动载体环境视觉惯性自适应融合物体姿态测量算法  

Adaptive Fusion Algorithm of Visual-Inertial Attitude Measurement in Moving Carrier Environment

作　　者：王鹏[1,2] 王丞博 张宝尚 孙长库 WANG Peng;WANG Chengbo;Zhang Baoshang;SUN Changku(State Key Lab of Precision Measuring Technology&Instruments,Tianjin University,Tianjin 300072,China;Key Laboratory of Electro-Optical Control Technology,Luoyang He’nan 471000,China)

机构地区：[1]天津大学精密测试技术及仪器国家重点实验室,天津300072 [2]光电控制重点实验室,河南洛阳471000

出　　处：《传感技术学报》2023年第2期218-224,共7页Chinese Journal of Sensors and Actuators

基　　金：航空科学基金项目(ASFC-201951048002)。

摘　　要：针对动载体环境下视觉惯性组合姿态测量的过程中,惯性器件噪声时变和突变干扰会使卡尔曼滤波器估计不准确的问题,研究了一种Sage-Husa自适应强跟踪扩展卡尔曼算法。算法采用Sage-Husa自适应滤波来实时估计惯性器件的噪声协方差,引入多重渐消因子来抑制突变干扰,增强融合算法的鲁棒性。实验证明,当存在系统噪声变化和突变干扰时,所提算法优于Sage-Husa自适应扩展卡尔曼算法与强跟踪扩展卡尔曼算法,具有良好的鲁棒性。To deal with the problem that the time-varying and abrupt interference of inertial device noise make the result of Kalman filter inaccurate,a Sage-Husa adaptive strong tracking extended Kalman filter is studied.The algorithm adopts Sage-Husa adaptive filter to estimate the noise covariance of inertial devices in real time and uses multiple fading factors to suppress abrupt interference to enhance the robustness of the fusion algorithm.Experiments show that the proposed algorithm is superior to the Sage-Husa adaptive extended Kalman filter and the strong tracking extended Kalman filter and has good robustness when there is system noise variation and abrupt interference.

关 键 词：姿态测量 视觉惯性融合 自适应强跟踪滤波器 多重渐消因子 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置]