A high-precision calibration approach for Camera-IMU pose parameters with adaptive constraints of multiple error equations

•Establishment Camera-IMU pose parameter calibration model.•Setting optimization objective function and constraint equation.•Quaternion and Newton's method are added to the model.•Camera-IMU pose parameters can be calculated based on calibration field.•The model improves the accuracy and robust...

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Bibliographic Details
Published in:Measurement : journal of the International Measurement Confederation 2020-03, Vol.153, p.107402, Article 107402
Main Authors: Qiu, Dongwei, Li, Shaofu, Wang, Tong, Ye, Qing, Li, Ruijie, Ding, Keliang, Xu, Hao
Format: Article
Language:English
Subjects:
Binocular vision mobile measurement sensor system
Calibration
Cameras
Differential equations
Drift
Error analysis
Error compensation
Error correction
Inertial measurement unit
Inertial platforms
Inertial sensing devices
Mathematical analysis
Newton method
Parallax
Parameters
Quaternion
Random errors
Robustness
Sensors
Spatial distribution
Stereovision
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Summary:•Establishment Camera-IMU pose parameter calibration model.•Setting optimization objective function and constraint equation.•Quaternion and Newton's method are added to the model.•Camera-IMU pose parameters can be calculated based on calibration field.•The model improves the accuracy and robustness of binocular vision data collection. In the calibration of the pose parameters of a camera and inertial measurement unit (Camera-IMU), the camera depth information is unreliable due to the uneven spatial distribution of calibration points, because the calibration points have random errors due to the IMU drift and the inadequate robustness of stereovision and because the Camera-IMU pose parameters lack self-adaptation. This paper proposes a high-precision calibration approach for Camera-IMU pose parameters with adaptive constraints of multiple error equations (adaptive constraint calibration approach, ACCA). The approach calibrates pose parameters of the Camera-IMU jointly via error equations, such as lens distortion correction, camera parallax correction and error compensation of the inertial sensor. The experimental results show that the calibration approach for Camera-IMU pose parameters with adaptive constraints of multiple error equations improves the measurement accuracy by 84.0% and can effectively suppress IMU drift with good robustness.
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2019.107402