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Sensors 2017, 17(3), 641; doi:10.3390/s17030641

An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph

1,2,* , 1,2,* , 1,2
and
1,2
1
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China
2
Satellite Communication and Navigation Collaborative Innovation Center, Nanjing 211100, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Cheng Wang, Julian Smit, Ayman F. Habib and Michael Ying Yang
Received: 4 January 2017 / Revised: 13 March 2017 / Accepted: 14 March 2017 / Published: 21 March 2017
(This article belongs to the Special Issue Multi-Sensor Integration and Fusion)
View Full-Text   |   Download PDF [4163 KB, uploaded 21 March 2017]   |  

Abstract

An integrated navigation system coupled with additional sensors can be used in the Micro Unmanned Aerial Vehicle (MUAV) applications because the multi-sensor information is redundant and complementary, which can markedly improve the system accuracy. How to deal with the information gathered from different sensors efficiently is an important problem. The fact that different sensors provide measurements asynchronously may complicate the processing of these measurements. In addition, the output signals of some sensors appear to have a non-linear character. In order to incorporate these measurements and calculate a navigation solution in real time, the multi-sensor fusion algorithm based on factor graph is proposed. The global optimum solution is factorized according to the chain structure of the factor graph, which allows for a more general form of the conditional probability density. It can convert the fusion matter into connecting factors defined by these measurements to the graph without considering the relationship between the sensor update frequency and the fusion period. An experimental MUAV system has been built and some experiments have been performed to prove the effectiveness of the proposed method. View Full-Text
Keywords: micro unmanned aerial vehicle; multi-sensor information fusion; factor graph; probability density micro unmanned aerial vehicle; multi-sensor information fusion; factor graph; probability density
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Zeng, Q.; Chen, W.; Liu, J.; Wang, H. An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph. Sensors 2017, 17, 641.

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