Online Time Calibration method of Monocular Visual-Inertial Odometry based on Adaptive Kalman Filter

Visual-Inertial Odometry (VIO) is a new technology to determine position and attitude of the object which it is mounted on. Because of its small size and powerful performance, VIO system is widely used in virtual and augmented reality, autonomous driving and robot navigation. Usually, we assume the timestamps of the Inertial Measurement Unit (IMU) and the camera is synchronized, and there is no temporal misalignment (time offset) in VIO system. However, in practice, the different triggering time and propagation delays cause the sensors to be temporal misaligned. If we calibrate the temporal misalignment, the performance of VIO system can be greatly improved. To this end, we propose a novel method to calibrate the time delay between visual and inertial devices. This method accurately estimates the IMU state, time offset and camera pose in the VIO system by using Adaptive Kalman Filter. Simulation experiments show that our method can quickly and accurately estimate the time delay. The results compared against other methods proves that the proposed approach can significantly boost the performance of VIO system.