关键词: 同时定位与地图创建, 粒子滤波器, Unscented卡尔曼滤波器, 迭代扩展卡尔曼滤波器

Abstract: FastSLAM algorithm is an important kind of method for SLAM. UPF-IEKF 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 UPF-IEKF 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.

Key words: simultaneous localization and map building, particle filter, Unscented Kalman filter, iterated extended Kalman filter