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A novel online time calibration framework via double-stage EKF for visual-inertial odometry

Published online by Cambridge University Press:  28 October 2024

Shuaiyong Li ,
Jiawei Nie Open the ORCID record for Jiawei Nie [Opens in a new window] ,
Chengchun Guo ,
Yang Yang  and
Lin Mei
Shuaiyong Li*
Affiliation:
School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
Jiawei Nie
Affiliation:
School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
Chengchun Guo
Affiliation:
School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
Yang Yang
Affiliation:
School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
Lin Mei
Affiliation:
Chongqing Special Equipment Inspection and Research Institute, Chongqing, China
*
Corresponding author: Shuaiyong Li; Email: lishuaiyong@cqupt.edu.cn
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Abstract

There is an unavoidable time offset between the camera stream and the inertial measurement unit (IMU) data due to the sensor triggering and transmission delays, which will seriously affect the accuracy of visual-inertial odometry (VIO). A novel online time calibration framework via double-stage EKF for VIO is proposed in this paper. First, the first-stage complementary Kalman filter is constructed by adapting the complementary characteristics between the accelerometer and the gyroscope in the IMU, where the rotation result predicted by the gyroscope is corrected through the measurement of the accelerometer so that the IMU can output a more accurate initial pose. Second, the unknown time offset is added to the state vector of the VIO system. The estimated pose of IMU is used as the prediction information, and the reprojection error of multiple cameras on the same feature point is used as the constraint information. During the operation of the VIO system, the time offset is continuously calculated and superimposed on the IMU timestamp to obtain the data synchronized by the IMU and the camera. The Schur complement model is used to marginalize the camera state that carries less information in the system state, avoiding the loss of prior information between images, and improving the accuracy of camera pose estimation. Finally, the effectiveness of proposed algorithm is verified using the EuRoC dataset and the real experimental data.

Keywords

state estimationsensor fusiononline time calibrationvisual-inertial odometry

Information

Type
Research Article
Information
Robotica , Volume 42 , Issue 11 , November 2024 , pp. 3675 - 3691
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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