Research on IMU Calibration Model Based on Polar Decomposition

Author:

Zhao Guiling1,Tan Maolin1,Wang Xu1,Liang Weidong1,Gao Shuai1,Chen Zhijian1ORCID

Affiliation:

1. School of Geomatics, Liaoning Technical University, Fuxin 123000, China

Abstract

As an important deterministic error of the inertial measurement unit (IMU), the installation error has a serious impact on the navigation accuracy of the strapdown inertial navigation system (SINS). The impact becomes more severe in a highly dynamic application environment. This paper proposes a new IMU calibration model based on polar decomposition. Using the new model, the installation error is decomposed into a nonorthogonal error and a misalignment error. The compensation of the IMU calibration model is decomposed into two steps. First, the nonorthogonal error is compensated, and then the misalignment error is compensated. Based on the proposed IMU calibration model, we used a three-axis turntable to calibrate three sets of strapdown inertial navigation systems (SINS). The experimental results show that the misalignment errors are larger than the nonorthogonal errors. Based on the experimental results, this paper proposes a new method to simplify the installation error. This simplified method defines the installation error matrix as an antisymmetric matrix composed of three misalignment errors. The navigation errors caused by the proposed simplified calibration model are compared with the navigation errors caused by the traditional simplified calibration model. The 48-h navigation experiment results show that the proposed simplified calibration model is superior to the traditional simplified calibration model in attitude accuracy, velocity accuracy, and position accuracy.

Funder

Natural Science Foundation of Liaoning Province

Liaoning Provincial Department of Education Project

State Key Laboratory of Geo-Information Engineering

Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, CASM

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Control and Systems Engineering

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Low-Cost MEMS IMU Calibration Using Deep Learning and Visual-Inertial Odometry;2023 11th RSI International Conference on Robotics and Mechatronics (ICRoM);2023-12-19

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