Trust Your IMU: Consequences of Ignoring the IMU Drift
This work addresses camera-IMU calibration for UAV navigation, offering a faster and more convenient method by eliminating tedious calibration processes, though it is incremental as it builds on existing pre-integration techniques.
The authors tackled the problem of camera-IMU calibration by assuming IMU drift is negligible for short intervals, enabling a simplified camera model and joint estimation of relative pose, focal length, and radial distortion. They demonstrated significant speed-ups with minimal accuracy loss on synthetic and real UAV data.
In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us to consider a simplified camera model, which in turn admits further intrinsic calibration. We develop the first-ever solver to jointly solve the relative pose problem with unknown and equal focal length and radial distortion profile while utilizing the IMU data. Furthermore, we show significant speed-up compared to state-of-the-art algorithms, with small or negligible loss in accuracy for partially calibrated setups. The proposed algorithms are tested on both synthetic and real data, where the latter is focused on navigation using unmanned aerial vehicles (UAVs). We evaluate the proposed solvers on different commercially available low-cost UAVs, and demonstrate that the novel assumption on IMU drift is feasible in real-life applications. The extended intrinsic auto-calibration enables us to use distorted input images, making tedious calibration processes obsolete, compared to current state-of-the-art methods.