High-Precision Multicamera-Assisted Camera-IMU Calibration: Theory and Method

Abstract

This paper presents a high precision single-camera IMU extrinsic calibration method by tightly fusing the visual information from other cameras. Specifically, multiple additional cameras are added to the monocular camera-IMU system for assisting calibration as we theoretically prove that more cameras used in calibration can lead to smaller lower bound on the covariance of the estimated extrinsic parameters, which then results in better calibration accuracy. Moreover, we provide two degenerative motion conditions in the resulting multi-camera visual-inertial system, which impair the calibration accuracy and should be avoided in real application whenever possible. More importantly, we present the requirement of minimum motion for a reliable extrinsic calibration to provide the practical guideline. Finally, the full validation on both simulation and real world data are demonstrated. By evaluating the Cramér-rao Lower Bound on the covariance, the proposed camera-IMU calibration method is shown to be statistically efficient for accurate calibration with errors less than 0.01m in translation and 0.5° in rotation, which is consistent with the theoretical analysis in paper.