Player Based Indoor Service Robot Map Building and Locating System

An indoor service robot map building and positioning system is built based on the open source platform Player.First,DP-SLAM algorithm is transplanted to the Player to build the dynamic maps offline,in order to reduce errors and constraints caused by manual map building.Then,the KLD-Sampling Adaptive Monte Carlo locating(KLD-AMCL) algorithm is introduced,which can adaptively adjust the required number of particles by calculating the MLE and real posterior KL distance.Finally,an indoor service robot position system is built by combine the Player platform,dynamic map building and KLD-AMCL algorithm.Empirical results show that the system has good environmental adaptability and high positioning accuracy.