Trust Your IMU: Consequences of Ignoring the IMU Drift

TL;DR

This paper demonstrates that IMU drift can be ignored over short intervals, enabling simplified camera calibration and faster algorithms with minimal accuracy loss, validated on UAV navigation data.

Contribution

It introduces a novel solver that jointly estimates relative pose, focal length, and radial distortion using IMU data, with significant speed improvements and auto-calibration capabilities.

Findings

IMU drift can be ignored for short time intervals in practice.

The proposed solver achieves faster computation with comparable accuracy.

Auto-calibration of intrinsic parameters is feasible with distorted images.

Abstract

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.