A new filter framework for FastSLAM algorithm
Chunxia Zhao · Jisuanji kexue yu tansuo · 2008
FastSLAM algorithm is an important kind of method for SLAM. FastSLAM 2.0 estimates the robot path with UPF, and the map with IEKF. UPF makes the particles move towards the area of high posterior likelihood. Therefore, UPF can improve estimation accuracy to some extent, and the computational effort will decrease greatly for the reason that UPF needs fewer particles than general particle filter. IEKF improves estimation accuracy with the iteration of observation update. Simulation results indicate that the cumulative time of map building for FastSLAM 2.0 is shorter than that for UPF-UKF FastSLAM 2.0 when iteration number is equal or less than two, and it performs better than UPF-UKF FastSLAM 2.0 in estimation accuracy when the iteration number equals two. Taking into account of both estimation accuracy and computational effort, UPF-IEKF is regarded as a much more reasonable framework for FastSLAM algorithm.