针对移动机器人噪声模型不确定性导致定位算法鲁棒性弱、精度低的问题,提出一种基于奇异值分解(Singular Value Decomposition,SVD)的自适应无迹H∞滤波定位算法.该算法利用无迹H∞滤波融合多传感器数据估计移动机器人位姿,并通过自适应调节滤波器参数γ,提高了移动机器人的定位精度.同时为了提高算法的鲁棒性,采用SVD分解代替常规Cholesky分解,避免了误差协方差矩阵在数值迭代过程中出现负定的情况.实验结果表明:相较于扩展H∞滤波和粒子滤波算法,基于SVD分解的自适应无迹H∞滤波定位算法具有精度高、鲁棒性强的优势.
Adaptive Unscented H∞ Filtering Localization Algorithm Based on SVD
Targeting at the problems of low robustness and accuracy of the localization algorithm of mobile robot caused by the indefi-niteness of the noise model,an adaptive unscented H∞ filtering localization algorithm based on singular value decomposition(SVD)is proposed.Firstly,the pose of the mobile robot is estimated by combining the data of the odometry and the LiDAR.Then,the filter pa-rameters are adaptively adjusted to improve the accuracy of the localization.Meanwhile,singular value decomposition(SVD)is used to improve the robustness of the numerical calculation and the negative definiteness of the error covariance is avoided,which is always en-countered by the conventional Cholesky decomposition.Finally,some experimental results are given to show the advantages of the algo-rithm proposed in this paper.
localization of the indoor mobile robotmulti-sensor fusionadaptive unscented H∞ filteringSVD
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