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.
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.
出处
《机器人》
EI
CSCD
北大核心
2010年第2期190-195,共6页
Robot
基金
国家自然科学基金资助项目(60575033
60804020)
国家863计划资助项目(2007AA04Z227)
