Abstract
Due to the sensors’ latency, unsynchronized clocks, triggering, and data jams, there usually exists an inevitable time offset between image streams and inertial measurement unit (IMU) data sequences in visual-inertial systems. Such a time offset has a significant effect on the robustness and accuracy of the system. In this article, an online temporal calibration algorithm is proposed based on a modified projection model. In particular, we introduce the time offset as an estimated state variable in an extended Kalman filter (EKF)-based visual-inertial odometry (VIO). In addition, in order to align camera and IMU measurements in the time domain, the projection model is sophisticatedly given, and we propose a systematic method to obtain features’ virtual observations with respect to the time offset. Our approach is general and can be easily applied to different EKF-based VIO frameworks. Experimental results demonstrate that the time offset is calibrated quickly and accurately even compared with other existing offline tools, and the proposed online temporal calibration method significantly improves the performance of VIO.
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