A SLAM algorithm of fused EKF and Particle filter

Abstract

Aiming at the problems of low SLAM precision, poor localization effect and obvious cumulative error of mobile robot based on the Extended Kalman filtering algorithm. This article proposes a mobile robot SLAM algorithm which fusion EKF and particle filter. In this method, the particle filter algorithm is used to calculate the positioning problem of mobile robot and uses EKF algorithm to estimate the location of environment, which reduce the computational complexity and has better robustness. The type of noise that is not limited to the environment, the desired result can be obtained in the mobile robot SLAM. The error range decreased from 0.5m to 0.2m, and the positioning effect was significantly improved. The experimental results show that the SLAM algorithm of mobile robot fused EKF and particle filter is more accurate than the Extended Kalman filter.