A new cooperative localization algorithm based on maximum entropy gaming

Abstract

The problem of cooperative localization in the situation when an object is detected by robots simultaneously was studied. As each robot has its own relative observation about the object,a mathematical model for comparing the consistency of these relative observations was presented. With that method,a new cooperative localization algorithm based on maximum entropy gaming and Extended Kalman Filter( EKF) was proposed. As the gaming results are different,the EKF equations that can match any gaming result were derived. Several simulation results showing that the proposed algorithm can improve the localization performance and avoid the relative observations conflict problem in cooperative localization in the meantime.