基于抗差因子图的AUV多源信息融合定位方法

doi:10.11947/j.AGCS.2023.20210735

测绘学报 ›› 2023, Vol. 52 ›› Issue (8): 1278-1285.doi: 10.11947/j.AGCS.2023.20210735

基于抗差因子图的AUV多源信息融合定位方法

黄紫如^1,2, 柴洪洲¹, 向民志^1,3, 杜祯强^1,3

1. 信息工程大学, 河南郑州 450001;
2. 32022部队, 广东广州 510000;
3. 自然资源部海洋测绘重点实验室, 山东青岛 266590

收稿日期:2022-01-07 修回日期:2023-03-20 发布日期:2023-09-07
通讯作者: 柴洪洲 E-mail:chaihz1969@163.com
作者简介:黄紫如(1997-),女,硕士,助理工程师,研究方向为多源信息融合定位技术。E-mail:ziruhuang97@163.com
基金资助:
国家自然科学基金(42074014)

AUV multi-source information fusion localization method based on robust factor graph

HUANG Ziru^1,2, CHAI Hongzhou¹, XIANG Minzhi^1,3, DU Zhenqiang^1,3

1. Information Engineering University, Zhengzhou 450001, China;
2. Troops 32022, Guangzhou 510000, China;
3. The Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, Qingdao 266590, China

Received:2022-01-07 Revised:2023-03-20 Published:2023-09-07
Supported by:
The National Natural Science Foundation of China (No. 42074014)

摘要/Abstract

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。

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

Abstract: 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.

Key words: factor graph, multi-source information fusion localization, autonomous underwater vehicle, robust factor graph

中图分类号:

P229.2

黄紫如, 柴洪洲, 向民志, 杜祯强. 基于抗差因子图的AUV多源信息融合定位方法[J]. 测绘学报, 2023, 52(8): 1278-1285.

HUANG Ziru, CHAI Hongzhou, XIANG Minzhi, DU Zhenqiang. AUV multi-source information fusion localization method based on robust factor graph[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(8): 1278-1285.

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基于抗差因子图的AUV多源信息融合定位方法

AUV multi-source information fusion localization method based on robust factor graph

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