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基于非线性滤波的移动机器人位姿估计 被引量：1

Pose State Estimate of Mobile Robots Based on Nonlinear Filters
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摘要 研究了基于扩展卡尔曼滤波和粒子滤波的移动机器人位姿估计。由于扩展卡尔曼滤波必须假定噪声服从高斯分布,若用于复杂非线性系统,其估计精度不甚理想。粒子滤波对噪声类型没有限制,正在成为非线性系统状态估计的有效近似方法。在不同噪声条件下,对基于粒子滤波和扩展卡尔曼滤波的移动机器人位姿估计问题进行了比较研究。仿真结果表明:粒子滤波能明显地改善移动机器人位姿估计的鲁棒性和精度。 The issue of pose state estimate of mobile robots based on extended Kalman filter （EKF） and particle filter is studied. Because EKF must assume that the noise is subject to Gaussian distribution, the estimate accuracy is not so good if it is used to estimate the state of complicated nonlinear system. Particles filter （PF） has no restriction to the noise type and it is an effective approximate method for the state estimate of nonlinear systems. Under various noise conditions, the comparative studies for the pose state estimate of mobile robots have been done based on EKF and PF, respectively. Simulation results show that particle filter effectively improves the robustness of the pose state estimate and localization precision of mobile robots.
作者 陈杨钟 刘士荣 俞金寿
机构地区 华东理工大学自动化研究所 杭州电子科技大学自动化研究所
出处 《华东理工大学学报（自然科学版）》 EI CAS CSCD 北大核心 2007年第4期558-563,共6页 Journal of East China University of Science and Technology
基金 国家自然科学基金资助项目(60675043) 浙江省自然科学基金资助项目(Y104560) 杭州电子科技大学科研启动基金资助项目(KYS09150543)
关键词 移动机器人 位姿估计 扩展卡尔曼滤波 粒子滤波 非线性滤波 mobile robot pose state estimate extended Kalman filter particle filter nonlinear filter
分类号 TP24 [自动化与计算机技术—检测技术与自动化装置]
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参考文献12

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共引文献394

同被引文献6

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华东理工大学学报（自然科学版）
2007年 第4期

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