Distributed Entropy Based Relative Observation Selection for Multi-Robot Localization

摘要

We propose a novel distributed entropy-based measurement selection method for multi-robot localization.In multi-robot cooperative localization based on relative measurements,all the measurements obtained by a robot at one moment are fused to update pose estimation and covariance matrix.As the number of robots and measurements increase,the computation cost increase fast,then influencing the real-time and efficiency of localization.In order to reduce the computational burden and keep real-time localization,those measurements,which yield the most information gain in estimating robots location,are selected from all the measurements obtained by the robot group to update the whole group pose estimation and the covariance matrix.It ensures the necessary localization accuracy and meantime reduces the computational burden,so as to improve the reliability and real-time of localization.We compare the localization accuracy and the computation time by using different number of measurements.Simulation results show that the proposed method can effectively improve the efficiency in dealing with multi-robot localization,especially when the group is large.