摘要
针对冗余惯性测量单元(inertial measurement unit,IMU)姿态估计问题,通过采用不变扩展卡尔曼滤波器结构,将被估计系统动态特性建模为矩阵李群和偏差向量的混合模型,从而设计基于协方差在线校正的局部IMU姿态估计器.在此基础上,采用对数映射将李群不确定性映射到向量空间,进而构造依赖于局部姿态估计器参数的分布式融合问题,从而设计基于李群的鲁棒协方差交叉融合准则.特别地,为解决局部估计器之间互协方差未知的问题,通过引入先验容差来约束未知互协方差,从而提供低保守性的协方差上界以提高融合估计性能,形成一种具有双层结构的冗余IMU姿态估计方法.最后在下肢外骨骼康复机器人平台上验证所提出算法的有效性.
This paper focuses on the attitude estimation problem of redundant inertial measurement unit(IMU).The dynamic characteristics of the estimated system are modeled as a hybrid model of matrix Lie group and bias vector by using invariant extended Kalman filter structure,and a local IMU attitude estimator based on online covariance correction is designed.On this basis,the Lie group uncertainty is mapped to the vector space using logarithmic mapping,and then the distributed fusion problem depending on local attitude estimator parameters is constructed,thus designing a robust covariance intersection fusion criterion based on the Lie group.In particular,in order to solve the problem of unknown cross-covariance between local estimators,a prior tolerance is introduced to constrain the unknown cross-covariance,thereby providing a low-conservative covariance upper bound to improve fusion estimation performance,forming a twolayer structure method for redundant IMU attitude estimation.Finally,the effectiveness of the proposed algorithm is verified on the lower limb exoskeleton rehabilitation robot platform.
作者
仇翔
石水萌
胡中尧
陈博
QIU Xiang;SHI Shui-meng;HU Zhong-yao;CHEN Bo(College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China)
出处
《控制与决策》
北大核心
2025年第1期345-352,共8页
Control and Decision
基金
浙江省重点研发计划项目(2022C03029,2024C03040)。
关键词
姿态估计
冗余IMU
不变卡尔曼滤波
矩阵李群
协方差交叉
attitude estimation
redundant IMU
invariant Kalman filter
matrix Lie group
covariance intersection
