Multi-source information fusion based on factor graph in autonomous underwater vehicles navigation systems

Abstract

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.

Factor Graph with Local Constraints: A Magnetic Field/Pedestrian Dead Reckoning Integrated Navigation Method Based on a Constrained Factor Graph

“…The PDR method was used as a baseline. The performances of the extended Kalman filter (EKF) [9]; the existing factor graph algorithm (FG) [11]; the improved factor graph (IFG) [21], which is the stateof-the-art method; and the factor graph with local constraints algorithm (FGLC) proposed in this paper were compared, respectively.…”

“…The combination of the EKF algorithm and a particle filter was used to fuse the magnetic field gradient and inertial navigation information by Gao D et al [10]. The "plug and play" feature based on a factor graph helps deal with the challenge of multi-sensor asynchrony [11]. A composite positioning inertial/geomagnetic/lidar technology based on graph optimization was proposed by Zhao Y N et al [12].…”

Three-Dimensional Dead-Reckoning Based on Lie Theory for Overcoming Approximation Errors

“…To further enhance the robustness and accuracy of DR systems, recent studies have explored the integration of factor graphs [19,20]. Zhang et al (2024) optimized AUV navigation by integrating side-scan sonar (SSS) data and inertial measurement units (IMU) using factor graphs, demonstrating significant improvements in positioning accuracy under challenging conditions [19].…”

A transfer alignment algorithm based on combined double-time observation of velocity and attitude

“…If the static flexural deformation and installing errors are not calibrated and compensated immediately, it will be introduced into the attitude measurement system as a constant value error, which will reduce or even diverge in the performance of the transfer alignment Kalman filter (KF). The attitude matching method can use the attitude as an observation to compensate for the constant error, but it needs to rely on specific maneuvers for angular motion excitation (Ma et al , 2021). From the description in the previous paragraph, the ship is not only bulky but also has poor maneuverability, and the wind and wave excitation is limited.…”