Abstract
This paper is concerned with the problem of estimating the relative orientation between an inertial measurement unit (IMU) and a camera. Unlike most existing IMU-camera calibrations, the main challenge in this paper is that the information output from the IMU is incomplete. For example, only two tilt information can be read from the gravity sensor of a smart phone. Despite incomplete inertial information, there are strong restrictions between the IMU and camera coordinate systems. This paper addresses the incomplete information based IMU-camera calibration problem by exploiting the intrinsic restrictions among the coordinate transformations. First, the IMU transformation between two poses is formulated with the unknown IMU information. Then the defective IMU information is restored using the complementary visual information. Finally, the Levenberg-Marquardt (LM) algorithm is applied to estimate the optimal calibration result in noisy environments. Experiments on both synthetic and real data show the validity and robustness of our algorithm.
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