摘要
粒子滤波器能够给出移动机器人全局定位非线性非高斯模型的近似解.然而,当新感知出现在先验概率的尾部或者与先验相比感知概率太尖时,传统的粒子滤波器会退化导致定位失败.本文提出了一种重要性采样跟中心差分滤波器(cen tra l d ifference filter,CDF)相结合的新算法,并对测量更新步的加权粒子集应用基于KD-树的加权期望最大(w e igh ted expecta tion m ax im iza tion,W EM)自适应聚类算法获得表示机器人位姿状态后验密度的高斯混合模型(G au ssian m ixtu re m od e l,GMM).实验结果表明,新方法提高了定位准确率,降低了计算复杂度.
The particle filter can give the approximate solutions to the non-linear non-Gaussian model of mobile robot global localization.However,if the new measurements appear in the tail of the prior or if the likelihood is too peaked in comparison to the prior,the conventional particle filter can degenerate and make localization fail.We present a novel algorithm that combines an importance sampling with central difference filter(CDF).The posterior pose state density is represented by Gaussian mixture model(GMM)that is recovered from the weighted particle set of the measurement update step by means of a weighted expectation maximization(WEM)adaptive clustering algorithm,which based on the kd-trees.Experimental results show that this new approach has an improved localization accuracy and reduceds computational complexity.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2006年第3期553-558,共6页
Acta Electronica Sinica
基金
国家自然科学基金(No.2002AA735041)
关键词
移动机器人
全局定位
粒子滤波器
中心差分滤波器
加权期望最大
高斯混合模型
mobile robot
global localization
particle filters
central difference filter(CDF)
weighted expecta-tion maximization(WEM)
Gaussian mixture model(GMM)
