An Improved Particle Filter SLAM Algorithm for AGVs

Aiming at the problems of the traditional Particle Filter (PF) algorithm used in robot localization technology, for example, large computational expense, poor real-time performance, and limited positioning accuracy, an IPF-SLAM (Improved Particle Filtering SLAM Simultaneous Localization and Mapping) algorithm is proposed to tackle these difficulties. First, an interactive multi-model extended Kalman filter is used to provide a proposed distribution for particle filtering. The degree of fitting of Kalman filtering to a nonlinear system is improved by the multi-models, so that the filtering result is closer to the true value. Then, the “number of effective particles” is employed to determine the resampling timing and reduce the number of resampling. A Gaussian distribution function is introduced to randomly generate replicated particles to alleviate particle degradation. The simulation results show that the location error of IPF-SLAM algorithm is 17.26% lower than that of RBPF-SLAM (Rao-Blackwellise Particle Filter-Simultaneous Localization and Mapping) algorithm, and the calculation time is 5.7% lower. The experimental results show that the traditional algorithm is significantly improved in reducing computational complexity, improving positioning accuracy and robustness, etc. Therefore, the IPF-SLAM has a more significant positioning and mapping effects, compared with the traditional RBPF-SLAM algorithm.