IMU error online calibration method based on factor graph

Abstract

The invention discloses an IMU error online calibration method based on a factor graph and relates to the field of combined navigation. According to the method, the scale factor error and the installation error of the IMU are modeled into a pre-integration model, and navigation information output by the IMU is fused with navigation information provided by other measurement sensors through an optimization theory based on a factor graph model. The error coefficient of the IMU can be obtained after optimization solution. Most traditional IMU error calibration methods are based on off-line calibration, and IMU errors are changed due to mechanical shock or other factors caused by carrier movement, so that the problem that the navigation precision is reduced is solved. The method can effectivelysolve the problem that the precision of the navigation system is reduced due to changes of IMU errors, and can be applied to various integrated navigation systems based on inertial navigation.

Description

technical field [0001] The invention relates to the field of integrated navigation, in particular to an IMU error online calibration method based on a factor graph. Background technique [0002] Due to its advantages of strong autonomy, good continuity and high stability, the integrated navigation system based on inertial navigation system has always been a research hotspot in the field of navigation technology. However, the inertial navigation system is essentially a dead reckoning system, and there is a problem of error accumulation over time. There are many error sources that affect the navigation accuracy, and the errors of the inertial device itself, such as: zero bias, scale factor error, installation error, etc., have a great impact on the navigation accuracy, and accurate calibration is required. [0003] In order to obtain various error coefficients of the IMU (Inertial measurement unit inertial measurement unit), there are currently two main methods: [0004] Met...

AI Technical Summary

Problems solved by technology

[0006] However, the disadvantage of method 1 is that the errors of the IMU due to the uncertainty of random startup and the errors of the calibration itself make the offline calibration results deviate from the errors when the IMU is actually used, which has a negative impact on real-time high-precision navigation; method 2 The disadvantage is that the consideration of the IMU device error is relatively simple. This method usu...

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