An Iterative-Optimization-Based Calibration Framework for VIO With Limited Prior Conditions

Wei, Hongyu; Zhang, Tao; Zhu, Yeyu

doi:10.1109/jsen.2021.3112060

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…And this initialization method is used in the open source library of ORB-SLAM3 (Campos et al , 2021). Wei et al (2021) propose a visual inertial odometer calibration framework with limited prior conditions based on iterative optimization, including three stages: time offset estimation, external and internal parameter calibration and global time and IMU parameter optimization.…”

Section: Introductionmentioning

confidence: 99%

A progressive online external parameter calibration and initialization method for stereo-IMU system

Zhai

Feng

2021

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Purpose This paper aims to present a pipeline to progressively deal with the online external parameter calibration and estimator initialization of the Stereo-inertial measurement unit (IMU) system, which does not require any prior information and is suitable for system initialization in a variety of environments. Design/methodology/approach Before calibration and initialization, a modified stereo tracking method is adopted to obtain a motion pose, which provides prerequisites for the next three steps. Firstly, the authors align the pose obtained with the IMU measurements and linearly calculate the rough external parameters and gravity vector to provide initial values for the next optimization. Secondly, the authors fix the pose obtained by the vision and restore the external and inertial parameters of the system by optimizing the pre-integration of the IMU. Thirdly, the result of the previous step is used to perform visual-inertial joint optimization to further refine the external and inertial parameters. Findings The results of public data set experiments and actual experiments show that this method has better accuracy and robustness compared with the state of-the-art. Originality/value This method improves the accuracy of external parameters calibration and initialization and prevents the system from falling into a local minimum. Different from the traditional method of solving inertial navigation parameters separately, in this paper, all inertial navigation parameters are solved at one time, and the results of the previous step are used as the seed for the next optimization, and gradually solve the external inertial navigation parameters from coarse to fine, which avoids falling into a local minimum, reduces the number of iterations during optimization and improves the efficiency of the system.

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Section: Introductionmentioning

confidence: 99%