提出一种基于改进的鲁棒机器人蒙特卡罗定位(Improved Robust Robot Monte Carlo localization
IRR-MCL)算法.首先利用扩展卡尔曼滤波来精确设计粒子滤波器的提议分布
将当前观测信息融入顺序重要性采样过程
以改善滤波效果
减小所需粒子数;然后
给出IRR-MCL定位算法的实现细节
实验结果表明
该算法与传统的方法在定位精度和鲁棒性方面都有显著提高.
Abstract
This paper puts forward a kind of algorithm based on Improved Robust Robot Monte Carlo Localization(IRR-MCL)
with the view of researching the particle degeneracy phenomenon and improving the particle refining performance in the process of the particle filter.Firstly
distribute using the proposal about accurately designing a particle filter by the extended Kalman filter and blend the current observational information into the sampling process of progression importance in order to improve the filtering effect and reduce the required particles.Then present the implementation details of IRR-MCL algorithm.Experimental results show that this algorithm
compared with the traditional methods
has improved significantly in terms of localizing accuracy and robustness.