@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.}
}