Purpose

This paper aims to present a multi-source information fusion algorithm based on factor graph for autonomous underwater vehicles (AUVs) navigation and positioning to address the asynchronous and heterogeneous problem of multiple sensors.

Design/methodology/approach

The factor graph is formulated by joint probability distribution function (pdf) random variables. All available measurements are processed into an optimal navigation solution by the message passing algorithm in the factor graph model. To further aid high-rate navigation solutions, the equivalent inertial measurement unit (IMU) factor is introduced to replace several consecutive IMU measurements in the factor graph model.

Findings

The proposed factor graph was demonstrated both in a simulated and vehicle environment using IMU, Doppler Velocity Log, terrain-aided navigation, magnetic compass pilot and depth meter sensors. Simulation results showed that the proposed factor graph processes all available measurements into the considerably improved navigation performance, computational efficiency and complexity compared with the un-simplified factor graph and the federal Kalman filtering methods. Semi-physical experiment results also verified the robustness and effectiveness.

Originality/value

The proposed factor graph scheme supported a plug and play capability to easily fuse asynchronous heterogeneous measurements information in AUV navigation systems.

中文翻译：

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自主水下航行器导航系统中基于因子图的多源信息融合

目的

本文旨在提出一种基于因子图的多源信息融合算法，用于自主水下航行器（AUV）导航和定位，以解决多个传感器的异步和异构问题。

设计/方法/方法

因子图由联合概率分布函数 (pdf) 随机变量制定。因子图模型中的消息传递算法将所有可用的测量值处理成最佳导航解决方案。为了进一步帮助高速导航解决方案，引入等效惯性测量单元 (IMU) 因子来替换因子图模型中的多个连续 IMU 测量值。

发现

使用 IMU、多普勒速度测井、地形辅助导航、磁罗盘导航和深度计传感器在模拟和车辆环境中演示了所提出的因子图。仿真结果表明，与未简化的因子图和联邦卡尔曼滤波方法相比，所提出的因子图将所有可用的测量结果处理成显着提高的导航性能、计算效率和复杂性。半物理实验结果也验证了稳健性和有效性。

原创性/价值

所提出的因子图方案支持即插即用功能，可轻松融合 AUV 导航系统中的异步异构测量信息。