Online temporal calibration based on cross-correlation and rotation constraints for visual-inertial odometry
In the fusion of camera and inertial measurement unit(IMU)data for inferring the robot's trajectory,the timing of sensor measurements plays a crucial role in the robustness and accuracy of the visual-inertial odometry(VIO).However,due to differences in the delay of sensor data reaching the receiver,an unavoidable time offset exists between the image data stream and the IMU data stream.To address this issue,this paper proposed an algorithm based on cross-correlation and rotation con-straints for online temporal calibration of VIO.Firstly,it used the epipolar geometry and preintegration algorithms to obtain the relative poses for both the camera and the IMU.Then,it evaluated cross-correlation based on the angular velocities to obtain an initial estimate of the time offset.Finally,it utilized the relative pose between the camera and IMU to impose rotation con-straints and optimize an error function,leading to a more accurate estimation of the time offset.It employed this time offset va-lue to calibrate the sensors.Experimental results show that the proposed algorithm effectively mitigates the impact of time offset on the accuracy of odometry trajectories,enabling the system to reliably operate when significant time offsets exist in the data streams.
万方数据
维普
