Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

10-2015

Abstract

Robot perception is generally viewed as the interpretation of data from various types of sensors such as cameras. In this paper, we study indirect perception where a robot can perceive new information by making inferences from non-visual observations of human teammates. As a proof-of-concept study, we specifically focus on a door detection problem in a stealth mission setting where a team operation must not be exposed to the visibility of the team's opponents. We use a special type of the Noisy-OR model known as BN2O model of Bayesian inference network to represent the inter-visibility and to infer the locations of the doors, i.e., potential locations of the opponents. Experimental results on both synthetic data and real person tracking data achieve an F-measure of over .9 on average, suggesting further investigation on the use of non-visual perception in human-robot team operations.

Discipline

Artificial Intelligence and Robotics

Research Areas

Information Systems and Management

Publication

Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems, Hamburg, Germany, 2015 September 28 - October 2

First Page

5315

Last Page

5320

ISBN

9781479999941

Identifier

10.1109/IROS.2015.7354127

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/IROS.2015.7354127

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