scholarly journals Upper Stage Visual Inertial Integrated Navigation Method Based on Factor Graph

2021 ◽  
Vol 2085 (1) ◽  
pp. 012018
Author(s):  
Peng Wu ◽  
Rongjun Mu ◽  
Bingli Liu
Keyword(s):  
Information Fusion ◽  
Inertial Sensors ◽  
Joint Probability ◽  
Graph Model ◽  
Factor Graph ◽  
Joint Probability Distribution ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Upper Stage ◽  
Navigation Information

Abstract In the working process of the upper stage integrated navigation information fusion system, the multi-source navigation information fusion algorithm based on factor graph Bayesian estimation is used to fuse the information of inertial sensors, visual sensors and other sensors. The overall joint probability distribution of the system is described in the form of probability graph model with the dependence of local variables, so as to reduce the complexity of the system, adjust the data structure of information fusion to improve the efficiency of information fusion and smoothly switch the sensor configuration.

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

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaoshuang Ma ◽  
Xixiang Liu ◽  
Chen-Long Li ◽  
Shuangliang Che
Keyword(s):  
Information Fusion ◽  
Autonomous Underwater Vehicles ◽  
Graph Model ◽  
Underwater Vehicles ◽  
Factor Graph ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Source Information ◽  
Content Type ◽  
Factor Graph Model

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.

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