Fast large-scale SLAM with improved accuracy in mobile robot

Abstract

A Fast Map Joining algorithm (FMJ) is proposed in this paper to achieve the Simultaneous Localization and Mapping (SLAM) of mobile robot in the large-scale environments. The proposed algorithm can efficiently improve the accuracy of the SLAM and reduce the computational load compared with the standard extended Kalman filter (EKF) SLAM. The FMJ SLAM algorithm divides the global map into a sequence of local sub-maps whose sizes are determined according to the density of the features in the environment. The final localization and mapping is achieved once the sub-maps are jointed accordingly. Simulations are conducted to validate the proposed technique.