Accurate state estimation is a fundamental module for various intelligent
applications, such as robot navigation, autonomous driving, virtual and
augmented reality. Visual and inertial fusion is a popular technology for 6-DOF
state estimation in recent years. Time instants at which different sensors'
measurements are recorded are of crucial importance to the system's robustness
and accuracy. In practice, timestamps of each sensor typically suffer from
triggering and transmission delays, leading to temporal misalignment (time
offsets) among different sensors. Such temporal offset dramatically influences
the performance of sensor fusion. To this end, we propose an online approach
for calibrating temporal offset between visual and inertial measurements. Our
approach achieves temporal offset calibration by jointly optimizing time
offset, camera and IMU states, as well as feature locations in a SLAM system.
Furthermore, the approach is a general model, which can be easily employed in
several feature-based optimization frameworks. Simulation and experimental
results demonstrate the high accuracy of our calibration approach even compared
with other state-of-art offline tools. The VIO comparison against other methods
proves that the online temporal calibration significantly benefits
visual-inertial systems. The source code of temporal calibration is integrated
into our public project, VINS-Mono.