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Graph-optimisation-based self-calibration method for IMU/odometer using preintegration theory

Published online by Cambridge University Press:  13 January 2022

Shiyu Bai
Affiliation:
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, People's Republic of China
Jizhou Lai*
Affiliation:
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, People's Republic of China
Pin Lyu
Affiliation:
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, People's Republic of China
Yiting Cen
Affiliation:
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, People's Republic of China
Bingqing Wang
Affiliation:
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, People's Republic of China
Xin Sun
Affiliation:
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, People's Republic of China
*
*Corresponding author. E-mail: laijz@nuaa.edu.cn

Abstract

Determination of calibration parameters is essential for the fusion performance of an inertial measurement unit (IMU) and odometer integrated navigation system. Traditional calibration methods are commonly based on the filter frame, which limits the improvement of the calibration accuracy. This paper proposes a graph-optimisation-based self-calibration method for the IMU/odometer using preintegration theory. Different from existing preintegrations, the complete IMU/odometer preintegration model is derived, which takes into consideration the effects of the scale factor of the odometer, and misalignments in the attitude and position between the IMU and odometer. Then the calibration is implemented by the graph-optimisation method. The KITTI dataset and field experimental tests are carried out to evaluate the effectiveness of the proposed method. The results illustrate that the proposed method outperforms the filter-based calibration method. Meanwhile, the performance of the proposed IMU/odometer preintegration model is optimal compared with the traditional preintegration models.

Type
Research Article
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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