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移动机器人的改进无迹粒子滤波蒙特卡罗定位算法
Mobile Robot Monte Carlo Localization Based on Improved Unscented Particle Filter
【摘要】 粒子滤波是移动机器人蒙特卡罗定位(Monte Carlo localization,MCL)的核心环节.首先,针对粒子滤波过程的粒子退化问题,利用迭代Sigma点卡尔曼滤波来精确设计粒子滤波器的提议分布,以迭代更新方式将当前观测信息融入顺序重要性采样过程,提出IUPF(Improved unscented particle filter)算法.然后,将IUPF与移动机器人MCL相结合,给出IUPF-MCL定位算法的实现细节.仿真结果表明,IUPF-MCL是一种精确鲁棒的移动机器人定位算法.
【Abstract】 Particle filter is a key issue in mobile robot Monte Carlo location(MCL).Firstly,improved unscented particle filter(IUPF) algorithm is proposed in this paper.To overcome particles degeneracy phenomenon,the algorithm utilizes iterated sigma points Kalman filter to generate more accurate proposal distribution,which introduces most recent measurement information into sequential importance sampling(SIS) routine through iterated update processing.Secondly,by applying IUPF to MCL,IUPF-MCL algorithm is given.Finally,simulation results show that IUPF-MCL is an accurate and robust mobile robot localization algorithm.
【Key words】 Mobile robot; Monte Carlo localization(MCL); particle filter; unscented Kalman filter;
- 【文献出处】 自动化学报 ,Acta Automatica Sinica , 编辑部邮箱 ,2010年06期
- 【分类号】TP242
- 【被引频次】81
- 【下载频次】1107