 2012

Keywords

  • activity recognition, machine learning, simultaneous localization and mapping

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