A Novel Particle Filter Method for Mobile Robot Localization

Particle filter is a powerful tool for mobile robot localization based on Sequential Monte Carlo framework. However, it needs a large number of samples to properly approximate the posterior density of the state evolution, which makes it computational expensive. In this paper, an improved particle filter is proposed by adopting an EKF proposal distribution and Support Vector Regression (SVR). The proposed particle filter uses an EKF proposal to provide good quality samples, and an SVR based re-weighting scheme to re-weight the sample more accurately. Thus the effectiveness and diversity of samples are maintained meanwhile impoverishment is avoided as much as possible. Experiment results show that the proposed particle filter can work with a small sample set effectively and is more precise for mobile robot localization than classical particle filter.