Title :
A Novel Particle Filter Method for Mobile Robot Localization
Author :
Yin, Bo ; Wei, Zhiqiang ; Cong, Yanping ; Xu, Tao
Author_Institution :
Dept. of Comput. Sci., Ocean Univ. of China, Qingdao, China
Abstract :
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.
Keywords :
Monte Carlo methods; mobile robots; particle filtering (numerical methods); path planning; regression analysis; support vector machines; EKF proposal distribution; mobile robot localization; particle filter method; posterior density; reweighting scheme; sequential Monte Carlo framework; state evolution; support vector regression; Marine technology; Mobile robots; Monte Carlo methods; Motion measurement; Particle filters; Particle measurements; Proposals; Robot localization; Sea measurements; State estimation; EKF; SVR; particle filter; sample impovrishment;
Conference_Titel :
Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on
Conference_Location :
Changsha City
Print_ISBN :
978-1-4244-5001-5
Electronic_ISBN :
978-1-4244-5739-7
DOI :
10.1109/ICMTMA.2010.32
