by T Schmitt, R Hanek, M Beetz, S Buck and B 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:
Cooperative Probabilistic State Estimation for Vision-based Autonomous Mobile Robots (T Schmitt, R Hanek, M Beetz, S Buck and B Radig), In IEEE Transactions on Robotics and Automation, volume 18, 2002.
Bibtex Entry:
@article{schmitt_cooperative_2002,
author = {T Schmitt and R Hanek and M Beetz and S Buck and B Radig},
title = {Cooperative Probabilistic State Estimation for Vision-based Autonomous
Mobile Robots},
journal = {{IEEE} Transactions on Robotics and Automation},
year = {2002},
volume = {18},
number = {5},
month = {oct},
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.},
}