An Improved Rao-Blackwellized Particle Filter for SLAM

Simultaneous localization and map building (SLAM) is one of the fundamental problems in robot navigation, and FastSLAM algorithms based on Rao-Blackwellized particle filters (RBPF) have become popular tools to solve the SLAM problems. For solving the potential limitations, which are the derivation of the Jacobian matrices, and particles impoverishment in SLAM algorithms, this paper proposes an improved algorithm based on unscented Kalman filter (UKF) for landmark feature estimate and particles resampling strategy to overcome the above- mentioned drawbacks. Experimental results demonstrate the effectiveness of the proposed algorithm.