Towards a Context Enhanced Framework for Multi Object Tracking in Human Robot Collaboration
Sharath Chandra Akkaladevi, Matthias Plasch, Christian Eitzinger, Andreas Pichler, Bernhard Rinner
Abstract
In a goal-oriented Human Robot Collaborative (HRC) scenario, where the goal is to complete an assembly process, a robust object tracker might not necessarily fulfill its functional role due to the dynamic nature of HRC. Moreover, for an efficient HRC, the functional role of the object tacker should not only be limited to localizing and tracking objects for robotic manipulation. It should also help to determine the current state of the assembly process and verify if the chosen action has been successfully performed and thus to enable an uninterrupted completion of an HRC assembly process. We present a Context Enhanced Framework for Multi Object Tracking, that i) allows uninterrupted completion of an assembly process, ii) improves the overall functional accuracy of the object tracker from 49 percent to 96 percent, and iii) enables the object tracker to handle multiple instance of multiple objects in a HRC setting.
BibTeX
@inproceedings{iros2018_towardsacontexte,
title = {Towards a Context Enhanced Framework for Multi Object Tracking in Human Robot Collaboration},
author = {Sharath Chandra Akkaladevi and Matthias Plasch and Christian Eitzinger and Andreas Pichler and Bernhard Rinner},
booktitle = {IROS 2018},
year = {2018}
}