摘要
为了解决惯性导航系统长航时累计误差和实时性问题,提出了一种基于因子图的多源信息融合算法。论文基于和积算法,采用了因子图模型进行基于最大后验概率的状态估计,计算变量的均值和方差完成数据融合,利用超宽带技术和视觉传感器对发生累积误差的惯性导航系统进行校准,完成误差修正。对信息融合算法进行了仿真,结果为:该算法各轴向的误差分别为0.36%和0.31%,解算时间为50 ms。该算法误差仅为惯性导航累计误差的1/3,实时性相较于其它算法更高。
In order to solve the long endurance cumulative error and real-time problem of inertial navigation system, a multi-source information fusion algorithm based on factor graph is proposed. Based on sum product algorithm, the factor graph model is used to estimate the state based on the maximum a posteriori probability, the mean and variance of variables are calculated to complete data fusion, and the UWB technology and vision sensor are used to calibrate the inertial navigation system with accumulated error to complete error correction. The simulation results show that the error of each axis is 0.36% and 0.31% respectively, and the solution time is 50 ms. The error of this algorithm is only 1/3 of the cumulative error of inertial navigation, and its real-time performance is better than other algorithms.
作者
姚祖威
刘宇
郭俊启
欧毅
邹新海
康鹏川
芶志平
Yao Zuwei;Liu Yu;Guo Junqi;Ou Yi;Zou Xinhai;Kang Pengchuan;Gou Zhiping(Chongqing Engineering Research Center of Intelligent Sensing Technology and Microsystem,Chongqing University of Post and Telecommunications,Chongqing 400065,China;Institute of Microelectronics of the Chinese Academy of Sciences,Beijing 100029,China;The 26th Institute of China Electronics Technology Group Corporation,Chongqing 400060,China)
出处
《电子测量技术》
北大核心
2021年第1期130-134,共5页
Electronic Measurement Technology
基金
国家重点研发计划基金(2018YFF01010202,2018YFF01010201,2018YFE0115500)项目资助。
关键词
实时性
自主导航
和积算法
因子图
信息融合
real time
autonomous navigation
sum product algorithm
factor graph
information fusion
