摘要
面向AUV搭载的传感器信息频率不一致与有效性易动态改变的情况,因子图算法相较于扩展Kalman滤波算法表现出更好的稳定性、灵活性与扩展性。本文首先比较了FGO与EKF算法分别应用于AUV多传感器信息融合定位的性能,再针对复杂水下环境中,传感器的异常观测值影响FGO算法定位精度的问题,提出了一种基于抗差因子图的AUV多源信息融合定位方法,利用动态协方差缩放策略对粗差因子进行降权处理,在海测数据的基础上模拟观测粗差进行算法验证。分别采用普通FGO算法、基于DCS的抗差FGO算法对受到粗差干扰后的数据进行解算。统计结果表明,相较于不进行抗差处理,该算法降低了14.6%的平面位置误差,对异常观测具有良好的稳健性能。
Compared with the extended Kalman filtering(EKF)algorithm,factor graph optimization(FGO)shows better stability,flexibility and expansibility for the asynchronous and dynamic change of sensor information carried by AUV.This paper compares the performance of FGO and EKF algorithms applied to AUV multi-sensor information fusion and location firstly,and then proposes an AUV multi-source information fusion location method based on robust factor graph to solve the problem that abnormal observations of sensors affect the positioning accuracy of FGO algorithm in complex underwater environment.Dynamic covariance scaling(DCS)strategy was used to reduce the weight of the gross error factor,and the algorithm was verified by simulating the observed gross error based on the sea data.The ordinary FGO algorithm and DCS anti-difference FGO algorithm were used to calculate the data disturbed by gross error.The statistical results show that the proposed algorithm reduces the plane position error by 14.6%compared with that without anti-error processing,and has good robustness for abnormal observation.
作者
黄紫如
柴洪洲
向民志
杜祯强
HUANG Ziru;CHAI Hongzhou;XIANG Minzhi;DU Zhenqiang(Information Engineering University,Zhengzhou 450001,China;Troops 32022,Guangzhou 510000,China;The Key Laboratory of Ocean Geomatics,Ministry of Natural Resources,Qingdao 266590,China)
出处
《测绘学报》
EI
CSCD
北大核心
2023年第8期1278-1285,共8页
Acta Geodaetica et Cartographica Sinica
基金
国家自然科学基金(42074014)。
关键词
因子图
多源信息融合定位
自主水下潜航器
抗差因子图
factor graph
multi-source information fusion localization
autonomous underwater vehicle
robust factor graph
