A Simultaneous Localization and Mapping Algorithm of Mobile Robot Based on Improved FastSLAM

This paper takes the Simultaneous Localization and Mapping (SLAM) problem of mobile robot as research object and improves the FastSLAM algorithm. As the estimation precision of Extended Kalman Filter (EKF) is low, we adopt Unscented Kalman Filter (UKF) to approach the posterior distribution instead of EKF, at the same time use UKF to estimate the landmark position. We adopt adaptive resample method which resamples when needed by choosing suitable standard to reduce the depletion of samples. Theory analysis and simulation results prove that the improved algorithm can enhance the performance of SLAM effectively.