Fast computation of look-ahead Rao-Blackwellised Particle Filter in SLAM

In this paper, we explore a novel strategy for fast computation of the look-ahead Rao-Blackwellised Particle Filtering (la-RBPF) algorithm for the simultaneous localization and mappping (SLAM) problem in the probabilistic robotics framework. We show that the complexity of the existing algorithm can be substantially reduced by computing for the Kalman filtering prediction and update steps to only a representative particle of a group of particles offering the same robot´s poses. Simulation results reveal the potential of the proposed method in reducing the computational time steps as compared to the original la-RBPF algorithm without affecting the performance. The test results also show its superior estimation accuracy as compared to the standard RBPF SLAM algorithm when the number of particles is small.