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- Title
Improved IMU Preintegration with Gravity Change and Earth Rotation for Optimization-Based GNSS/VINS.
- Authors
Jiang, Junxiang; Niu, Xiaoji; Liu, Jingnan
- Abstract
IMU preintegration technology has been widely used in the optimization-based sensor fusion framework, in order to avoid reintegrating the high-frequency IMU measurements at each iteration and maintain the ability of bias correction when bias estimation changes. Since IMU preintegration technology was first proposed, several improved versions have been designed by changing the attitude parameterization or the numerical integration method in the most current related research. However, all of these versions have failed to take the change of gravity and the earth rotation into consideration. In this paper, we redesign the IMU preintegration algorithm in which the earth rotation and gravity vector are calculated from the geodetic position. Compared with the covariance matrix form, in this paper, the uncertainty of the preintegrated IMU measurements is propagated in the form of a square root information matrix (SRIM) for better numerical stability and easy use in the optimization-based framework. We evaluate the improved IMU preintegration algorithm by using the dataset collected by our sensor platform equipped with two different-grade IMUs. The test results show that the improved IMU preintegration algorithm can cope well with the gravity change and earth rotation. The earth rotation must be taken into consideration for the high-grade IMU that can effectively sense the earth rotation. If the change of gravity is omitted, the root-mean-square error (RMSE) of the horizontal attitude is about 1.38 times greater than the geodetic displacement. Additionally, the positioning RMSE does not increase obviously within a limited range, which means tens of kilometers and several hundred meters for the low-grade and high-grade IMU used in the experiment, respectively.
- Subjects
ROTATION of the earth; GRAVITY; SQUARE root; NUMERICAL integration; ESTIMATION bias
- Publication
Remote Sensing, 2020, Vol 12, Issue 18, p3048
- ISSN
2072-4292
- Publication type
Article
- DOI
10.3390/rs12183048