基于自适应渐消UKF的FastSLAM算法

doi:10.11896/jsjkx.190700186

摘要/Abstract

摘要： 同时定位与地图构建(Simultaneous Lolalization And Mapping,SLAM)是未知环境下实现机器人自主导航的主要方法,FastSLAM是一个著名的SLAM问题解决方法。由于FastSLAM使用序贯重要性采样的方法,随着算法迭代计算,大部分粒子的权重值变得很小,只有很少粒子具有较大的权重,算法发生退化。为了使采样的粒子分布更加精确,避免粒子出现退化情况,从而进一步提高FastSLAM算法的估计精度,提出了一种基于自适应渐消无迹卡尔曼滤波(AFUKF)的快速同步定位和地图创建(FastSLAM)算法。针对FastSLAM的粒子退化问题,从研究粒子的建议分布函数出发,采用渐消无迹卡尔曼滤波(Adaptive Fading Unscented Kalman Filter,AFUKF)代替扩展卡尔曼滤波器(Extended Kalman Filter,EKF)来估计机器人位姿的建议分布函数,避免了EKF的线性化误差。同时,利用自适应渐消滤波思想产生一种参数可自适应调节的建议分布函数,使其更接近移动机器人的后验位姿概率分布,减缓粒子集的退化。在MATLAB平台上的仿真实验结果表明,所提方法的位置估计均方误差比标准FastSLAM降低了28.7%,即估计精度提升了28.7%。在与近几年相关算法的对比实验中,所提方法也取得了较高的估计精度。改变粒子数量条件进行实验时,随着粒子数量的增加,各算法的估计精度都在提升,所提算法依然取得了最好的估计精度。实验结果充分说明,提出的算法计算建议分布函数更加精确,有效缓解了FastSLAM算法中的粒子退化问题,从而显著提高了算法的估计精度。

关键词: 机器人, 建议分布函数, 粒子退化, 同步定位与地图构建, 自适应渐消无迹卡尔曼滤波

Abstract: Simultaneous localization and mapping(SLAM) is the main method to realize autonomous navigation of robots in unknown environments and FastSLAM algorithm is a popular solution to SLAM problem.Due to the sequential importance sampling method used in FastSLAM,a few of particles have a larger weight while the weight of most particles becomes very small throughout the iterative process,which leads to particle degradation.In order to make the particle distribution more accurate and reduce the particle degradation,a FastSLAM algorithm based on adaptive fading unscented Kalman filter (AFUKF) is proposed to improve the estimation accuracy of FastSLAM algorithm.To overcome the problem of particle degradation in FastSLAM,starting from the study of particle’s proposal distribution function,this paper uses adaptive fading unscented Kalman filter (AFUKF) instead of EKF to estimate the proposed distribution function of robot’s position to avoid the linearization error of EKF.With using the idea of adaptive fading filter,the proposal distribution is closer to the posterior position of the mobile robot and the particle set degradation is relieved.The simulation results on MATLAB platform show that the mean square error of position estimation of the proposed method is 28.7% lower than that of standard FastSLAM,i.e.the estimation accuracy is improved by 28.7%.And the proposed method achieves high estimation accuracy compared with the related algorithms in recent years.When increasing the increase of the number of particles,the estimation accuracy of each algorithm is improved,and the proposed algorithm still achieves the highest estimation accuracy.The experimental results fully show that the proposed algorithm can calculate the proposed distribution function more accurately and effectively alleviate the particle degradation problem in FastSLAM algorithm,which significantly improve the estimation accuracy of FastSLAM algorithm.

Key words: Adaptive fading unscented kalman filter, Particle degradation, Proposal distribution function, Robot, Simultaneous localization and mapping

中图分类号:

TP242.6

王秉洲, 王慧斌, 沈洁, 张丽丽. 基于自适应渐消UKF的FastSLAM算法[J]. 计算机科学, 2020, 47(9): 213-218. https://doi.org/10.11896/jsjkx.190700186

WANG Bing-zhou, WANG Hui-bin, SHEN Jie, ZHANG Li-li. FastSLAM Algorithm Based on Adaptive Fading Unscented Kalman Filter[J]. Computer Science, 2020, 47(9): 213-218. https://doi.org/10.11896/jsjkx.190700186

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