基于变分贝叶斯的自适应PF-SLAM方法研究

Research on adaptive PF-SLAM method based on variational Bayesian

针对移动机器人即时定位与地图构建中时变观测噪声及粒子位置分布对SLAM精度的影响,本文提出基于变分贝叶斯的自适应PF-SLAM算法,采用高斯混合模型对时变的观测噪声建模,并通过变分贝叶斯方法,迭代估算出混合模型中的未知参数;同时根据粒子权值将粒子划分为固定粒子和优化粒子,通过粒子间的近邻拓扑位置关系调整粒子分布,处理时变观测噪声与优化粒子的位置分布,使得优化的粒子集可以更好地表示机器人位置概率分布,实现观测噪声及粒子位置分布自适应。仿真实验表明本算法对比传统PF-SLAM算法定位与地图构建误差降低了76.45%。实际实验表明本算法处理下的环境轮廓误差对比传统PF-SLAM算法的环境轮廓误差减小了61.87%。该算法有效提高了移动机器人的状态估计精度,为移动机器人即时定位与地图构建提供了新的参考。

To address the time-varying observation noise and particle position distribution on simultaneous localization and mapping(SLAM)accuracy in particle filter SLAM(PF-SLAM)for simultaneous localization and mapping of mobile robots,this article proposes an adaptive PF-SLAM algorithm based on variational Bayes,which adopts a Gaussian mixture model to formulate the time-varying observation noise and iteratively estimates the unknown parameters in the mixture model by using a variational Bayesian method.Meanwhile,the particles are divided into fixed particles and optimized particles according to the particle weights,and the particle positions are adjusted by the topological position distribution relationship between two particles,which handle the time-varying observation noise and optimize the particle position distribution.In this way,the optimized particle set could represent the robot position probability distribution and realize the adaptive observation noise and particle position distribution.Compared with the traditional PF-SLAM algorithm,simulation results show that the positioning and map building error of this algorithm is reduced by 76.45%.Compared with the traditional PF-SLAM algorithm,the actual experiments show that the environmental contour error of this algorithm is reduced by 61.87%.It effectively improves the state estimation accuracy of mobile robot and provides a new reference for mobile robot real-time positioning and map construction.