Publications
• Sorted by Date • Classified by Publication Type • Classified by Research Category • Classified by Research Group • Classified by Author Last Name •
Thorsten Schmitt, Robert Hanek, Sebastian Buck, and Michael Beetz. Cooperative Probabilistic State Estimation for Vision-based Autonomous Mobile Robots. In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1630–1638, Maui, Hawaii, 2001.
Download
Abstract
With the services that autonomous robots are to provide becoming more demanding, the states that the robots have to estimate become more complex. In this paper, we develop and analyze a probabilistic, vision-based state estimation method for individual, autono-mous robots. This method enables a team of mobile robots to estimate their joint positions in a known environment and track the positions of autonomously moving objects. The state estimators of different robots cooperate to increase the accuracy and reliability of the estimation process. This cooperation between the robots enables them to track temporarily occluded objects and to faster recover their position after they have lost track of it. The method is empirically validated based on experiments with a team of physical robots.
BibTeX Entry
@inproceedings{Sch01Coo1,
author = "Thorsten Schmitt and Robert Hanek and Sebastian Buck and Michael Beetz",
title = {Cooperative Probabilistic State Estimation for Vision-based Autonomous Mobile Robots},
booktitle = {Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
address = "Maui, Hawaii",
pages = "1630--1638",
year = "2001",
bib2html_pubtype = {Refereed Conference Paper},
bib2html_rescat = {Plan-based Robot Control, State Estimation},
bib2html_groups = {IAS, AGILO},
bib2html_funding = {AGILO},
bib2html_keywords = {Robot, State Estimation Vision},
abstract = {With the services that autonomous robots are to provide becoming more demanding, the states that
the robots have to estimate become more complex. In this paper, we develop and analyze a
probabilistic, vision-based state estimation method for individual, autono-mous robots. This method
enables a team of mobile robots to estimate their joint positions in a known environment and track
the positions of autonomously moving objects. The state estimators of different robots cooperate to
increase the accuracy and reliability of the estimation process. This cooperation between the
robots enables them to track temporarily occluded objects and to faster recover their position
after they have lost track of it. The method is empirically validated based on experiments with a
team of physical robots.}
}
Generated by bib2html.pl (written by Patrick Riley ) on Wed Aug 20, 2008 14:00:40 UTC

















