基于强跟踪UKF的自适应SLAM算法

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张文玲, 朱明清, 陈宗海. 基于强跟踪UKF的自适应SLAM算法[J]. 机器人, 2010, 32(2): 190-195.. ZHANG Wenling, ZHU Mingqing, CHEN Zonghai. An Adaptive SLAM Algorithm Based on Strong Tracking UKF. ROBOT, 2010, 32(2): 190-195..

摘要针对无迹卡尔曼滤波（UKF）缺乏在线自适应调整能力，导致系统状态估计精度较低的问题，提出了一种将强跟踪滤波器（STF）与UKF相结合的SLAM算法．该算法对于UKF中每个采样点采用STF进行更新，获得优化滤波增益，抑制噪声对系统状态估计的影响，使系统状态估计迅速收敛到真实值附近．仿真实验对比了当前几种SLAM算法在不同噪声环境下的性能，实验表明，基于强跟踪UKF的自适应SLAM算法具有更好的鲁棒性和自适应性．

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	张文玲
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关键词 ：同时定位与地图创建, UKF-SLAM, 强跟踪滤波器, 自适应滤波

Abstract：Unscented Kalman filter (UKF) is lack of adaptive on-line adjustment ability that seriously decreases the estimation accuracy of system state. To deal with this problem, this paper proposes an improved SLAM (simultaneous localization and mapping) algorithm that combines the strengths of strong tracking filter (STF) and UKF. Each sampling point of UKF is updated by STF, the effects of noises on system state estimation are suppressed by optimizing filter gains, and the system state estimation converges to real values quickly. Performances of several SLAM algorithm in different noisy environments are compared by simulation. The experimental results show that this adaptive SLAM algorithm based on STF and UKF is of better adaptability and robustness.

Key words： simultaneous localization and mapping UKF-SLAM strong tracking filter adaptive filter

收稿日期: 2009-03-30 录用日期: 2009-10-19

TP24

基金资助:国家自然科学基金资助项目（60575033，60804020）；国家863计划资助项目（2007AA04Z227）

通讯作者: 陈宗海 E-mail: chenzh@ustc.edu.cn

作者简介: 张文玲（1984- ），女，硕士生．研究领域：机器人导航，机器人同时定位与地图创建．
朱明清（1982– ），男，博士生．研究领域：计算机视觉，模式识别，量子信息调控．
陈宗海（1963– ），男，教授，博士生导师．研究领域：复杂系统的建模、仿真与控制，智能机器人，量子信息控制等．

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https://robot.sia.cn/CN/ 或 https://robot.sia.cn/CN/Y2010/V32/I2/190

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