Relative Localization of Mobile Robots Based on Bayesian Theory
Chen Yuqing, Ying Hu, Zi Ma · 2006
This paper investigates the relative localization problem of multiple mobile robots under the unknown circumstance. The localization rules between robots are analyzed based on Bayesian theory, which satisfies the Markov assumption. Then the estimating equations of the robot's the states and covariances are deduced from the odometry's model of noholonomic robots, also the relative observation equations between robots are constructed. So the estimated values of the states and covariances can be updated by the rules of distributed extended Kalman filter. Experiment results have demonstrated the validity of the proposed approach for a group of robots.