引用本文:闫德立,喻薇,宋宇,等.基于矩阵李群表示及容积卡尔曼滤波的视觉惯导里程计新方法[J].控制与决策,2020,35(8):1823-1832
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基于矩阵李群表示及容积卡尔曼滤波的视觉惯导里程计新方法
闫德立1,2, 喻薇3, 宋宇1, 吴春慧2, 宋永端1,3/sup>
(1. 北京交通大学电子信息工程学院,北京100044;2. 石家庄铁道大学电气与电子工程学院,石家庄050043;3. 重庆大学自动化学院,重庆400044)
摘要:
针对滤波方法实现的视觉-惯导里程计(VIO)问题,为更准确传递旋转运动的不确定性并降低系统线性化误差,提高位姿估计的精度,设计并实现了一种高维矩阵李群表示的采用容积卡尔曼滤波框架实现的VIO算法.算法将状态变量构建为一个高维李群矩阵,并定义了李群变量在容积点采样过程中的‘加法’运算,将容积点和状态均值、方差等概念由欧氏空间扩展到流形空间;采用容积变换传递状态均值及方差,避免了旋转运动复杂的雅克比矩阵计算过程,降低了模型线性化误差.最后,使用EuRoc MAV数据集进行算法验证,结果表明所提出算法在提高位姿估计精度方面是有效的.
关键词:  视觉-惯导里程计  矩阵李群  容积卡尔曼滤波  位姿估计
DOI:10.13195/j.kzyjc.2018.1596
分类号:TP242
基金项目:国家自然科学基金项目(61773081,61860206008,61803053,61833013,61573053,11972238);中央高校基本科研业务费专项基金项目(2018CDPTCG0001/43);河北省自然科学基金项目(E2016210104);河北省教育厅项目(Z2017022).
A new method for visual inertial odometry based on cubature Kalman filter and matrix Lie group representation
YAN De-li1,2,YU Wei3,SONG Yu1,WU Chun-hui2,SONG Yong-duan1,3
(1. College of Electronic and Information Engineering,Beijing Jiaotong University,Beijing100044,China;2. College of Elctric and Electronic Engineering,Shijiazhuang Tiedao University,Shijiazhuang050043,China;3. College of Automation,Chongqing University,Chongqing400044,China)
Abstract:
Considering robotic visual inertial odometry(VIO) using filtering methods, a VIO algorithm is proposed in order to improve estimation accuracy. This algorithm uses cubature Kalman filter on matrix Lie group to realize it, which can accurately describe system uncertainty in rotation and reduce the linearization error of systems. The characters of the proposed algorithm are that: 1) the state is built by an high dimensional Lie group matrix and the definition of the additional operation for Lie group variant is proposed in cubature point sampling, which can extend the concept of cubature point, state mean and covariance from Euclidean space to manifold; 2)the state mean and covariance are propagated by cubature transformation, which avoids calculating complicated Jacobi matrixes and reduces the linearization error of the system. The performance of the proposed algorithm is tested in the EuRoc MAV dataset, and the results show the effectiveness of the proposed algorithm in improving estimation accuracy.
Key words:  visual inertial odometry  matrix Lie group  cubuture Kalman filter  pose estimation

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