摘要
The research of robot localization aims at accuracy, simplicity and robustness. This article improves the performance of particle filters in robot localization via the utilization of novel adaptive technique. The proposed algorithm introduces probability retracing to initialize particle sets, uses consecutive window filtering to update particle sets, and refreshes the size of particle set according to the estimation state. Extensive simulations show that the proposed algorithm is much more effective than the traditional particle filters. The proposed algorithm successfully solves the nonlinear, non-Gaussian state estimation problem of robot localization.
The research of robot localization aims at accuracy, simplicity and robustness. This article improves the performance of particle filters in robot localization via the utilization of novel adaptive technique. The proposed algorithm introduces probability retracing to initialize particle sets, uses consecutive window filtering to update particle sets, and refreshes the size of particle set according to the estimation state. Extensive simulations show that the proposed algorithm is much more effective than the traditional particle filters. The proposed algorithm successfully solves the nonlinear, non-Gaussian state estimation problem of robot localization.
出处
《自动化学报》
EI
CSCD
北大核心
2005年第6期833-838,共6页
Acta Automatica Sinica
基金
国家自然科学基金
