Inferring door locations from a teammate's trajectory in stealth human-robot team operations
Jean Oh, Luis Navarro-Serment, Arne Suppé, Anthony Stentz, Martial Hebert
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.
BibTeX
@inproceedings{iros2015_inferringdoorloc,
title = {Inferring door locations from a teammate's trajectory in stealth human-robot team operations},
author = {Jean Oh and Luis Navarro-Serment and Arne Suppé and Anthony Stentz and Martial Hebert},
booktitle = {IROS 2015},
year = {2015}
}