A RBPF Algorithm with Non-intact Particle Data for FastSLAM

Abstract

RBPF (Rao-Blackwellized Particle Filter) is a popular PF (Particle Filter) in decreasing the dimension of estimation problems and FastSLAM (Fast Simultaneous Localization and Mapping) is a RBPF-based algorithm. In FastSLAM, each particle carries a large amount of data which results in low computing efficiency and large memory space occupancy. To solve this problem, a RBPF algorithm with non-intact particle data is studied. The key idea is to differentiate the particle data. Through the screening of particles, the number of particles carrying individual map data is limited to reduce the data occupied space and speed up the computational efficiency. The simulation and experiment results have verified the effectiveness and accuracy of the algorithm. Compared with the original one, this proposed algorithm reduces time consumption by 18%-34% and considerably saves memory space.