Cooperative Probabilistic State Estimation for Vision-based Autonomous Mobile Robots (bibtex)

by Thorsten Schmitt, Robert Hanek, Michael Beetz, Sebastian Buck and Bernd Radig

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 article, we develop and analyze a probabilistic, vision-based state estimation method for individual, autonomous 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 tate 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.

Reference:

Thorsten Schmitt, Robert Hanek, Michael Beetz, Sebastian Buck and Bernd Radig, "Cooperative Probabilistic State Estimation for Vision-based Autonomous Mobile Robots", In IEEE Transactions on Robotics and Automation, vol. 18, no. 5, 2002.

Bibtex Entry:

@Article{Sch02Coo,
  author  = "Thorsten Schmitt and Robert Hanek and Michael Beetz and Sebastian Buck and Bernd Radig",
  title   = "Cooperative Probabilistic State Estimation for Vision-based Autonomous Mobile Robots",
  journal = "IEEE Transactions on Robotics and Automation",
  year    = "2002",
  month   = "October",
  volume  = "18",
  number  = "5",
  bib2html_pubtype  = {Journal},
  bib2html_rescat   = {State Estimation, Robot Vision, RoboCup},
  bib2html_groups   = {AGILO},
  bib2html_funding  = {AGILO},
  bib2html_keywords = {Robot, Vision, State Estimation},
  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 article, we develop and analyze a probabilistic, vision-based state estimation method for individual, autonomous 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 tate 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.}
}