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.
Highlights
In recent years, the use of low-cost miniature unmanned aerial vehicles (MUAVs) for civilian applications has evolved from imagination to actual implementation
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 factor graph without considering the relationship between the sensor update frequency and the fusion period
Summary
The use of low-cost miniature unmanned aerial vehicles (MUAVs) for civilian applications has evolved from imagination to actual implementation. In order to obtain an acceptable cost/benefit ratio of these systems, there have been many techniques to reduce the cost including the aspects of the sensor, the platform, and the algorithm [6]. Cost reduction of the sensor can lead to lower stability and accuracy, which is directly related to the reliability of the system. As a matter of fact, multi-sensor integrated navigation is becoming a hot area of research. Many studies focus on the integration of the GPS/INS system with additional sensors such as magnetometers, cameras, ultrasonic sensors, laser scanners, barometers, or earth/sun/star sensors [8]
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