摘要
针对3维空间中移动机器人同时定位与地图构建(SLAM)问题,提出了一种基于改进强跟踪滤波(STF)的快速自适应SLAM算法.该算法首先对于强跟踪滤波器的噪声协方差阵进行在线自适应估计,用于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;随后将状态协方差矩阵进行奇异值分解(SVD),提高算法的数值稳定性.该算法可提高对系统时变的自适应能力以及系统状态估计精度.与基于强跟踪滤波器的SLAM算法的仿真对比结果说明了该算法的有效性及其在抑噪性能和估计精度方面的优越性.
For the simultaneous localization and mapping （SLAM） problem of mobile robots in the three-dimensional space, a fast and adaptive SLAM algorithm based on the improved strong tracking filter （STF） is proposed. Firstly, the noise covariance matrixes of STF are adaptively estimated on line to suppress the effects of noises on system state estimation, which makes the system state estimation converge to real values quickly. Secondly, singular value decomposition （SVD） is used to decompose the state covariance matrix which can help the algorithm get better numerical stability. This algorithm can improve the adaptive ability of time-varying systems and the state estimation precision, and the comparative simulation results with STF-SLAM algorithm illustrate the effectiveness and the superiority of the proposed algorithmon on the denoising performance and the estimation precise.
出处
《信息与控制》
CSCD
北大核心
2012年第4期419-424,共6页
Information and Control
基金
国家自然科学基金资助项目(60804066
61034006)
江西省自然科学基金资助项目(2007GQS0885)
江西省教育厅科技项目(GJJ09508
GJJ11115
GJJ12286)
关键词
同时定位与地图构建(SLAM)
强跟踪滤波器
在线自适应估计
噪声协方差阵
奇异值分解
simultaneous localization and mapping （SLAM）
strong tracking filter
online adaptive estimation
noise co- variance matrix
singular value decomposition
