A new algorithm merging static game with complete information into EKF for multi-robot cooperative localization

Abstract

For recognizing and eliminating the conflicting observations of multi-robot cooperative localization, while improving its consistency and effectiveness, a new cooperative localization algorithm which merged static game with complete information into EKF(extended Kalman filter) was proposed. The proposed algorithm uses the game theory to check the relative observations. After eliminating the conflicting relative observations, cooperative localization can be more effective. Since relative observations can be classified into two types, i.e one-sided relative observations and bidirectional relative observations, two sets of EKF cooperative localization formulations were respectively deduced. The simulation results show that the proposed algorithm makes the robot team only share coherent relative observations between them. It ensures the improvement of localization accuracy of every robot and reduces the computational complexity at the same time.